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In order to contrast temperature patterns in the tropics with those in the middle latitudes, allow me to briefly employ an analogy. It might sound a little bizarre, but I'm going to liken temperatures in the tropics and middle latitudes to human personality types. One theory of human personality defines two types -- Type A and Type B. In a nutshell, people with a "Type A personality are "high-strung," obsessed with details and organization, and somewhat rigid. "Type B" personalities on the other hand, are more laid back, "go-with-the-flow" types. They're less stressed out about organization and details.

If I could label the middle latitudes with a human personality, I would probably rate them "Type A". Recall that the middle latitudes mark the region where advancing warm and cold air masses invariably collide. Like a typical "Type A" personality, the middle latitudes seem to be obsessed with organization, dutifully structuring the lower troposphere into narrow zones of relatively large temperature gradients (cold, warm, and stationary fronts). The middle latitudes are constantly trying to manage their temperature gradients in an attempt to be as "organized" as possible.

In contrast, the tropics have a "Type B" personality. As a general rule, horizontal temperature gradients are weak and much more "laid back". To understand why, let's start with the short background video (2:21) below. In case you're wondering, the values of absorbed solar and emitted infrared radiation plotted in the video represent latitudinal (sometimes called "zonal") averages.

We can confirm the great emission of infrared radiation from the tropics discussed in the video by viewing plots of outgoing long-wave radiation (OLR). For the record, OLR is most intense where surface temperatures are the greatest, such as hot subtropical deserts (the Sahara, for example) during summer. In contrast, OLR is the least intense where it's colder, either because the ground is cold or because deep convection is present. That's because cloud tops in areas of deep convection are high and cold and thus weakly emit long-wave (infrared) radiation.

If we look at the long-term average of OLR across the globe, we can see the general pattern described in the video. The blazing hot Sahara Desert in northern Africa is clearly an area of high OLR values (some of the highest on Earth, denoted by dark purples), while other tropical areas frequently characterized by deep convection (like the Amazon River Basin in northern South America) have lower values. OLR charts have lots of other practical applications for studying trends in cloudiness and rainfall over the tropics (if you're interested in checking out the variety of OLR products available, check out the Earth System Research Laboratory page of OLR plots).

The relatively large losses of infrared energy to space over the tropics only partially offset major-league solar heating, resulting in a broad surplus of energy (shaded in red in the interactive graph above) that varies little with latitude between 30 degrees north and south. This relatively even distribution of surplus energy across the tropics accounts, in part, for the general lack of moderate to strong horizontal temperature gradients in the tropical troposphere.

One other reason for the generally weak temperature gradients at low latitudes is that the water covers approximately 75% of the tropics. That means that the uniform surplus of energy in the tropics gets distributed over large expanses of water, thus further limiting opportunities for strong temperature gradients to form (cold air traveling over relatively warm ocean waters gets rapidly modified).

The figure above represents the long-term average of annual surface air temperatures across the globe. I point out that there are indeed temperature gradients between tropical land masses and surrounding oceans, but the overall pattern of temperature gradients in the tropics is weak compared to those at higher latitudes.

Now I readily admit that any annual average in temperature tends to "wash out" strong signals of gradients in winter, so perhaps a look at temperatures for a single day would more effectively drive home my point. Check out daily global surface temperatures for January 23, 2013, when sharp temperature gradients existed over eastern North America, for example, on the fringe of a continental Arctic air mass. Now, compare them to the flabby gradients over the tropics. No contest, wouldn't you agree? Notice that there are some sharper gradients along the outer fringes of the tropics near 30 degrees north. These larger gradients near 30 degrees are not unusual, given that Arctic air masses drive farther south in winter (occasionally into the fringes of the tropics). In the heart of the tropics, however, gradients are weak by almost any standard.

The lack of large temperature gradients does not stop at the surface, of course. At 500 mb, for example, the lack of strong temperature gradients over the tropics is striking compared to the middle latitudes (check out the annual mean 500-mb temperatures across the globe). So, with regard to temperature gradients, the tropical troposphere has a completely different personality than the middle latitudes.

I hope the analogy to personality types helps you to understand the different nature of temperature patterns in the tropics and middle latitudes, but now it's time to get a bit more formal. How do we formally describe these different "personalities" of the middle latitudes and the tropics? Meteorologists formally refer to the "Type A" middle latitudes as baroclinic and the "Type B" tropics as barotropic. In the broadest terms, a baroclinic atmosphere is one where horizontal temperature gradients prevail. The middle latitudes, for example, are highly baroclinic during winter, when large horizontal temperature gradients often set the stage for strong temperature advection. A barotropic atmosphere, on the other hand, is one in which temperature advection is pathetically weak. In the presence of wind, that means that horizontal temperature gradients must all but vanish. For all practical purposes, the tropics are bereft of horizontal temperature gradients, so "barotropic" best describes the tropical atmosphere.

Recall from the video discussing absorbed solar and emitted infrared radiation versus latitude that, while the tropics run a surplus in energy, the middle and polar latitudes run a deficit. Thus, to balance the ledger of the earth-atmosphere system, it is pretty obvious that there must be a transfer of heat energy poleward from the tropics. This transfer is accomplished by the meridional transport of heat energy by the atmosphere and the oceans. You may already be familiar with some mechanisms for this transport, such as the Gulf Stream (an ocean current that conveys heat energy northward from low latitudes).

As far as atmospheric transport of heat energy goes, there are several mechanisms working to export heat energy out of the tropics, which we'll explore in later lessons. For now, though, recall that large mid-latitude cyclones are very effective at transporting warm air northward and cold air southward with their broad circulations. Given the large north-south temperature gradients that prevail in the middle latitudes during the cold season, the large impacts on regional temperatures from strong advection qualify mid-latitude cyclones as "big business" in the world of heat transport. Is the same true for tropical cyclones? Not really. Tropical cyclones transport some heat energy and moisture from the tropics to higher latitudes, but their overall contribution pales in comparison to other transport mechanisms. If you're interested, check out the "Explore Further" section below for more on this topic and another peculiarity that arises from the barotropic nature of the tropics. Otherwise, get ready to explore another aspect of the "Type B" behavior of the tropics on the next page.

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Just as the tropics display a "Type B" personality with respect to temperatures, they generally maintain that same personality when it comes to pressure. In the tropics, pressure gradients tend to be much more relaxed than they do in the "Type-A" middle latitudes (resuming our analogy from the previous page). If you look at the surface analysis over the northern Atlantic Basin at 06Z on September 8, 2003 below, you should note that the middle latitudes (toward the top of the image) have much tighter pressure gradients than low latitudes. The exception to the rule is tropical cyclones, of course. The tightly-packed isobars around Hurricane Isabel indicate a strong pressure gradient, as do those around Hurricane Fabian (which was on the doorstep of the middle latitudes).

Text description of the September 8, 2003 06Z image.

The 06Z surface analysis over the North Atlantic Basin from 06Z on September 8, 2003 shows generally weak pressure gradients over the tropics, except for Hurricane Isabel

The image is a detailed weather map, displaying the North Atlantic region, including parts of North America, Europe, and Africa. It features numerous isobars, which are black contour lines representing areas of equal atmospheric pressure, and weather fronts, indicated with varying line styles and symbols. High and low-pressure systems are marked by "H" and "L" respectively, scattered throughout the map. Distinctive systems include several hurricanes represented by circular isobars with progressively lower pressure toward the center, notably Hurricane Fabian and Hurricane Isabel. The map is covered with meteorological symbols and numerical codes that indicate specific conditions like wind speed and direction. Numerous regions with varying air pressures are marked, with discernible shapes and troughs. The map also contains textual data enclosed in boxes related to storm tracking, such as coordinates, dates, and maximum wind speeds.

Credit: NCEP

So it is pretty apparent that the relatively small (large) temperature gradients over the tropics (middle latitudes) go hand in hand with relatively small (large) pressure gradients. At this point, the only flies in the ointment seem to be tropical cyclones. Indeed, tropical cyclones do something that is unheard of in the middle latitudes: they form in an environment bereft of large temperature gradients yet somehow develop very large pressure gradients around their centers.

The overwhelming message, however, is that pressure gradients are typically weak across the tropics. That also means that changes in surface pressure with time at any given location are usually puny compared to the larger increases and decreases that regularly accompany the approach and passage of mid-latitude high- and low-pressure systems. In the equable tropics, pressure patterns can persist for very long periods (weeks and even months). Yet, almost mysteriously, there is a regular daily rhythm of changes in surface pressure that meteorologists detect in the tropics. If you're intrigued, check out the "Explore Further" section at the end of the page.

For a broader view of pressures across the tropics and how they compare to those in the middle latitudes, check out the chart of average sea-level pressures at 00Z on February 12, 1998 below. At the time, there were intense northern hemispheric low-pressure systems over the Gulf of Alaska, the Great Lakes, the middle Atlantic Ocean, northern Russia and the east coast of Asia (splotches of blues and purples on the map). Meanwhile, robust high-pressure systems (bigger blobs of greens, yellows, oranges and reds) were interspersed between the intense lows.

In the tropics, on the other hand, pressure patterns are much more relaxed and much more equable than the middle latitudes. In other words, prominent centers of high and low barometric pressure are more difficult to find, especially equator-ward of latitudes 30 degrees north and south, which mark the very outer fringes of the tropics. The most notable exception to the generally more relaxed pattern of pressure in the tropics on February 12, 1998, was a tropical cyclone, not surprisingly. The spot of relatively low pressure (blue splotch) just to the east of Madagascar in the southwest Indian Ocean is the signature of Tropical Cyclone Ancelle, which reigned over the southwest Indian Ocean from February 5 to February 13, 1998 (during the southern hemisphere's summer).

The height patterns on constant pressure surfaces over the tropics are similarly relaxed. Consistent with the general lack of temperature gradients at 500 mb over the tropics, note the absence of strong gradients between 30 degrees latitude (north and south) on this chart of long-term mean 500-mb heights. Height contours on the other mandatory pressure levels in the tropical troposphere show a similarly relaxed pattern.

Since the pressure gradient force is a primary driver of wind speed, you might think that the winds are almost always weak in the tropics (outside of tropical cyclones, that is), with the weak pressure gradients at the surface and aloft. But, that's far from the truth! To help you visualize the fact that many places in the tropics are quite breezy, despite weak surface pressure gradients, I'm going to introduce a new type of plot -- the wind rose. Wind roses display the observed frequency of wind directions (and sometimes speeds) at a particular location. On the left below is a histogram displaying frequencies of observed wind speeds (in meters per second) at an ocean buoy moored at 8 degrees South, 95 degrees West during the year 2002. On the right is the corresponding wind rose for the buoy, which shows the frequency of observed wind directions during the same year.

From these two images, we can quickly get two important messages. First, wind speeds at the buoy were between five and nine meters per second (roughly 10 to 20 mph) the vast majority of the time, which hardly constitutes "weak" winds. Second, the direction from which the wind blew during the year was remarkably consistent. To get your bearings with the wind rose, note that each concentric ring represents a ten-percentage point increase in the relative frequency of the observed wind direction. Thus, the daily mean wind direction of 130 degrees (from the southeast) occurred on nearly 45% of the days, and the daily mean wind direction of 140 degrees occurred on about 28% of the days! The wind rose clearly demonstrates that winds retained their overall southeasterly direction for almost the entire year (and didn't deviate much from 130 degrees). Small variations in wind direction and breezy conditions are fairly typical in tropical locations because of the famous belt of "trade winds," which we'll cover formally in a later lesson.

Many wind roses that you'll encounter also include wind-speed data right on the wind rose plot. For example, check out this wind rose plot for the month of March at Grand Rapids, Michigan. At first glance, it's easy to see that it looks much different than the wind rose from the tropical ocean buoy shown above. Wind directions are much more variable during the month, which is more common in the middle latitudes thanks to the parade of high- and low-pressure systems that march around the globe. On this wind rose, each concentric ring represents a two-percent increase in the relative frequency of the observed wind direction, so the most common wind direction at Grand Rapids during March (from 90 degrees -- due east) occurs a little less than 10% of the time.

The various colors along each "spoke" represent wind speed ranges according to the color key at the bottom of the image. So, along the 90-degree "spoke," winds between 1.80 meters per second and 3.34 meters per second (roughly 3.5 - 6.5 knots) marked by the yellow shaded area occurred approximately 2% of the time. Winds between 3.34 meters per second and 5.40 meters per second (roughly 6.5 knots - 10.5 knots) marked by the red shaded area occurred about 3.5% of the time (5.5% - 2%). Winds between 5.40 meters per second and 8.49 meters per second (roughly 10.5 - 16.5 knots) marked by the blue shaded area occurred about 3.5% of the time (9% - 3.5% - 2%), and so on. Along any given spoke, the individual percentages for each range of wind speeds should sum to the total percentage associated with the entire spoke.

I strongly recommend taking some time to practice extracting information from wind roses (you can start with the "Key Skill" section below). Wind roses can provide lots of practical information. For example, consulting meteorologists use wind roses when the work on the design of airports (runways should be built to avoid strong crosswinds), and skilled forecasters regularly use wind roses when studying the climatology of a particular location. After you're comfortable with interpreting wind roses (and check out the "Explore Further" section, if you wish), you'll be ready to examine another difference between the tropics and the middle latitudes -- the structure of mid-latitude cyclones versus the structure of tropical cyclones.

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Up to this point, you've seen the generic phrase "tropical cyclone" used to describe the low-pressure systems that form over warm tropical seas. However, as you're about to find out, naming and classifying tropical cyclones is somewhat complicated. Before we get into how tropical cyclones are named, let's look at the areas where tropical cyclones tend to form. Do they form just anywhere in the tropics? Not really. As you can see from the image below, the breeding grounds and regions where tropical cyclones typically track can be boiled down to seven areas:

1. Atlantic Basin (the northern Atlantic Ocean, the Gulf of Mexico, and the Caribbean Sea)
2. Northeast Pacific Basin (from Mexico to the International Dateline)
3. Northwest Pacific Basin (from the International Dateline to Asia, including the South China Sea)
4. North Indian Basin (includes the Bay of Bengal and the Arabian Sea)
5. Southwest Indian Basin (from Africa to about 100 degrees east longitude)
6. Southeast Indian/Australian Basin (100 degrees east longitude to 142 degrees east longitude)
7. Australian/Southwest Pacific Basin (142 degrees longitude to about 120 degrees west longitude

Of these seven areas, the Northwest Pacific and Northeast Pacific basins tend to be the busiest, as this frequency plot for tropical cyclones suggests. It shows the average number of occurrences (from 1972 to 2001) that the center of a tropical cyclone occupied an area with dimensions of one degree latitude by one degree longitude (based on each storm's best track). The dark red color indicates the highest average of such occurrences and thus marks the core of these breeding basins for tropical cyclones. Meanwhile, some areas in the tropics are nearly entirely free of tropical cyclones. While tropical cyclones can form outside of the seven areas listed above, it happens relatively infrequently. For example, the southern Atlantic Ocean (south of the equator) is rarely home to tropical cyclones.

With tropical cyclones in any basin possibly impacting multiple countries, how do forecasters keep tabs on all of them? The World Meteorology Organization created a branch called the Tropical Cyclone Programme (TCP) to ensure that all countries bordering and within each basin are adequately prepared for the threat posed by tropical cyclones. To accomplish this goal, the TCP's primary responsibility is to establish a nationally and regionally coordinated network of forecasting centers. To define areas of responsibility, they partitioned the tropical-cyclone basins and assigned Regional Climate Centers (RCCs), which issue the official forecasts and advisories for their respective basins (see figure below). If you wish, you can check out the WMO Web Page that lists the links to the Tropical Cyclone RCCs where you can access all the current RCC advisories.

Although each RCC is responsible for issuing the official forecasts and advisories for their jurisdiction, there can be multiple cities which issue warnings for various countries under the umbrella of a single RCC. For example, the official advisories and forecasts for the Atlantic Basin come from: the National Hurricane Center in Miami, but the Canadian Hurricane Centre in Dartmouth, Nova Scotia issues warnings when tropical cyclones threaten Canada, using the National Hurricane Center's products as their basis. Keep in mind that Atlantic hurricanes and tropical storms moving northward along the East Coast can pose a significant threat to the Canadian Maritimes (the provinces of New Foundland, Nova Scotia, New Brunswick, and Prince Edward Island).

In addition to the Regional Specialized Meteorological Centers and Tropical Cyclone Warning Centers on the map above, the Joint Typhoon Warning Center (JTWC) is an additional warning center and serves as a joint effort between the United States Navy and Air Force. JTWC was founded in 1959 in Guam, but has since moved to Pearl Harbor, Hawaii. While JTWC does not issue official public forecasts, they keep tabs on tropical cyclones globally for U.S. Department of Defense interests.

Now that we know who's keeping track of tropical cyclones around the globe, we can delve into how they keep track of them. That's where things can get a bit complicated because standards differ across the globe. For starters, forecasters often have their eyes on clusters of showers and thunderstorms across the tropics (often called "tropical disturbances"). Tropical disturbances do not have closed circulations and are not formally tropical cyclones; however, by convention in the U.S., tropical disturbances that have the potential to develop into tropical cyclones are dubbed "invests." Each invest is tagged with a number from 90-99 along with a capital letter, which corresponds to the tropical basin where it's located (see the table below for the letters that correspond to each basin). Forecasters start with the number 90 and sequentially progress to 99, and then start over again at 90. So, for example, Invest 99L in the Atlantic Basin would be followed by Invest 90L, and so on.

If the tropical disturbance with organized convection develops a closed cyclonic circulation in its surface wind field, it becomes a tropical depression as long as its maximum sustained wind speeds are less than 34 knots (39 miles per hour). Note that different definitions of "sustained" (as described in the link) can lead to different storm classifications in different basins. Tropical depressions are formally considered tropical cyclones, and at this point The Joint Typhoon Warning Center, Central Pacific Hurricane Center, and the National Hurricane Center routinely assign a new number to go along with the letter referring to the basin of origin. For each season in each basin, the digits start at "01" and then increase by one for each successive depression that forms. Since the capital letter refers to the basin of origin, the letters are not changed if tropical cyclones cross 140 or 180 degrees longitude. I should note, however, that depressions in the Atlantic Basin are an exception. When a depression is classified in the Atlantic, the National Hurricane Center simply refers to it by its number ("One", "Two", etc.) and the letter "L" gets dropped from the designation.

Once a tropical cyclone reaches sustained wind speeds of at least 34 knots (39 miles per hour), it becomes a tropical storm and receives a name (more on the naming of tropical cyclones in a bit). Tropical cyclones retain their tropical storm status as long as their maximum sustained winds remain between 34 knots and 63 knots. Once a tropical cyclone reaches maximum sustained winds of at least 64 knots (74 miles per hour), it loses its "tropical storm" label, and earns the classification of hurricane, typhoon, severe cyclonic storm, tropical cyclone or severe tropical cyclone, depending on the basin in which the storm is located (see the table below for what words are used in each basin. At times, I may generically refer to "hurricanes" in the text, but keep in mind that such references also include strong tropical cyclones that go by various labels in basins around the world.

Of course, all "strong" tropical cyclones (hurricanes, typhoons, etc.) are not created equal. Some are much more intense than others. In the Atlantic and Northeast Pacific basins, forecasters use the Saffir-Simpson Hurricane Wind Scale to further classify a given hurricane. Hurricanes classified as "Cat 3", "Cat 4" or "Cat 5" (all hurricanes with maximum sustained winds of at least 96 knots, or 111 mph) qualify as major hurricanes. Although major hurricanes make-up only 21% of the hurricanes that hit the United States, these fierce storms account for over 83% of all the damage from landfalling hurricanes. For the record, Australian forecasters, rank tropical cyclones a bit differently.

Other basins also have different descriptors for extremely intense tropical cyclones. In the Northwest Pacific Basin, for example, the particularly descriptive classification of "super typhoon" is used once a typhoon's maximum sustained wind speed reaches at least 130 knots (more than twice the minimum typhoon wind speed). For some interesting tidbits on some memorable super typhoons, check out the Explore Further section below. In the North Indian Ocean, meanwhile, severe tropical cyclones that attain maximum sustained winds of at least 130 knots graduate to super cyclonic storm.

The Name Game

There's a checkerboard history behind the naming of tropical cyclones in the various basins around the world. If you're interested in the history of naming tropical cyclones, I encourage you to check out the corresponding section within Explore Further below. The reason why tropical cyclones get named, however, is pretty straightforward. The practice of naming tropical cyclones ensures clear, unambiguous communication between forecasters and the general public when forecasts, watches, and warnings are issued. At any given time across the globe (or even within a single tropical basin) there can be multiple tropical cyclones present at any one time. For example, the satellite image from September 2, 2008 (below) shows a whopping four named storms present in the Atlantic Basin!

Without the practice of naming tropical storms, deciphering forecasts with four active storms in the basin could have been a real mess -- sifting through coordinates or other technical descriptions of a storm's location. In the end, using names is much simpler for the general public, so let's get to the (not so simple) business of how storms are named. In the Atlantic and eastern Pacific, the World Meteorological Organization and National Weather Service (NWS) have used lists of alternating male and female names in alphabetical order to christen storms since 1979 (check out the lists currently in use if you wish).

I should point out that any year that the alphabetical list of male and female names is not long enough to accommodate all the named storms in a season, the National Hurricane Center turns to a supplemental list of names; however, prior to 2021, the standard was to use letters of the Greek Alphabet (Alpha, Beta, Gamma, Delta, etc.) to name storms once the original list of names had been exhausted. Use of the Greek Alphabet to name storms only occurred twice (2005 and 2020).

For Central Pacific storms, the Central Pacific Hurricane Center uses its own list of names of Hawaiian origin. Because not many tropical cyclones form in the Central Pacific, they don't restart the list from the beginning each year, and instead they just keep using the same list until all the names have been used. When they reach the end of one list, they simply begin with the first name on the next list.

Meanwhile, in the northwest Pacific Basin, since the year 2000, the World Meteorological Organization has used names which are, for the most part, not male or female names. Instead, most names on the list refer to flowers, animals, birds, trees, or even foods, etc. Others are simply descriptive adjectives. Each name on the list is contributed by a participating nation within the basin. The names are not used in alphabetical order like in the Atlantic and eastern Pacific, however. Instead, the contributing nations are listed in alphabetical order and this ranking determines the order that the names are assigned.

It's important to note, however, that the established lists from the World Meteorological Organization are not universally used for storms in the northwest Pacific. The Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) assigns Filipino words as storm names when storms threaten the Philippines so that locals can easily remember them and communicate about the storm. For example, in November 2013 Super Typhoon Haiyan made landfall in the Philippines as one of the strongest tropical cyclones on record at the time of landfall. But, in the Philippines, Haiyan (which is from the Chinese for "petrel" -- a type of seabird) was known as "Yolanda." So, be aware that you may come across two names for some storms in the northwest Pacific Basin.

Finally, in the North Indian Ocean, tropical cyclones weren't named from a traditional list until 2004. Prior to that year, conventions for identifying storms and keeping historical records were somewhat awkward (more in Explore Further). With regard to name selection, eight countries belonging to the WMO Tropical Cyclone panel for the North Indian basin contributed eight names each, which were tabulated into eight columns. In each column, one name from each country appeared, with the names listed in the order determined by the alphabetized contributing nations (the same convention as in the Northwest Pacific basin). You can see the lists of names for tropical cyclones in the North Indian Ocean and all other tropical basins (including basins not covered in-depth here) on the World Meteorological Organization's page of tropical cyclone names.

An average of approximately 80 strong tropical cyclones form each year worldwide -- a small number compared to the hundreds and hundreds of cyclones that parade across the middle and high latitudes each year. Yet, the attention that meteorologists focus on tropical cyclones sometimes seems disproportionately great. That's because strong tropical cyclones can cause staggering losses of life and property. Next up, we'll compare tropical cyclones with mid-latitude cyclones. Not surprisingly, there's a world of difference between them!

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With the great potential for loss of life and property posed by tropical cyclones, they certainly garner great attention from weather forecasters and the public at large. But, why do powerful tropical cyclones more frequently steal national and international headlines, while mid-latitude cyclones rarely do? The first reason is likely that mid-latitude cyclones are more numerous. Hundreds of them trek across the globe each year. Meanwhile, only about 80 tropical cyclones develop each year.

Secondly, a tropical cyclone can attain a much greater intensity in terms of both sea-level pressure and wind speed (some even call hurricanes the "kings" of all low-pressure systems). For example, the most intense tropical cyclones can have sea-level pressures below 900 mb. Typhoon Tip (1979) had the all-time lowest at 870 mb, but other storms such as Hurricane Wilma (2005) and Super Typhoon Haiyan (2013) have had central pressures below 900 mb. On the other hand, the sea-level pressure at the center of a mid-latitude cyclone rarely drops below 950 mb. For example, the Superstorm of 1993 (aka the "Storm of the Century"), had a central pressure of 963 mb at its peak.

With these observations in mind, a natural question might be, "Why do strong tropical cyclones often attain sea-level pressures that are notably lower than those associated with mid-latitude cyclones?" While both types of cyclones are low-pressure systems, the answer to that question can found by examining the differences in structure and strengthening mechanisms characteristic of each type of low-pressure system.

For starters, recall that mid-latitude cyclones undergo the process of self-development. This process requires the cyclone to develop in a region of strong horizontal temperature gradients (a baroclinic zone, or front) and under a region of strong upper-level divergence. In short, divergence downwind of a 500-mb shortwave trough reduces the weight of air columns, forming an area of low pressure at the surface, around which winds rotate counterclockwise (in the Northern Hemisphere). Decreasing surface pressures result in a stronger pressure gradient force, which causes faster winds. As winds around the cyclone increase, cold-air advection southwest of the low increases, causing 500-mb heights to fall and the 500-mb trough and vorticity maximum to strengthen. In turn, upper-air divergence increases over the center of the low, causing surface pressures to further decrease and surface winds to increase further. This positive feedback loop continues uninterrupted until the late stages of occlusion, when the low moves back into the cold air (away from the baroclinic zone) and upper-level divergence over the low weakens (the low starts to "fill" -- surface pressure rises).

In a nutshell, the magnitude of the divergence aloft (which is greater than the magnitude of the convergence at lower altitudes) drives the intensity of the mid-latitude cyclone. Also note, however, that the divergence aloft along with low-level convergence drives upward motion over the center of the low. You'll occasionally read or hear explanations that suggest that rising causes lower surface pressures, but that's just not true. In fact, just the opposite is true. Rising air actually works against the overall reduction in surface pressure.

Recall that rising air cools via expansion, and once clouds and precipitation develop, can also yield evaporational cooling (assuming the atmosphere is not already at saturation). In turn, cooling by forced ascent increases the mean density in the column of air that extends from the ground to the tropopause (low-level convergence and upper-level divergence are still at work). Assuming a nearly hydrostatic atmosphere (in which the force of gravity is balanced by the upward pressure gradient force), this increase in mean column density serves to add column weight. During the development stage of a mid-latitude cyclone, dominant weight-loss processes, such as net column divergence and warm advection near 200 mb overwhelmingly offset the tendency for air columns to gain weight from adiabatic and moist adiabatic cooling. But my point should now be clear: Rising air tends to make surface pressures higher, not lower. In other words, rising air actually works against the deepening of a mid-latitude cyclone; it serves as a "check and balance" on the overall intensity of the system.

Strong tropical cyclones, on the other hand, don't have this "check and balance" over their centers. Indeed, the predominant vertical motion over the center of a hurricane is downward. It's that downward motion that creates the eye of the storm, as shown in the visible satellite image of Hurricane Isabel from 1404Z on September 11, 2003 (below).

For the record, the eye is a roughly circular, fair-weather zone at the center of a hurricane. By "fair weather", I mean that little or no precipitation occurs in the eye and an observer looking upward in the eye can often see some blue sky or stars. The diameter of the typical eye ranges from approximately 30 to 60 kilometers (about 16 to 32 nautical miles across), but eye diameters as small as four kilometers (approximately two nautical miles) and as large as 200 kilometers (approximately 110 nautical miles) have been observed. For the record, Hurricane Wilma's "pinhole eye" was the smallest recollected by forecasters at the National Hurricane Center (two nautical miles) as the storm deepened to 882 mb (the lowest on record in the Atlantic Basin).

The "fair weather" in the eye can largely be attributed to the sinking air over the center of the storm. The downward motion in the eye is only on the order of a few centimeters per second, which suggests that the central core of strong tropical cyclones is approximately hydrostatic. Given that the compressional warming in the eye decreases the mean density of the central column of air in the eye (and thus its weight), we can deduce that subsidence contributes to the low central pressures observed in hurricanes. Of course, as the sinking air warms, relative humidity decreases within the sinking parcels, which promotes the clearing observed within the eye.

I should point out however, that the air does not uniformly sink within the eye of a hurricane. Dropwindsonde observations taken from the eye of a hurricane often reveal an inversion at an altitude of about one to three kilometers. To see what I mean, check out the representative temperature and dew-point soundings retrieved from dropwindsonde measurements in the eye of Hurricane Jimena (1991). The subsidence inversion near 850 mb is the telltale sign of downward motion in the eye of a hurricane, but the presence of this inversion means that air does not sink all the way to the ocean surface. The fact that air does not sink all the way to the surface explains why low clouds frequently exist in the eyes of hurricanes (although skies may not be completely overcast).

Regardless of the fact that air does not uniformly sink throughout the entire eye, the compressional warming associated with the subsidence in the eye is one contributor to the "warm core" of a hurricane. Meanwhile, deep, moist convection outside of the eye (in the eyewall--the partial or complete ring of powerful thunderstorms around the eye, and spiral bands--relatively long and thin bands of convective rains) also contributes to the warm core.

The image above gives you a "bird's-eye" view of the basic structure of a hurricane on radar. Note the spiral bands (yellow, orange, and red shadings) curving in toward the center of the storm, and the eyewall almost completely encircling the much lower reflectivity values (dark green and blue) in the eye. How does the deep, moist convection in the eyewall and spiral bands contribute to the warm core of the storm? Simply put, the air parcels rising in thunderstorm updrafts are initially very warm and moist (due to evaporation from warm tropical seas). As these parcels rise in thunderstorm updrafts, huge amounts of latent heat of condensation are released. Yes, air parcels cool as they rise, but the release of latent heat keeps them warmer than they otherwise would be, which keeps the air within a hurricane warmer than air at the same altitudes outside of the influence of the hurricane. Weaker tropical cyclones are also warm core systems because of the release of abundant latent heat (even though weaker systems don't have eyes--there's no organized compressional warming in the center of the storm).

All in all, within a strong tropical cyclone, the warm core generated by latent heat release and compressional warming can be quite substantial. For example, check out the cross-section of satellite-detected temperature anomalies from Super Typhoon Haiyan at 1726Z on November 7, 2013 (below).

The core of the warm anomaly approximately coincides with the eye of Haiyan, and at its peak in the middle and upper troposphere, temperatures were as much as 7 degrees Celsius greater than the environment surrounding the storm. Outside of the eye, the warm anomaly is weaker, but still spans hundreds of miles across the storm. Given the maximized warm core near the center of the storm, it becomes clear that hurricanes create large horizontal temperature gradients internally (especially at the interface of the eye and eyewall) during their development, even though they initially form in the weak horizontal temperature gradients that characterize the tropics. As you've learned, mid-latitude cyclones are just the opposite: They form in areas with large horizontal temperature gradients, and their circulations ultimately act to reduce horizontal temperature gradients over time.

Sustaining Tropical Cyclones

Now that we've established a key difference between tropical cyclones (which have a warm core) and mid-latitude cyclones (which do not, since they are characterized by rising motion over their centers and typically lack deep, moist convection near their cores), let's turn our attention to another key factor in the intensification of both mid-latitude and tropical cyclones--divergence aloft. You're already familiar with the role of divergence aloft in mid-latitude cyclones, supplied primarily by 500-mb shortwave troughs and 300-mb jet streaks, but divergence aloft plays an important role in tropical cyclones, too.

In order to help you visualize divergence aloft in tropical cyclones, allow me to introduce the secondary circulation of a tropical cyclone. As the name implies, tropical cyclones have two distinct circulations. The primary circulation, as you might expect, refers to rotation of air around the center of the storm. But, there's another circulation going on at the same time. In a basic sense, low-level air flows in toward the center of the storm, rises in thunderstorms within the eyewall and spiral bands, and flows (mostly) outward aloft, sinking around the periphery of the storm. This general circulation (in at the bottom of the storm, up, out at the top, and down around the storm's periphery) is the secondary circulation. To visualize this "in, up, and, out" process in the context of a strengthening hurricane, check out the slideshow animation below.

The divergence aloft in a healthy tropical cyclone acts to further reduce surface pressure by removing mass from air columns near the center of the storm. Ultimately, hurricanes intensify as a result of a positive feedback loop, albeit a completely different one than the self development process for mid-latitude cyclones. One of the salient features in the positive feedback loop for hurricanes is "scale interaction." In a nutshell, processes on the spatial scale of convection (thunderstorms, for example) work to amplify changes on a larger spatial scale (such as lowering surface air pressure in the eye of a hurricane). In turn, amplification on the larger spatial scale amplifies convection (thunderstorms), and the feedback loop is off to the races.

We'll delve much deeper into the details later in the course, but here are the basics of the feedback: As eye-wall thunderstorms mushroom upwards and intensify, the magnitude of the secondary circulation (and divergence aloft) becomes greater, as does subsidence and compressional warming in the eye. This all sets the stage for negative pressure tendencies near the ocean surface (surface pressure decreases with time), which draws more low-level air inward to rise in thunderstorm updrafts, and the cycle continues. The key to maintaining the whole process is sustaining organized deep convection around the core of the storm.

As you now know, tropical cyclones operate quite a bit differently from mid-latitude cyclone, so make sure that you understand the main contrasts between the two types of storms. To help you keep track of the major differences, below is a quick summary, highlighting the key differences between mid-latitude and tropical cyclones.

By now, I hope you're beginning to appreciate the differences between the mid-latitudes and the tropics. But, we're not done quite yet. Even the tools that tropical forecasters use are different! We'll start with map projections next. You'll quickly see that the map projections commonly used in the mid-latitudes don't work so well in the tropics!

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The fact that Earth is a sphere presents some hurdles for map-makers (and weather forecasters). Trying to accurately depict our spherical Earth on flat maps brings some real challenges, and the resulting process is always imperfect. Because of these imperfections, many types of map projections exist. Depending on the type of map projection, it is possible to minimize (or, in some cases, eliminate altogether) distortions in shapes, areas, distances and directions (the "Big Four" that map-makers worry about). But, no single map projection accurately preserves them all. Indeed, minimizing or eliminating distortions in one or two of the "Big Four" often results in gross distortions in the others.

Because a number of different projections exist, weather forecasters must always be aware of the benefits and limitations of viewing data displayed on various map projections. From your previous studies, you should be familiar with the polar stereographic projection, which is commonly centered on the North Pole. The benefit of such polar stereographic projections is that it allows forecasters to track the movement of weather systems in the middle and high latitudes over long distances (for example, check out this enhanced infrared satellite loop of the entire Northern Hemisphere displayed on a polar stereographic projection). It is, however, important for forecasters to get their bearings when looking at polar stereographic projections because compass directions are not preserved. For example, in the polar stereographic map below, the arrow off the Pacific Coast of the United States represents a wind blowing from due west (270 degrees). An arrow representing a due west wind off the East Coast of the U.S. would be oriented quite differently, though, because it still would need to parallel the nearest latitude circle.

Polar stereographic map projections are commonly used by forecasters when tracking weather features in the middle and high latitudes, but not in the tropics. Why are polar stereographic projections not favored by tropical forecasters? For a clue, check out Texas and Alaska on the map above. They look to be nearly the same size, but in reality, Alaska is more than twice the size of Texas. Indeed, polar stereographic projections like this one suffer from gross size distortions farther away from the North Pole, and that's a problem when analyzing tropical weather patterns.

Tropical forecasters, therefore, turn to Mercator projections like the one below to track tropical weather systems. Distance distortions in the tropics are very limited on Mercator maps; however, they have major distance distortion problems at higher latitudes, as the image below indicates. As a result, Alaska completely dwarfs Texas (far more than in reality). At even higher latitudes, Greenland looks to be about the size of Africa, but in reality, Africa is more than 13 times larger than Greenland. At the extreme, the North and South Poles (single points, in reality) appear as straight lines at the top and bottom of Mercator maps. Now that's distortion!

The limited distortion in the low latitudes is one reason why the Mercator projection is the map of choice for tropical forecasters. Another reason for its favored-map status is the relative ease in plotting and interpreting the tracks of tropical cyclones. That's because any line drawn between two points on a Mercator map preserves compass direction (formally called a rhumb line). For this reason, tracking tropical storms and hurricanes on Mercator maps is standard practice at the National Hurricane Center. For example, check out this five-day forecast for Hurricane Katia, issued at 5 A.M. EDT on August 31, 2011. The fact that Katia was moving toward the west-northwest, and was predicted to take a turn toward the northwest in the next five days is easy to discern because of the use of a Mercator map. The cost of preserving compass directions, however, is the large distortions at higher latitudes.

To understand why distances are accurately represented in the tropics (and not at higher latitudes), you need to have a general understanding of the technique for creating Mercator projections. The common Mercator map is a cylindrical projection that accurately represents east-west distances along the equator (in other words, the distance scale is true). Nonetheless, distances are reasonably accurate within 15 degrees of the equator, making the Mercator projection ideal for the tropics. I should point out that Mercator projections can be constructed so that east-west distances are accurate along two standard latitudes equidistant from the equator.

Because horizontal distances in the tropics are depicted with reasonable accuracy, it is possible to look at satellite loops of hurricanes and do a quick rough calculation of the storm's westward speed across the Atlantic. The key to these calculations is realizing that at the equator, Earth's circumference is 24,901 statute miles, and we have 360 degrees longitude in total. Simple division tells us that one degree longitude at the equator is equivalent to 69 statute miles (or 60 nautical miles). As we move away from the equator, this distance changes, but in the tropics, it serves as a good approximation (especially within about 15 degrees of the equator). If you're wondering, one degree latitude is always equivalent to these values.

Yes, the method is pretty old-fashioned but it's fairly simple and can be quite useful. Perhaps the simplest way to make these estimates is to literally put one finger on the center of the hurricane in the first image of a satellite loop and then your thumb on the storm's center in the last image (as demonstrated by Lee Grenci on his "old school" computer monitor). Then, simply estimate the number of longitude degrees between your finger and thumb, multiply by 69 statute miles (or 60 nautical miles), and divide by the time (in hours) in order to calculate the forward speed in statute miles per hour (which most folks would just call "miles per hour.") or nautical miles per hour (which most folks would just call "knots").

For example, check out this loop of infrared satellite images showing Hurricane Fabian from 21Z on August 27, 2003 through 21Z on August 29, 2003. Fabian was moving roughly westward near 15 degrees North latitude during this time, and by my estimate, moved from about 31 degrees West to about 45 degrees West (a total of 14 degrees longitude). If we wanted the forward speed in knots (nautical miles per hour), multiplying 14 degrees by 60 nautical miles per degree gives a total of 840 nautical miles in a 48-hour time period, for an average speed of about 17.5 knots (20 miles per hour). Of course, we now have sophisticated computer models that predict positions and movement of tropical cyclones, but for short-term forecasts (say, less than 12 hours), extrapolating the storm's current motion can sometimes be quite useful (possibly even yielding superior results to computer model guidance).

Extrapolating current tropical cyclone movement can be helpful when the storm's environment doesn't change much, but tropical cyclones often change directions as their steering environments change. Furthermore, tropical cyclones don't always move from east to west, nor do they always stay in the tropics! Many tropical cyclones eventually curve toward the poles (as Hurricane Fabian eventually did) As they do so, Mercator maps become less useful because of the increasingly large distortions at higher latitudes. For example, check out this three-day forecast for Hurricane Karl (2004) from the National Hurricane Center plotted on a Mercator projection. At the latitudes where Karl was predicted to travel, it's pretty difficult to get a feel for the storm's predicted forward speed because the distances on the map are so highly distorted.

So, what do forecasters do as storms enter the middle latitudes? They turn to the Lambert conformal projection, which is a conical map projection that preserves distances along two standard latitudes (typically 30 and 60 degrees north -- note that the standard latitudes lie on the same side of the equator). Moreover, distortion is minimized in a narrow band along the two standard latitudes, but it increases with distance from these standard parallels. As its name suggests, the map projection is conformal, meaning that it preserves the proper angles between intersecting lines and curves and thus tends to preserve the shapes of relatively small areas better than other kinds of projections. Even though Lambert conformal projections preserve the shapes of small areas, it distorts their sizes, particularly those areas that lie relatively far from the standard latitudes.

In most of the mid-latitudes, however, distortion is relatively low on Lambert conformal projections (it's not nearly as significant as it is in the deep tropics). For this reason, meteorologists frequently take advantage of the shape-preserving nature of the Lambert conformal projection as tropical cyclones move out of the tropics into the mid-latitudes. Preserving the shapes of tropical cyclones as they travel to higher latitudes (on satellite images, for example) is important to forecasters because they continually look for physical changes in these weather systems to help them get a better handle on their current and future states.

Now that we've covered the map projections most commonly used by tropical forecasters, I want to now talk briefly about the computer models used in tropical forecasting. If you pay close attention, you'll note the frequent use of Mercator maps to display model data in the tropics. You'll also note that some of the commonly-used forecast plots are a bit different than what you may already be familiar with. Keep reading!

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Although we covered an "old-school" approach for short-term tropical cyclone track forecasts on the previous page, we have many sophisticated tools for predicting the track and intensity of tropical cyclones. Indeed, the advent of computer model guidance revolutionized weather forecasting, and tropical forecasting is no exception. Thanks to developments in computer guidance, reasonably accurate forecasts for tracks of tropical cyclones are now the norm several days in advance.

You're already familiar with how computer models work and what some of their main flaws are from your previous studies, and you should be familiar with some commonly used computer models and forecast variables used for forecasting in the middle latitudes. That basic knowledge is still applicable to the tropics, but because tropical cyclones operate differently than mid-latitude cyclones, tropical forecasters need to evaluate some different forecast variables than mid-latitude forecasters do.

Before we get into some of those new forecast variables, let's start with addressing what computer models are commonly used for tropical forecasting. Since tropical cyclones are a global phenomena, forecasters often turn to "global models" (that is, models that have a domain covering the entire globe) to keep tabs on tropical cyclones in any basin. The flagship global model run in the United States is the GFS model, which you should already be familiar with. The U.S. Navy also runs a global model called the NAVGEM, which stands for NAVy Global Environmental Model. Other prominent global models are run in other countries, such as the United Kingdom (UKMET), Japan (JMA), Canada (CMC), and the model from the European Centre for Medium-Range Weather Forecasts (ECMWF).

For an example of what a tropical cyclone looks like in the broad domain of one of these models, check out the GFS forecast valid at 18Z on August 14, 2023 above (initialized six hours earlier). The "footprints" of four tropical cyclones (circled) are apparent as regions of relatively low sea-level pressure. From this image, we can get the idea that tropical cyclones are relatively small features in the scheme of things (certainly compared to the larger mid-latitude cyclones located at higher latitudes). Just a few decades ago, global models had resolutions that were so coarse that they weren't of much use in providing detailed looks at the core and wind field of a tropical cyclone, but resolution has increased so that global models can provide these details to some degree. Still, other models have been developed specifically to provide more detailed guidance for existing tropical cyclones.

NOAA's flagship model developed specifically for tropical-cyclone forecasting is the Hurricane Analysis and Forecast System (HAFS), which became operational in June, 2023. Some benefits of the HAFS include the fact that it is "ocean coupled," which means that changes in the ocean and atmosphere respond to each other in the model, which is not the case in most global models. Ocean coupling in a model can be a big advantage because as you'll learn later, strong hurricanes can dramatically alter the characteristics of the ocean beneath them, which can then in turn alter the intensity of the storm.

The HAFS is also run at a relatively high resolution, with "nests" that follow individual storms along in time. Its high resolution means that it is capable of predicting small-scale structures within a storm. Of course, there's no guarantee that these small-scale details will be accurate for any given storm, but the ability to realistically simulate deep convective cells can be very helpful in simulating processes in the cores of tropical cyclones, which can improve intensity prediction, on average. As an example of the detail provided by these forecasts, check out the 6-hour forecast (below) of composite radar reflectivity and mean sea-level pressure for Super Typhoon Doksuri, valid at 06Z on July 25, 2023, as it approached northern Luzon in the Philippines.
 

The core of Doksuri was depicted with great detail as it approached northern Luzon, and the HAFS predicted a central pressure of 917 mb. But, as I just mentioned, while the HAFS can make highly-detailed predictions, there's no guarantee that they'll be accurate (the lowest estimated central pressure during Doksuri's life was 926 mb).

The HAFS is actually run in two configurations -- HAFS-A and HAFS-B (note that the forecast prog above is from the HAFS-A). While the HAFS is not a global model, the HAFS-A configuration is run in all tropical basins. The HAFS-B configuration is only run on tropical basins under the responsibility of the National Hurricane Center and the Central Pacific Hurricane Center. The HAFS-A and HAFS-B also have some differences in their ocean coupling schemes and how they simulate some small-scale physical processes. Furthermore, tropical cyclones in the HAFS-B domain that have Doppler radar and other data collected during aircraft reconnaisance flights have some extra initialization data compared to storms in other basins.

Lest you think that NOAA didn't run tropical-cyclone specific models until the HAFS debuted in 2023, there's actually a history of such models going back to the early 1990s with the GHM (Geophysical Fluid Dynamics Lab Hurricane Model). Earlier generations of tropical-cyclone specific models also consisted of the HMON (Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic model), which became operational in 2017, and the HWRF (Hurricane Weather Research and Forecasting) model, which became operational in 2007. The HWRF in particular was ground breaking because it was first operational model to be able to assimilate Doppler radar data collected during aircraft reconnaissance flights in its initialization. The HMON and the HWRF are still being run, but are planned to be phased out.

With many modeling options available, it's important to remember from your previous studies that forecasters look for consensus among the models and diligently comb over real-time observations that might offer clues about which model has more of a handle on a particular weather system. The same approach rings true for predicting tropical cyclones. If you're curious about where you can access model guidance from the global models and other models specifically created for predicting tropical cyclones, I'll have some links to data sources later on the page. But, for now, I want to talk about the value of ensemble forecasts in tropical forecasting.

Ensembles

As you know, computer guidance is fallible, and often, various models have differing solutions. Indeed, check out the average cyclone forecast track errors of various computer models. Given that no models are perfect, and their solutions are often different, do forecasters have any tools at their disposal for helping them navigate the sea of uncertainty? Ensemble forecasts, to the rescue! Ensemble forecasting embraces the tendency toward differing forecast solutions by allowing forecasters to see a range of possible forecast outcomes, which allows forecasters to gauge uncertainty.

You've already been exposed to the basics of ensemble forecasting, but allow me to quickly review. Recall from your previous studies that the data used to initialize a computer model is always imperfect (we're nowhere close to being able to perfectly measure variables in the atmosphere everywhere at all times). So, the model initialization always contains errors. Ensemble forecasts are created by slightly altering the initial conditions fed into the model and / or altering the model physics (recall that a model's ability to mimic the atmosphere is not quite perfect). Each slight altering of the initial conditions or model physics generates an ensemble member. When there's very little spread in the solutions from all ensemble members, confidence in the operational model solution is high, but when lots of spread exists among the individual member solutions, confidence is lower.

How can slightly altering the model's initial conditions lead to different solutions and allow us to gauge uncertainty? The details are beyond the scope of this course, but allow me to invoke a metaphor. Imagine that the tweaks to the initial conditions is akin to "tickling" the virtual atmosphere to see if you get any reaction. If you get a noticeable reaction (a set of very different solutions for a specific forecast area), you know you've found a "sensitive" area, where minor errors in the model initialization can create rapidly growing forecast errors, which cause lots of forecast uncertainty. If you tickle the atmosphere and don't get much reaction (ensemble member solutions look very similar), then the forecast isn't particularly sensitive to small errors in initialization, and we can have more confidence in the solutions. For example, check out the ECMWF ensemble track forecast for Hurricane Ian initialized at 12Z on September 27, 2022 below.

The most striking message from this ECMWF ensemble forecast for Hurricane Ian is that confidence in the track forecast was pretty high within the first 24 hours (little spread in the solutions), but confidence steadily lowered with increasing forecast time (it's simply the nature of the beast that errors associated with computer guidance grow with increasing time). Another type of ensemble approach is to plot track forecasts from completely different models in a similar fashion. As an example, check out this corresponding plot showing predicted tracks of Hurricane Ian from a myriad of models run at 12Z on September 27, 2022. These models also showed some "scatter" in their predictions for Ian's landfall along the west coast of Florida, suggesting that at the time these models were run, Ian's landfall location was highly uncertain.

We can take a similar ensemble approach with tropical cyclone intensity forecasts, as demonstrated by this plot of intensity forecasts for Hurricane Ian initialized at 12Z on September 27, 2022. The bottom line with respect to ensemble forecasts (both ensembles from the same model or completely different models) is that they help forecasters gauge the uncertainty and see the range of possibilities in a given forecast situation. That's very helpful information, and it's much better to take into account the range of possibilities as opposed to locking in on a couple of operational model runs.

If you're looking for more information and background on the variety of computer guidance available to tropical forecasters (including some of the individual models displayed on these ensemble plots), check out the Explore Further section below. In the meantime, I want to give you a list of resources on the Web where you can access computer guidance for tropical forecasters.

Yes, there's a wide variety of model guidance available to tropical forecasters, and you're now prepared to access guidance from a variety of models. But, because tropical cyclones operate differently than mid-latitude cyclones, some unique forecast variables are of interest to tropical forecasters. We'll take a look at these variables next by taking a tour of a rich online resource for tropical forecasters -- the Penn State Tropical e-Wall.

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In order to introduce you to some of the forecast variables that are important to tropical forecasters (and which may be new to you), I want to take a brief tour of the four-panel forecast progs available on the Penn State Tropical e-Wall. You may encounter other web pages with model graphics that include many of these same variables, but the four-panel display on the e-Wall provides a convenient way to look at them simultaneously.

The tropical e-wall allows you to keep tabs on tropical cyclones in the major basins around the world, and includes satellite imagery and various specialized model fields. The four-panel progs (like the one below) are the most frequently used item on the page, so I'll take a little time to discuss their format because they're not the standard progs you've seen before. Below is a sample of a four-panel tropical prog from the GFS over the Northwestern Pacific basin. The initialization for this 12-hour forecast was 00Z on June 7, 2005, so the prog was valid at 12Z on June 7.

I'll briefly touch on each panel, with the understanding that we'll discuss all the concepts that I mention in much greater detail later in the course. Let's start with the upper-left panel. It shows the predicted mean wind speed (color-coded in knots) and wind direction (streamlines) for the tropospheric layer between 850 mb and 250 mb. Since the mean flow in this layer is a primary contributor to the steering of strong tropical cyclones, this panel essentially shows the predicted steering currents in the basin.

The upper-right panel shows the predicted mean sea-level isobars. The shades of blues and oranges/red represent areas of anticyclonic (negative) and cyclonic (positive) relative vorticity at 925 mb (standard height is 750-800 meters). The units are x10^-7sec^-1. Tropical forecasters use this field to forecast the development of tropical cyclones, which depends, in part, on a source of low-level cyclonic vorticity. For example, the bull's eye of closed isobars and strong cyclonic relative vorticity off the east coast of Asia is the footprint of Typhoon Nesat.

Onto the lower-left panel, which displays vertical wind shear (the change in wind speed and / or wind direction with increasing altitude). Specifically, the lower-left panel displays the predicted east-west component (called the "zonal" component) of the wind shear between 850 mb and 250 mb (color-coded in meters per second). Westerly wind shear appears in shades of red and orange while darker shades of blue mark easterly shear. Streamlines indicate the direction of the total shear vector (the resultant from the vector difference between the wind directions at 250 mb and 850 mb).

Why do forecasters assess the predicted "deep layer" vertical wind shear? In a nutshell, when vertical wind shear is too strong, tropical cyclones can't maintain organized thunderstorms around their cores. Thus, vertical wind shear between 850 mb and 250 mb (or a similar layer) must be relatively weak for tropical cyclones to form and develop. Basically, tropical forecasters look for wind shear values to be less than 10 meters per second (about 20 knots) as an indication of favorable conditions for genesis and development of tropical cyclones.

There's one subtle point to keep in mind about vertical wind shear: A hurricane can create its own vertical wind shear. Indeed, the cyclonic circulation of winds around a hurricane sometimes produces a narrow swath (or swaths) of vertical wind shear on the periphery of the storm. As a general rule, you should ignore this swath (or swaths). Rather, focus your attention on the overall pattern of the surrounding environmental vertical wind shear in which the tropical cyclone is embedded. For example, check out this forecast prog showing Hurricane Igor in 2010. Note, on the lower-left panel, the swaths of relatively high westerly and easterly zonal wind shear on the northern and southern flanks of Igor (respectively). These swaths of high zonal shear were associated with the storm's cyclonic circulation, and were not part of the overall environmental shear (and, thus, did not hinder the storm).

Finally, the lower-right panel displays the predicted mean relative humidity between 700 mb and 500 mb. RH values greater than 70% appear in shades of green, while RH values less than 30% are marked by shades of peach. Relatively moist air in the middle troposphere is favorable for the genesis and development of tropical cyclones, while very low relative humidity values in the middle troposphere are unfavorable. Keep in mind that it's not unusual to see a pocket of high mid-level relative humidity collocated with a hurricane (again, check out this forecast prog showing Hurricane Igor in 2010). That's because the updrafts that sustain showers and thunderstorms promote cooling, lowering mid-level temperatures and increasing relative humidity there. It's the really low relative humidity (the peach colors on the progs in the PSU Tropical e-Wall) that's the kiss of death for hurricanes. Meanwhile, the short slashes on the lower-right panel mark areas of the ocean with sea-surface temperatures in excess of 26 degrees Celsius since SSTs greater than 26 degrees Celsius tend to favor development.

That covers the four panels of the progs on the Penn State Tropical e-Wall. But, these progs aren't all the page has to offer. If you're interested in learning about a few of the other products available on the page, check out the Explore Further section below. In the meantime, you're not faced with weeding through all of the computer model guidance on your own. Professional tropical forecasters around the world are always keeping tabs on the tropics, and they regularly issue forecast products when tropical cyclones are lurking. In the next section, we'll focus on some of the main forecast products available from the National Hurricane Center. They can be a great learning tool!

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With a wide array of model data available online, it's easy for newer forecasters to get lost in a sea of forecast data. Not to worry, though! Professional tropical forecasters around the globe are constantly watching over the tropics, and issuing forecast products when tropical cyclones form. These products from RSMCs like the National Hurricane Center (NHC) in Miami, Florida, are a great help as you track current tropical cyclones. In addition, they also allow you to virtually shadow professional forecasters to get a sense for how they're thinking about a particular forecast. Indeed, professional forecasters can be an invaluable knowledge resource, even if you don't have "personal connections" to any of them.

In general, tropical forecasters have access to numerous types of observations (some of which you'll learn about later), as well as the same suite of models we've covered. That's a lot of information to manage! The solution? The Automated Tropical Cyclone Forecasting system (ATCF) was developed to streamline the forecasting of tropical cyclones at operational forecasting centers run by the U.S. Department of Defense (like JTWC) and the National Weather Service (like NHC). All of the data are organized in files called "decks" which become the data sources for many graphics used by professional forecasters (many of which are available online). If you're interested in reading more about the various "decks", the Tropical Cyclone Guidance Project provides a brief discussion of these ATCF files in their description of real-time guidance.

Professional forecasters at NHC process and analyze the available data to develop forecast products in line with their mission statement, which is to "save lives, mitigate property loss, and improve economic efficiency by issuing the best watches, warnings, forecasts, and analyses of hazardous tropical weather, and by increasing understanding of these hazards." From its headquarters on the campus of Florida International University, NHC has responsibilities covering 24 countries in the Americas and the Caribbean Islands, as well as maritime interests in the North Atlantic Ocean, Gulf of Mexico, Caribbean Sea, and Eastern Pacific (north of the Equator).

Even when no active tropical cyclones are present within their jurisdiction during hurricane season (June 1 - November 30 for the Atlantic Basin, May 15 - November 30 for the Eastern Pacific), forecasters at NHC are still watching for areas of potential development, which you can follow with their daily tropical weather discussions (Atlantic Discussion; Eastern Pacific Discussion). However, when a tropical cyclone forms within their jurisdiction, that's when NHC's Web page becomes more active with an abundance of compelling information and images. I won't cover all of NHC's tropical cyclone forecasting products in this section, but I do want to briefly cover the major products so that you know what's available and you can seek professional guidance when tropical cyclones are active in the Atlantic or Eastern Pacific. For the sake of simplicity, I'll separate NHC's products into text products and graphical products. For the record, the same set of products is also available from the Central Pacific Hurricane Center (CPHC) in Honolulu when storms enter their domain.

NHC Text Forecast Products

For a comprehensive overview of all of NHC's text products, you can check out NHC's text products description page. For brevity's sake, I'm only going to highlight and summarize three of the most commonly encountered text products:

• Public Advisories are issued by NHC every six hours (03Z, 09Z, 15Z, 21Z) once a tropical cyclone forms, and are meant to do just what their name implies - advise the public of a tropical cyclone's current status and potential impacts. Check out this sample public advisory for Hurricane Sandy issued at 11 A.M. EDT on October 29, 2012. Note that the critical current facts about the storm (location, maximum sustained winds, central pressure, and current movement) are listed right at the top of the advisory. Following the current status of the storm are sections discussing watches and warnings, a brief outlook, and impacts. When a tropical cyclone threatens land, NHC quickens the pace, issuing public advisories as frequently as every two or three hours depending on the situation.
• Forecast Advisories are a bit more technical than public advisories (see the corresponding forecast advisory for Hurricane Sandy as an example). They're issued by NHC on the same schedule as public advisories, and include much of the same critical information as the public advisories. In addition, forecast advisories also include an estimate for the diameter of the eye in nautical miles, and maximum distances that tropical-storm-force winds (34 knots), storm-force winds (50 knots) and hurricane-force winds (64 knots) extend from the storm's center ("wind radii"). For maritime interests, forecast advisories routinely include distances that waves at least 12-feet high extend from the storm's center ("12-foot wave height radii"). If you need help deciphering a forecast advisory, NHC provides a handy online guide that you can reference (it's very helpful for decoding the forecast information). An added feature of the forecasts beyond 72 hours is that they offer a statement about previous errors in forecasting the storm's track and intensity.
• Forecast Discussions are also issued on the same six-hour schedule as public advisories, and allow you to eavesdrop on how NHC forecasters are thinking about a storm behind the scenes. Given the valuable experience of NHC forecasters, reading forecast discussions can be a tremendous way to learn about forecasting tropical cyclones. Regularly, forecast discussions contain comments on interesting storm features, forecasters' concerns about their current estimate of storm position or strength, model uncertainty and performance, explanations of the decisions forecasters have made, and more. Sometimes, forecasters even liken what they see in certain storms to past storms (an example of "analog forecasting"). The corresponding forecast discussion for Hurricane Sandy shows that forecasters were thinking about Sandy's status as a tropical cyclone as it headed toward landfall (toward the bottom of the discussion).

NHC Graphical Forecast Products

In addition to text forecast products, NHC also issues a number of graphical forecast products, some of which you may already be familiar with because they're so commonly seen on television weathercasts or online. First is NHC's forecast cone of uncertainty. The track forecasts produced by NHC (or any other forecasting outlet, for that matter) aren't perfect, so only providing a single solution would inevitably be fraught with error (check out the average errors for NHC 24, 48, 72, 96, and 120-hour track forecasts). While NHC's track forecasts continue to steadily improve, even three-day forecasts average almost 100 nautical miles of error. Thus, forecast cones of uncertainty, such as the one for Hurricane Irma at 5 A.M. EDT on September 7, 2017 (below), help reflect that the path of the center of the storm is uncertain.

The position of Irma's center at the time the graphic was issued is marked by the black "X." The series of black dots indicate the successive predicted positions of Irma's center (they're just a plot of the coordinates from the forecast advisory). The letters within each dot indicate Irma's predicted intensity at each forecast time ("H" = Hurricane; "M" = Major Hurricane). The white shaded area reflects the cone of uncertainty through Day 3, while the cone for Days 4 and 5 is marked by the white-stippled area. Note how the cone of uncertainty widens with time, reflecting the growing uncertainty as forecast lead time increases.

The width of the cone is based on NHC's historical forecast errors for the previous five years, so the actual width of the cone changes a bit every year. NHC data suggest that the five-day path of a tropical cyclone's center will remain entirely within the five-day forecast cone approximately 60-70% of the time. It should be noted, however, that hurricanes are not "points". They are storms with horizontal breadth. As a result, tropical-storm and hurricane conditions may occur outside the cone, even if the center of the storm remains within the forecast cone of uncertainty. To help make that point, NHC includes a depiction of the current wind extent around the center of the storm (brown and orange shading show the extent of hurricane and tropical-storm force winds, respectively).

Tropical cyclone intensity forecasts can also be quite uncertain, as suggested by this plot the average error for NHC 24, 48, 72, 96, and 120-hour forecasts. Improvements in intensity forecasting have generally been more modest (suggesting that much work remains to be done toward improving intensity forecasts), and have been most notable for four and five day forecasts. Given the challenges associated with intensity forecasting, NHC produces some probabilistic forecast graphics for tropical cyclone intensity. In an effort to produce products that are simple to comprehend and focus on potential impacts, NHC created graphics showing probabilities of wind speeds reaching or exceeding 34 knots (tropical-storm force), 50 knots (storm force), and 64 knots (hurricane force) within a five day period. The image below represents the probabilities that sustained wind speeds would exceed 34 knots (tropical storm-force) from 2 A.M. (EDT) on September 7 to 2 A.M. (EDT) on September 12, 2017.

Note that sustained tropical-storm force winds were nearly certain across parts of south Florida during this period. Given that Irma was still a far from Florida, however, tropical-storm force winds weren't a sure thing farther north. The probabilities of hurricane force winds in south Florida were lower during this time period (no better than a 50/50 chance in any given location) because of the uncertainties in the storm's future intensity and track, as well as the fact that hurricane-force winds occur over a much smaller area of the storm. If you'd like to see where tropical storm and hurricane-force winds actually ended up occurring from Irma, check out the "Wind History" product in the Explore Further section below.

Of course, timing the arrival of windy conditions with a landfalling tropical cyclone is important, too. For all practical purposes, most preparations need to be completed before tropical-storm force winds arrive in a given location, so NHC also issues products showing the most likely arrival time, as well as the "earliest reasonable" arrival time of tropical-storm force winds ("earliest reasonable" is defined as the time at which there's only a 1 in 10 chance that they'll arrive earlier). For Hurricane Irma, on the morning of Thursday September 7, 2017, NHC predicted that tropical-storm force winds were most likely to arrive in Florida on Saturday evening, but they could arrive as early as Saturday morning.

Other Useful Products

In addition to the forecast products outlined above, I want you to be aware of a few other aspects of NHC's page. First, their site includes links to a wide variety of satellite imagery from across the globe (some making use of techniques we'll cover later), including some "Floater Imagery." These satellite "floaters" provide a "storm-centric" perspective that follows the storm along in time. They're a great way to get a close-up view of a storm as it moves through the tropics.

After each hurricane season ends, NHC also posts a "Tropical Cyclone Report" for each storm in the Atlantic and Eastern Pacific. These reports contain a wealth of information about the storm, including its origins and history, relevant meteorological statistics, casualty and damage statistics, and a discussion / critique of how the storm was handled by forecasters as it happened. NHC even occasionally makes changes to a storm's intensity in their post analysis if they believe that a more thorough analysis revealed that mistakes were made in real time. The final estimates are contained in a table of "Best Track" data in the report.

That wraps up our look at the forecasting products from NHC. This wasn't a thorough treatment by any means, though. For now, I just wanted you to get a feel for the commonly-used products that are available. NHC produces some other products that we'll cover later. In the meantime if you're interested in NHC and its history, or want to see a few other operational products, I encourage you to check out the Explore Further section below.

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At 11 A.M. EDT on September 4, 2011, the National Hurricane Center upgraded Tropical Storm Katia to Hurricane Katia (see satellite image below) based on the observations of Buoy 41044 in the remote Atlantic Ocean (here's the 11 A.M. discussion, in which they reference the buoy data). Earlier, at 12Z on September 4, Buoy 41044 measured sustained wind speeds of 78 knots and a gust over 90 knots when the center of Katia passed very nearby. When coupled with other data, forecasters were convinced to upgrade Katia to a hurricane at 11 A.M.

In this particular case, several days of observations from Buoy 41044 provide some important insights about the storm. Indeed, this plot of sustained wind speeds (averaged over eight minutes), wind gusts, and sea-level pressure really demonstrates the drastic increase in wind speeds near the center of the storm along with the corresponding sharp drop in pressure around 12Z on September 4. In case you're interested, here's the 12Z station model from Buoy 41044 on September 4, 2011.

Encounters like the one that Hurricane Katia had with Buoy 41044 are, frankly, a bit "lucky," particularly when storms are located over remote ocean waters. Yes, sometimes we hit the jackpot and a tropical cyclone encounters a buoy or a ship (recall from previous studies that ships also provide weather observations over the ocean), but these observations miss many storms. To better see what I mean, check out the image below from the National Data Buoy Center, showing the locations of buoys (and oil-drilling platforms that collect observations) in the Gulf of Mexico, Caribbean Sea, and western Atlantic Ocean.

For the record, the buoys (or oil-drilling platforms) marked with yellow dots are stations that have recorded data recently. Meanwhile, stations marked by red dots hadn't reported data in at least eight hours at the time this image was produced. While the coastline of the United States and the central Gulf of Mexico are well sampled by buoys and observations from oil-drilling platforms, farther out over remote ocean waters, a tropical cyclone finding a buoy is akin to finding a needle in a haystack. The buoys over the Atlantic and other oceans around the world are widely spaced, leaving huge gaps between buoy observations.

The relative wealth of buoy observations along the coasts of the United States is augmented by the Coastal-Marine Automated Network (C-MAN), which was developed by the National Data Buoy Center in the early 1980s to better maintain weather observations near the coasts. C-MAN buoys provide crucial observations in coastal areas, particularly when tropical storms and hurricanes approach the East Coast and Gulf Coast states.

Besides C-MAN, other programs exist that supplement the data provided by the standard buoy network. One such program is the Global Drifter Program (GDP), under the auspice of the Atlantic Oceanographic and Meteorological Laboratory (AOML), which sometimes deploys drifting buoys in the paths of hurricanes. For example, on a mission into Hurricane Fabian in 2003, aircraft dropped 16 drifting buoys ahead of the storm, giving NHC forecasters crucial surface data.

The Global Drifter Program's Web site includes a wealth of data and interesting information, including an archive of deployments by year. You can actually look at the most current positions of drifting buoys in the Atlantic, but as a general rule, data from deployed drifter buoys are not regularly accessible. If you want to track buoy data around tropical cyclones, the resource discussed in the Explore Further section below might be your best bet.

Another special buoy program that you'll want to be aware of is the Tropical Atmosphere Ocean (TAO) project, which covers the equatorial Pacific (see image below). TAO buoys have since been combined with buoys from the Japanese TRITON (Triangle Trans Ocean Buoy Network) project to create the TAO / TRITON array, which contains roughly 70 buoys. As an aside, the TAO / TRITON array has a pretty interesting history, which you can read about in the Explore Further section below, if you would like. Data from the TAO / TRITON array are instrumental in detecting El Niño and La Niña conditions, which as you'll learn later, can have major impacts on global weather patterns.

On the TAO / TRITON Web site, you can access summary plots from individual buoys like this sample summary plot from the TAO buoy located at latitude 5 degrees South and longitude 155 degrees West. This summary, which spans from December, 2002 to December, 2003, represents a running five-day mean of wind speed and wind direction, elevation of sea level (not counting ocean waves) and temperatures from the sea surface to a depth of 300 meters. When you looked at the plot, you may have noticed that sea level in the vicinity of this buoy is not flat, nor does it correspond to an elevation of zero. We'll talk more about variations in sea-surface height in a later lesson.

One important note about the data on these graphs: You've learned that standard meteorological convention is to plot and express wind direction as the direction from which the wind blows. But, on the topmost graph of wind speed and wind direction, the red slashes extending outward point in the direction that the wind is blowing toward (exactly the opposite of the standard convention). So, for example, the winds from about October, 2002 to March, 2003 blew predominantly from the northeast (toward the southwest) at this buoy. By the way, the length of the red slash indicates the wind speed (in meters per second).

We'll return to data from the TAO / TRITON array later on when we cover El Niño and La Niña, but I wanted you to be aware of the TAO / TRITON project since it's an important component of the system of buoys that monitors tropical weather. Even with special buoy programs, however, the overall picture should be crystal clear to you by now -- ocean buoys simply can't cover the entirety of tropics, and they leave lots of gaping holes in our observing system. Therefore, forecasters must rely on other data sources to get a more complete picture of the state of the tropics. We'll start our investigation of those other sources by looking into the role that aircraft observations play in observing weather in the tropics (particularly when tropical cyclones are present). Read on.

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In this section, we're going to focus on the activities of the Air Force Hurricane Hunters, but did you know that Hurricane Hunters are not the only aviators that contribute to weather analysis and forecasting? As you've learned, the data collected by radiosondes aboard weather balloons contribute to the constant pressure analyses that you're accustomed to (at 500 mb or 300 mb, for example). But, the data captured by instruments on weather balloons is also supplemented by in-situ observations taken by commercial jets. Recall that the standard height of the 300-mb surface is 9,000 meters -- roughly 30,000 feet, which is a representative altitude where commercial aircraft often cruise.

How can we tell what data is contributed by commercial aircraft? Check out the DiFax 300-mb analysis from 00Z on December 11, 2003 from the National Centers for Environmental Prediction (below). DiFax maps are being used less and less these days, but they were once the standard format for common weather analyses and forecasts.

On the sample DiFax 300-mb analysis above, the station models designated by circles represent radiosonde observations aboard weather balloons. These observations are supplemented by observations from commercial aircraft (square station models colored red for emphasis). Aircraft routinely measure temperature, wind direction, wind speed, and altitude expressed in hundreds of feet (check out a template for the upper-air station model corresponding to an observation taken by commercial aircraft). For example, the aircraft observation over the Gulf of Mexico (lower right) indicates a westerly wind (approximately 270 degrees) blowing at 45 knots and a temperature of minus 38 degrees Celsius. The aircraft was cruising at an altitude of 33,000 ft.

Aircraft observations over coastal Atlantic and Pacific waters also appear on 00Z and 12Z DiFax analyses. Of course, aircraft take observations at other times, too. Indeed, the Aircraft Meteorological Data Relay (AMDAR), and the Aircraft Communication Addressing and Reporting System (ACARS) in the U.S. continuously collect digital communications from commercial aircraft. Not only do AMDAR and ACARS observations show up on DiFax analyses, but they're also incorporated into the initialization of some numerical weather prediction models.

But, observations from commercial aircraft are not enough to fully cover the tropics, obviously. To compensate, meteorologists incorporate satellite-derived winds (wind speeds and directions estimated by satellite at specified altitudes), which we'll cover later in this lesson. Some of these satellite-based observations, however, can be seen in the DiFax analysis above by station models designated by a star (highlighted in green for emphasis). You should also note that the indicated wind speed and wind direction represent the only weather data on station models based on satellite measurements.

To get back to the topic at hand (aircraft observations over the tropics), I point out that commercial and private aircraft prudently fly around big storms. However, a group of intrepid aviators in the U.S. Air Force Reserve, popularly known as "Hurricane Hunters," are available to fly reconnaissance missions into tropical cyclones whenever they develop. During the off-season, they also fly into fierce winter storms that rage along the Atlantic and Pacific Coasts. By the way, NOAA has its own Hurricane Hunters, and I'll talk more about them later in this lesson. To see what the Hurricane Hunters are up to on any given day, check out the Tropical Cyclone Plan of the Day.

Stationed at Keesler Air Force Base in Biloxi, Mississippi, the Hurricane Hunters formally belong to the 53rd Weather Reconnaissance Squadron. During hurricane season, the squadron is ready to spring into action at any sign of a tropical cyclone developing in the region spanning approximately from the mid-Atlantic Ocean (longitude 55 degrees West) to the Caribbean Sea and the Gulf of Mexico. Hurricane Hunters also fly reconnaissance into tropical cyclones over the central and eastern Pacific Ocean, particularly those that might pose a threat to Hawaii or mainland North America. Hurricane Hunters rely on the durable WC-130-J aircraft (from the class of WC-130 aircraft) equipped with an arsenal of weather instruments to monitor tropical cyclones. As an overall package, this instrumentation is called the Improved Weather Reconnaissance System (IWRS).

Flying into the storm

When Hurricane Hunters enter a tropical cyclone, they typically fly an alpha pattern. After flying the first diagonal across the storm (usually at least 105 nautical miles (120 statute miles) on either side of the center), executing a successful alpha pattern amounts to simply making a series of left-hand turns. In this way, WC-130 never flies directly into the teeth of the wind (remember that northern hemispheric low-pressure systems have a counterclockwise circulation). Avoiding the strong direct headwinds allows the aircraft to save fuel and fly longer missions. Moreover, the aircraft collects data in all four quadrants of the storm after making only two passes through the center. The aircraft passes through the center about every two hours and continues the pattern until the next WC-130 is ready to take its place if NHC wants fixes on the storm every six hours and "round-the-clock" surveillance. If NHC wants fixes on the storm less frequently (every 12 or 24 hours, for example), then there's no immediate replacement aircraft when the mission is complete (each mission lasts roughly eight hours, on average).

I should note here that the range of reconnaissance aircraft varies from 2,200 to 3,600 miles (the range depends, in part, on flight altitude). Thus, newly forming tropical cyclones over the eastern and central Atlantic Ocean are, for all practical purposes, out of range for reconnaissance aircraft. In its place, tropical forecasters rely on remote sensing from satellites to assess the intensity and structure of storms (more to come later in this lesson).

Special weather instruments mounted on the WC-130 frequently collect flight-level data, which include air temperature, dew point, wind velocity, air pressure, and altitude of the aircraft (altitude is measured by radar). Onboard computers process flight-level data every second, but "complete" weather observations take 30 seconds. Moreover, the computers are tied to the aircraft's navigational system, allowing the flight meteorologist to determine the position (or location) of each observation. These data are also sent off the plane in real time in various coded formats (we'll explore one in just a moment).

For most of the missions flown into hurricanes, the standard flight level is 700 mb (recall that the standard 700-mb height is 3,000 meters). When forecasters at the National Hurricane Center spot a suspicious cluster of tropical showers and thunderstorms on satellite imagery, Hurricane Hunters will fly a Low-level Investigative Mission at 500 or 1500 feet above the sea surface. At such altitudes, wind data can reveal a closed, low-level circulation that allows forecasters to upgrade the system to a tropical depression. As the depression develops into a tropical storm, Hurricane Hunters typically increase the flight level to 850 mb (recall that the standard 850-mb height is 1,500 meters). As the tropical cyclone further intensifies, Hurricane Hunters increase their flight level to 700 mb (the conventional maximum flight level inside hurricanes). I should note here that Hurricane Hunters fly at higher altitudes on other missions (such as reconnaissance in winter storms).

Vortex Data Messages (VDMs)

Observations from the eye are transmitted to the National Hurricane Center using several specifically coded messages. Perhaps the most commonly used is the Vortex Data Message (VDM), which focuses on conditions near the core of the storm. To see an example of a VDM along with basic descriptions of some of the main blocks of data, check out the annotated VDM below. This particular VDM tabulated the vital signs of Hurricane Otto on the morning of November 24, 2016.

Coded observations in the Vortex Data Message allow the National Hurricane Center to assess the current strength and demeanor of the storm, which, in turn, help to increase the accuracy of their forecasts. Note in the annotated VDM above that much of the information relates to the characteristics of the center of the storm, the maximum winds observed while flying inbound, and the maximum winds observed while flying outbound. However, this annotated VDM really doesn't tell you the specifics of how to translate each item (units, what specific codes mean, etc.). Not to worry, though. We'll cover those important details in the next section. During hurricane seasons over the Atlantic and eastern Pacific, you can access the current Vortex Data Message at the National Hurricane Center. There's also a link for an archive of reconnaissance messages, just in case you're interested.

Vortex Data Messages aren't the only coded messages disseminated by Hurricane Hunters, however. For more on other coded messages from the Hurricane Hunters, check out the Explore Further section below.

The Use of Dropwindsondes

Dropwindsondes (sometimes called "dropsondes" or just "sondes" for short) are instrument packages designed to be dropped from aircraft in order to take observations along their path to the surface. Dropsondes are very similar to the rawinsondes you learned about in your previous studies, but instead of ascending aboard a weather balloon, the descend toward the earth's surface. They have a long history of use in aircraft reconnaissance of tropical cyclones dating back to the 1950s. In the "old days," however, they couldn't be used to gather wind data in areas of clouds or rain. Therefore, forecasters at the National Hurricane Center "extrapolated" flight-level winds (700 mb) to the ocean surface. By "extrapolate" I mean that forecasters multiplied the maximum winds at flight level by a fraction between 0.80 and 0.90 to estimate the maximum surface winds (you will learn later in the course that the fastest winds in a hurricane typically blow at altitudes of several hundred meters above the sea surface).

This method ultimately proved to be fairly reliable, except for a few "misbehaved" storms. While scientific principles laid the groundwork for the extrapolation technique used by the National Hurricane Center, data collected by Global Positioning System (GPS)-based dropwindsondes beginning in 1997 proved that the scheme works pretty well most of the time. But without reservation, GPS-based dropwindsondes have improved the accuracy of estimating maximum surface winds in a hurricane (and model accuracy for predicting the path of tropical cyclones). If you're interested in learning more about the benefits of using GPS dropwindsondes, check out this research paper.

Once Hurricane Hunters release dropwindsondes in the eyewall of hurricanes (where the surface winds are strongest), the free-falling sonde deploys a drogue parachute immediately, which stabilizes the sonde's descent by stopping it from tumbling in the turbulent air motions within the eyewall. During descent, the in-situ sensors on the dropsonde (see image below) relay observations of pressure, temperature and relative humidity back to the aircraft via radio until the sonde splashes down into the ocean. These observations are processed by computers on board the aircraft as well as on the ground (computers can process real-time observations from multiple dropsondes simultaneously). For the record, in an average hurricane season, Hurricane Hunters release approximately 1,000 to 1,500 dropsondes on training and storm-reconnaissance missions.

Technically, the method for measuring wind speed using dropwindsondes qualifies as remote sensing. No, I haven't lost all my marbles. Each sonde contains a full GPS, which allows satellites to remotely track its exact location. By tracking the changes in the sonde's location in time, computers calculate the wind speed by subtracting out the terminal fall speed and friction.

Wind speed measurements aren't the only remote sensing done by Hurricane Hunters, however. They also use radar to locate the eye and eyewall as well as other tools for measuring surface wind speed, among other things (more later). In the meantime, on the next page, we'll explore the details of how to translate a Vortex Data Message -- perhaps the most widely-used coded message relayed by the Hurricane Hunters.

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In order to fully extract the relevant information from a Vortex Data Message (VDM), you'll need a bit more information than what you just learned. Namely, you'll need to know specifically what information each item contains, along with various codes and units. To help you with translating a VDM, we'll walk through one, item by item, in detail. Make sure to use the links available to navigate easily between each item and its translation.

Before we begin, however, I should point out that the format of VDMs was significantly changed in 2018. The guide for decoding VDMs below is based on the current format, but if you happen to research VDMs for storms that occurred prior to 2018, the format will be different. To help you with any old VDMs you may encounter if you're researching past tropical cyclones, check out the materials I have for you in the Explore Further section below.

The sample VDM that I will decode below was actually the prototype that NHC mocked up when they announced the format change, so it's based on data collected in a real hurricane prior to 2018 (Otto in 2016, to be exact). VDMs are transmitted in an alphabetical manner, and in each report, a letter of the alphabet is followed by information about the center of the tropical circulation. This information includes such items as lat/long of the center, temperatures inside and outside of the eye of the storm, wind information, minimum pressures, etc.

Sample Report: (clicking on each element will take you to the explanation)

URNT12 KNHC 241133
VORTEX DATA MESSAGE   AL162016
A. 24/11:12:50Z
B. 10.97 deg N 082.77 deg W
C. 700 mb 2927 m
D. 977 mb
E. 210 deg 11 kt
F. CLOSED
G. C20
H. 90 kt
I. 144 deg 5 nm 11:07:00Z
J. 253 deg 78 kt
K. 158 deg 8 nm 11:07:30Z
L. 95 kt
M. 314 deg 5 nm 11:17:00Z
N. 033 deg 108 kt
O. 349 deg 14 nm 11:17:30Z
P. 10 C / 3042 m
Q. 18 C / 3045 m
R. NA / NA
S. 12345 / 7
T. 0.02 / 1 nm
U. AF301 0616A OTTO OB 13
MAX FL WIND 108 KT 349 / 14 NM 11:17:00Z

Breakdown of the message:

MESSAGE HEADER

The first line of the message is the code used to identify a vortex message in various meteorological databases, followed by the date and time (Zulu) the message was transmitted. Back to Message

A. DATE AND TIME OF FIX

The time when the center of the storm was located or "fixed". 24/11:12:50Z means the report is from the 24th day of the month, at 11:12:50Z (hours:minutes:seconds of Zulu time). Back to Message

B. LOCATION OF THE VORTEX CENTER ("FIX")

Latitude and Longitude of the vortex fix in decimal degrees. 10.97 deg N 082.77 deg W means 10.97 degrees North latitude, 82.77 degrees West longitude. This information can be used to plot the latest location of the storm center; comparing the current position to previous positions gives the recent movement of the storm. Back to Message

C. MINIMUM HEIGHT AT STANDARD LEVEL

Standard level refers to certain "slices" of the atmosphere used by meteorologists around the world. The exact altitude of each of these slices relates to the pressure. The lower this height is below the "standard" height indicates how low the pressure is inside the hurricane; stronger storms tend to have lower pressures. The number reported is in meters. Hurricane Hunters fly storms at the "surface" (500 to 1500 feet above the water), 925 millibars (2500 feet or 762 meters), 850 mb (4780 ft or 1457 m), or 700 mb (9880 ft or 3011 m).

The aircraft will fly using an autopilot set to follow a constant pressure altitude. For example, when flying a mission at 700 mb, the aircraft's pressure altimeter will read 9,880 feet all day. But as the plane flies into lower pressure, the plane will actually be flying closer to the ground. A radar altimeter bounces radar pulses off the ground and tells the crew how high they actually are, and the meteorologist uses this number to calculate the height of standard surface. In the example above, the 700 millibar height was 2927 meters, which is 84 meters lower than the standard height of 3011 meters. When flying low-level missions (below 1500 feet) this block is reported as NA (Not Applicable). Back to Message

D. MINIMUM SEA-LEVEL PRESSURE

This value, computed from dropsonde or extrapolation, is one of the key pieces of information which indicates the intensity of the storm. "Standard" sea-level pressure is 1013 millibars. Since hurricanes, tropical storms, and tropical depressions are all low-pressure systems, the pressure reported here is almost always lower than standard. The lower the pressure, the more intense the storm. The word "EXTRAP" precedes any pressures extrapolated from aircraft sensor information; if the word "EXTRAP" is not there, it means the pressure was measured directly by a dropsonde released from the aircraft, and is usually more accurate. This lowest pressure is found in the center of the storm, and in this case it was 977 mb. There may be small fluctuations in pressure due to normal, daily pressure rises and falls. Back to Message

E. DROPSONDE CENTER WIND SPEED AND DIRECTION

The wind direction (in degrees) and speed (in knots) at the center of the storm as measured by dropsonde. In this case, winds were from 210 degrees (south-southwest) at 11 knots. In well-developed tropical cyclones, winds at the center will typically be fairly weak compared to the much faster winds found in the eyewall. Back to Message

F. EYE CHARACTER

This is a brief description of what the eye looks like on radar. "CLOSED" means that the eye is completely surrounded by a ring of thunderstorms. "OPEN NE" means there is a break in the eyewall to the northeast, etc. If the eye is not at least 50% surrounded by eyewall clouds, this item and Item G will be reported as "NA" (Not Applicable). Back to Message

G. EYE SHAPE ORIENTATION AND DIAMETER

Eye shapes are coded as follows: C-circular; CO-concentric; E-elliptical and all diameters are transmitted in nautical miles. In this case, "C20" translates to a circular eye with a diameter of 20 nautical miles. Orientation of major axis of an ellipse is transmitted in tens of degrees. Example: E09/15/5 means elliptical eye oriented with major axis through 90 degrees (and also 270 degrees), with length of major axis 15 nautical miles, and length of minor axis 5 nautical miles. CO8-14 means concentric eye with inner eye diameter 8 nautical miles, and outer diameter 14 nautical miles. The "healthiest" hurricanes usually have a small, circular eye. A concentric eye (a ring inside a ring) is a relatively rare phenomenon that may signal a temporary weakening while the storm reorganizes (which we'll explore later in the course). An eye diameter that shrinks (compared to the previous vortex message) may signal intensification: just as a twirling ice skater spins faster as she pulls in her arms, a hurricane may "spin" faster as its eye gets smaller. Eye diameters are usually 10-20 nautical miles, while we sometimes see them as small as 5 nautical miles to as large as 60 nautical miles. Back to Message

H. ESTIMATE OF MAXIMUM SURFACE WIND SPEED OBSERVED ON INBOUND LEG (IN KNOTS)

90 kt means the highest maximum sustained surface wind speed is 90 knots on this particular inbound leg. In the modern era, a Stepped Frequency Microwave Radiometer takes this measurement (I'll discuss how this instrument operates later in this lesson). Back to Message

I. BEARING, RANGE, AND TIME OF THE  WIND SPEED OBSERVED IN ITEM H

The "bearing" is the direction (given in degrees) from the center in which the maximum surface wind speed was recorded (similar to compass headings, except these bearings are in reference to "true" instead of "magnetic" north). Due north is 0 degrees, east is 90 degrees, south is 180 degrees, and west is 270 degrees. The bearing in the example is 144 degrees, which means the surface wind speed was recorded southeast of the center. To pinpoint where this was, you also need to know how far away it was: the "range". In this case, the 90 knot wind reported in part H was found 5 nautical miles (about 6 statute miles) southeast of the center at 11:07:00Z (11:07Z exactly). Back to Message

J. MAXIMUM INBOUND FLIGHT-LEVEL WIND SPEED AND DIRECTION

The highest wind speed in knots (and its direction) observed on the last leg inbound to the storm. These winds are at flight level, and were measured directly by the aircraft's instruments. In the example, the peak wind was 253 degrees, 78 knots, which means the wind was blowing from a direction of 253 deg (west-southwest) at a speed of 78 kts (about 90 miles per hour). Back to Message

K. BEARING, RANGE, AND TIME OF THE WIND OBSERVED IN ITEM J

Same method as reporting bearing, range, and time for the surface winds (see Item I, above). In this example, the 78 knot flight-level wind speed reported in Item J was found 158 degrees (south-southeast) of the center, and 8 nautical miles from the center at 11:07:30Z (in this case, that's 30 seconds after the maximum surface wind speed was observed). Usually the strongest winds are found in the "eyewall" surrounding the eye (if there is an eye), and this gives an idea of how large the center (or eye) of the storm is. Back to Message

L. ESTIMATE OF MAXIMUM SURFACE WIND SPEED OBSERVED WHILE FLYING OUTBOUND (IN KNOTS)

95 kt means the highest maximum sustained surface wind speed estimated while flying outbound from the storm center is 95 knots. Estimates are made in the same fashion as those in Item H. Back to Message

M. BEARING, RANGE, AND TIME OF THE WIND SPEED OBSERVED IN ITEM L

Same method as reporting bearing, range, and time for previous wind observations. In this example, the 95 knot estimated surface wind occurred 314 degrees (northwest) of the center, and 5 nautical miles from the center at 11:17:00Z (exactly 1117Z). Back to Message

N. MAXIMUM OUTBOUND FLIGHT-LEVEL WIND SPEED AND DIRECTION

The highest wind speed in knots (and its direction) observed while flying outbound from the storm's center. These winds are at flight level, and were measured directly by the aircraft's instruments. In the example, the peak wind was 33 degrees at 108 knots, which means the wind was blowing from a direction of 33 degrees (northeast) at a speed of 108 kts (about 124 miles per hour). Back to Message

O. BEARING, RANGE, AND TIME OF THE WIND OBSERVED IN ITEM N

Same method as reporting bearing, range, and time for previous wind observations. In this example, the 108-knot flight-level wind occurred 349 degrees (north-northwest) of the center, and 14 nautical miles from the center at 11:17:30Z (that's 30 seconds after the maximum surface wind speed was observed while flying outbound). Back to Message

P. MAXIMUM FLIGHT-LEVEL TEMPERATURE / PRESSURE ALTITUDE OUTSIDE THE EYE

This gives an idea of the general temperature surrounding the eye. "Standard" temperature at 700 mb (where we fly most hurricanes) is about -5 degrees Celsius, but in the tropics, it's usually 10 to 15 degrees warmer than "standard". What you especially want to look for is how it compares to the temperature inside the eye, in Item Q. The example shows a temperature of 10 degrees Celsius (50 degrees Fahrenheit) at an altitude of 3042 meters (9,980 feet). The altitude is included because the airplane bumps up and down due to turbulence and other factors, and minor changes in the temperature may be due to changes in altitude. Back to Message

Q. MAXIMUM FLIGHT-LEVEL TEMPERATURE / PRESSURE ALTITUDE INSIDE THE EYE

This is yet another indicator of how "healthy" the storm is. One of the unusual features of a hurricane is that it is warmer inside the eye than outside. What you want to look for here is how much warmer it is than the temperature reported outside the eye in Item "P." A developing storm may be only slightly warmer inside the center, while a strong hurricane may be 10 degrees warmer (or more). In this example, the eye temperature of 18 degrees Celsius (64 degrees Fahrenheit) is eight degrees Celsius higher than the temperatures immediately outside the eye. Be sure to look at the remarks in Item "U" to see if there was an even warmer temperature found inside the eye (but more than 5 miles from the fix position). The aircraft was at a pressure altitude of 3045 meters (9,990 feet). Back to Message

R. DEW POINT TEMPERATURE / SEA SURFACE TEMPERATURE INSIDE THE EYE

If available, the dew point measured at the center of the storm (in degrees Celsius) will be reported here; however, a dew point observation was unavailable in this case, so it was reported as "NA" (not applicable). The second part of Item R is no longer used, as the aircraft do not carry the infrared sensors needed to measure sea surface temperature. Back to Message

S. FIX DETERMINED BY / FIX LEVEL

The first string of numbers indicates what the meteorologist used to find the center of the storm, using numbers 1 through 5, as follows: 1-Penetration, 2-Radar, 3-Wind, 4-Pressure, 5-Temperature. After the solidus ("/"), you'll find one or two numbers which show at what level(s) the center was found, as follows: 0-surface, 1-1500 ft, 8-850 mb, 7-700 mb, 5-500 mb, 4-400 mb, 3-300 mb, 2-200 mb, 9-925 mb.

Example: 12345/7 means the fix was determined by all five means: penetration, radar, winds, pressure, and temperature. The fix was made at 700 mb (approx 10,000 feet). If a calm spot was seen on the surface of the water, the fix level could have been "07" to indicate the surface and the 700 mb center were found within 5 nautical miles of each other. Back to Message

T. NAVIGATION FIX ACCURACY / METEOROLOGICAL ACCURACY

These numbers give an estimate of how accurate the position is, in nautical miles. "Navigation accuracy" is a gauge of how well the navigation equipment is operating (within 0.02 nautical miles, in this case). The "Meteorological Accuracy" depends on how well the storm center can be defined by the meteorological data: if there is a sudden, sharp wind shift, and the temperature peak and pressure drop all coincide, the meteorological accuracy will be a small number. A weaker storm will probably have a larger meteorological accuracy. In this case, the meteorological accuracy was one nautical mile. Back to Message

U. REMARKS SECTION

Always starts with the Mission ID (a unique identifier for each mission): AFXXX AABBC NAME OB DD

Agency: Either AF (Air Force Reserve Hurricane Hunters) 
 or NOAA (National Oceanic and Atmospheric Agency)
XXX: Tail number of the aircraft
AA: Number of missions flown on this storm system
BB: Depression number (or "XX" if it's not a depression or greater)
C: Ocean basin. "A"=Atlantic, "C"=Central Pacific, "E"=Eastern Pacific
NAME: Storm name, or words CYCLONE (for depression) or INVEST.
OB: "Observation."
DD: Observation number.

Example: AF301 0616A OTTO OB 13 means Air Force Reserve aircraft number 301 is flying the 6th mission on Hurricane Otto, which is the 16th tropical cyclone of the season in the Atlantic/Gulf/Caribbean, and is making the 13th observation of the storm.

The flight meteorologist may add details of anything he or she feels are interesting to note. There are some standard remarks: "MAX FL WIND 108 KT 349 / 14 NM 11:17:00Z" reminds the public about the location and time of the maximum flight-level wind found in the storm overall (in this case, it's the outbound wind described in Items N and O). Another standard remark is given anytime a temperature peak is seen more than 5 nautical miles from the center location. The flight meteorologist may also further describe characteristics of the eye (such as "STADIUM EFFECT" if the clouds form a solid wall all around the eye, and stretch up and outward to reveal a circle of clear sky above, similar to a football stadium that's 50,000 feet tall), among other things. Back to Message

Read...

The Air Force Hurricane Hunters don't have the "market cornered" on hurricane hunting. Indeed, the Hurricane Research Division (HRD), under the auspice of the Atlantic Oceanographic and Meteorological Laboratory (AOML), routinely flies specially equipped aircraft into hurricanes and other tropical weather systems in a concerted effort to advance the scientific understanding of the tropics and, in the process, improve weather forecasts. HRD has a long and storied history in the pursuit of excellence in hurricane research.

The NOAA Hurricane-Hunter research fleet (see below), which consists of two WP-3D turboprops (sometimes referred to as "NOAA P-3s") and the Gulfstream IV-SP jet, is under the administrative umbrella of the NOAA's Aircraft Operations Center.

One of the primary responsibilities of the Gulfstream-IV jet is to deploy dropwindsondes in the environment around and ahead of hurricanes. Often flying at altitudes as high as 45,000 feet, dropwindsondes released by the Gulfstream-IV jet can assess the winds that steer these storms. For example, as Hurricane Isabel approached the East Coast on September 16, 2003, the Gulfstream-IV jet departed MacDill Air Force Base in Florida and proceeded to deploy 23 dropsondes around the hurricane, as this synoptic-surveillance flight plan indicates. If you're interested, you can read more about the capabilities and specifications of this aircraft at the Aircraft Operations Center's Web site.

The Gulfstream-IV jet also has two radars (one on the nose, and a Doppler radar on the tail), but the weather instrumentation aboard each NOAA WP-3D actually includes three radars (one on the nose, one on the lower fuselage, and a Doppler radar on the tail). In addition to giving insight into the precipitation occurring in the storm, recall from previous courses that Doppler radars have the capability of detecting wind velocities, which helps meteorologists observe the storm's wind field. For example, check out the winds detected by Hurricane-Hunter Doppler radar at an altitude of 500 meters within Hurricane Sandy (in meters per second) at 2351Z on October 28, 2012. The station models plotted on the image represent observations collected by dropwindsondes.

Keep in mind that the range of the ground-based system of radars along the East Coast of the United States (and the Caribbean Islands) is limited and only captures hurricanes that are relatively close to land, making radar images from NOAA P-3's (like the one shown above) indispensable as operational forecasting and research tools. Furthermore, data from these airborne Doppler radars are now being assimilated into the operational forecasting models. In case you want to look at past hurricanes and tropical storms, the Hurricane Research Division provides an archive of their radar data, but note that radar data is not available for every storm. You can check out more on the capabilities and specifications of the NOAA P-3, if you would like. And, if you're into history, this very readable paper published in the Bulletin of the American Meteorological Society discusses the history of the aircraft and the roles that they've played in researching tropical cyclones.

H*Wind Analyses

Recall that when tropical storms and hurricanes are active in the Atlantic and eastern Pacific basins, the National Hurricane Center routinely issues Forecast Advisories, which include the maximum radii from a storm's center of 34-knot, 50-knot, and 64-knot winds. As an example, look at this Forecast Advisory for Hurricane Isabel issued at 03Z on September 14, 2003. Take note of the wind radii just below the maximum sustained wind (in bold below):

ZCZC MIATCMAT3 ALL
TTAA00 KNHC DDHHMM
HURRICANE ISABEL FORECAST/ADVISORY NUMBER 32
NWS TPC/NATIONAL HURRICANE CENTER MIAMI FL AL132003
0300Z SUN SEP 14 2003
 
HURRICANE CENTER LOCATED NEAR 23.0N 63.7W AT 14/0300Z
POSITION ACCURATE WITHIN 15 NM
 
PRESENT MOVEMENT TOWARD THE WEST-NORTHWEST OR 295 DEGREES AT 10 KT
 
ESTIMATED MINIMUM CENTRAL PRESSURE 932 MB
EYE DIAMETER 40 NM
MAX SUSTAINED WINDS 140 KT WITH GUSTS TO 170 KT.
64 KT....... 75NE 60SE 60SW 75NW.
50 KT.......100NE 90SE 90SW 100NW.
34 KT.......175NE 175SE 175SW 150NW.
12 FT SEAS..325NE 325SE 275SW 300NW.
WINDS AND SEAS VARY GREATLY IN EACH QUADRANT. RADII IN NAUTICAL
MILES ARE THE LARGEST RADII EXPECTED ANYWHERE IN THAT QUADRANT.

The numbers and letters to the right of each wind threshold indicate the maximum radii from the storm's center in the four compass quadrants. For example, sustained surface winds of 34 knots (minimum tropical-storm strength) extended 175 nautical miles from the center of Hurricane Isabel into the northeast, southeast and southwest quadrants, but the radius of winds with minimum tropical-storm strength extended 150 nautical miles into the northwest quadrant of Isabel.

Historically, the wind radii on the Forecast Advisories from the National Hurricane Center were rather subjective because forecasters made their own interpretations of data from reconnaissance aircraft and available surface observations (for example, a forecaster may have multiplied maximum flight-level winds by a subjective value based on his own experience). This subjective approach obviously had drawbacks because it could provide some inconsistent results. However, since 1993, experimental wind fields, called "H*Wind Analyses" (like the one shown below), have helped to bridge the gap between subjective and objective analyses.

While these analyses are not freely available to the public in real-time (more on that in the Explore Further section below), they are used by various public and private agencies involved in risk management and mitigation. How are these analyses created? In a nutshell, data from a potpourri of in-situ and remote sources are collected and then processed to conform to a height of ten meters (about 33 feet), giving forecasters one of the most complete looks at a hurricane's wind field available.

If you peruse the text at the top of the H*Wind analysis of Hurricane Isabel at 0130Z on September 18, 2003 (above), you'll see that wind observations from a ship, a moored buoy, GPS dropwindsondes, GOES-12 (more on estimating winds from satellite later), a C-MAN station, an offshore NAVY tower and Air-Force reconnaissance went into the creation of this analysis. For this "AFRES" observation, maximum flight-level winds were extrapolated to the sea surface. The fact that data from a C-MAN station was included is a hint that Isabel was close to the U.S. coast at this time, as this visible satellite image from around the same time as the H*Wind analysis indicates.

At the bottom of the image of the wind field around Hurricane Isabel, note that the analysis quantifies the maximum observed sustained winds at 0130Z and pinpoints the location of the wind max relative to the center of the storm (82 knots, 49 nautical miles northeast of Isabel's center). This location differs only slightly from objective H*Wind analysis technique (82 knots, 51 nautical miles northeast of the center). By the way, the arrow indicates the direction of the maximum winds.

The Stepped Frequency Microwave Radiometer (SFMR)

Only four days earlier, Hurricane Isabel had packed a much bigger wallop as noted by the maximum wind speed of 125 knots listed below the H*Wind analysis at 0130Z on September 14, 2003. I point this out because, if you look closely, something called an "SFMR" observed the maximum wind speed about 19 nautical miles northeast of Isabel's center. SFMR stands for Stepped Frequency Microwave Radiometer, a passive remote sensor mounted on reconnaissance aircraft (both the NOAA and U.S. Air Force reconnaissance aircraft are equipped with SFMR units).

The SFMR is a powerful tool, and I think it's important for you to have a little background about how it works. The underlying principle that the SFMR employs is that the bulk radiative properties of a substance depend on the "nature" of the substance (size, shape, exposed surface area, etc.). By changing the nature of a substance, its radiative behavior changes, too.

For example, check out the side-by-side images below to see how changing the nature of liquid water can alter the transmission and scattering of visible light. The container on the right is one millimeter thick, so along the path to your eye, visible light has to travel through approximately one millimeter of "bulk water." Most of the visible light is transmitted right through the water, making it transparent. The cumulus congestus shown below on the left, however, is largely composed of tiny water drops about 10 microns in diameter (and there are billions and billions of them). Believe it or not, the total cloud-water content along your line of sight is roughly a measley one millimeter (the same amount as in the container on the right). Yet, the cloud blocks out most of the sunlight because the tiny spherical droplets back-scatter visible light a great deal more than the container of bulk water.

Therefore, when it comes to scattering and transmission, collections of tiny liquid drops cause visible light to behave much differently than it does when encountering the same amount of bulk water. Likewise, the nature of a substance can impact the emission of radiation and changes in emission serve as the basis for the SFMR's ability to detect surface wind speeds. You may not realize it, but the sea emits some natural microwave radiation (everything does, actually), but these emissions from the sea are not very large. In microwave-cooking terms, for example, you couldn't cook anything using the microwave radiation emitted by the ocean, but I assure you that natural microwave emissions from the sea are detectable by airborne radiometers like the SFMR.

A relatively smooth ocean (winds are relatively light) emits a certain amount of microwave radiation. But, winds blowing over the ocean change the nature of the surface (and thus, its radiative properties). As wind speed increases, patches and streaks of sea foam (essentially, bubbles) start to cover the ocean surface, and it turns out that these patches and streaks of sea foam emit more microwave energy than a smooth, "foamless" sea. The bottom line here is that the SFMR can infer surface wind speeds by detecting increases in microwave emissions from a foamy sea. And, the coverage of sea foam is a function of wind speed (the faster the wind speed, the foamier the sea).

Of course, it's raining to beat the band outside of the eye of a hurricane (particularly in the eyewall), and raindrops certainly would attenuate microwave emissions from the sea (by "attenuate", I mean that raindrops absorb microwave energy from the sea and thus limit the intensity of the energy reaching the SMFR). But the SFMR measures microwave emissions at six different frequencies between 4.6 and 7.2 Gigahertz (hence, the term "stepped frequency"). At any rate, scientists account for the absorption and scattering properties by raindrops at each frequency. By "stepping" through each frequency, scientists can correct for the attenuation of microwave emissions by rain. In the process of correcting for this attenuation, the rainfall-rate can be recovered, yielding bonus data from the SFMR.

For the record, the following SFMR observations are available:

• peak surface wind (in knots) averaged over ten seconds of measurements
• the rain rate (in millimeters per hour) derived over the same ten seconds
• quality-control "flags" that give weather forecasts an indication of the accuracy of these data

As you've already learned, Hurricane Hunters use the SFMR readings for Items H and L of the Vortex Data Message (estimated maximum surface winds inside the tropical cyclone), but the complete set of SFMR observations are transmitted in code. If you're interested in learning more about the complete coded observations and how to translate them in real-time, check out the Explore Further section below. Otherwise, we're ready to move on from the realm of in-situ and remote sensing from aircraft reconnaissance to remote sensing from satellites. After all, over remote seas aircraft reconnaissance is not feasible, so we need to explore some other techniques that forecasters use to remotely observe tropical cyclones. Read on.

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One of the primary goals of this course is for you to develop the ability to comprehend the discussions, advisories, and forecasts issued by the National Hurricane Center. For example, the references to SFMR measurements that you just learned about probably wouldn't make sense to a member of the general public, but you now have an appreciation of how this passive remote sensor works. This knowledge allows you to integrate such references into your overall understanding of the current or future status of a tropical storm or hurricane. Let's look at another commonly referenced term found in many NHC discussions -- the Dvorak Technique.

At 1445Z on September 8, 2003, satellite imagery revealed that Hurricane Isabel already had an impressive eye, even though the storm was still over the eastern Atlantic (as evidenced by this full-disk water-vapor image). At this time, Isabel was clearly out of range of aircraft reconnaissance; yet forecasters at the National Hurricane Center were still able to estimate that Isabel had a maximum sustained wind speed of 100 knots. Below is an excerpt from NHC's discussion at 15Z on September 8 (note the bold portion in particular):

ZCZC MIATCDAT3 ALL
TTAA00 KNHC DDHHMM
HURRICANE ISABEL DISCUSSION NUMBER 10
NWS TPC/NATIONAL HURRICANE CENTER MIAMI FL
11 AM EDT MON SEP 08 2003
 
ISABEL HAS CONTINUED TO RAPIDLY INTENSIFY. THE INITIAL INTENSITY IS
INCREASED TO 100 KT BASED ON SATELLITE INTENSITY ESTIMATES OF 115
KT FROM TAFB AND AFWA...102 KT FROM SAB...AND 102 KT/T5.5 3-HOUR
OBJECTIVE DVORAK INTENSITY ESTIMATES. THE 100 KT INITIAL INTENSITY
IS ALSO CONSISTENT WITH THE LATEST AMSU INTENSITY ESTIMATES OF 100
KT AND 960 MB.

As you can see, there are several techniques for remotely sensing the estimated intensity of a tropical cyclone. In this section, I'll kick-off the discussion about remote sensing by focusing on the Dvorak Technique so that you can interpret the T5.5 Number referenced in the Isabel discussion. In a nutshell, the Dvorak Technique is an analysis procedure for estimating the intensity of tropical cyclones based on cloud patterns on satellite imagery. The technique is named after Vernon Dvorak, who pioneered the technique with his research in the 1970's and early 1980's.

How does the Dvorak Technique work? In a nutshell, it's really just a statistical system that combines observed cloud patterns on satellite imagery with a set of established guidelines (based on years of observations) to estimate the intensity of a tropical cyclone. These estimates are called T Numbers, which range from 1.0 to 8.0 (check out the Dvorak scale). Please note that the scale refers to a "CI Number" (Current Intensity Number) and not, specifically, a "T Number". However, the two are usually highly similar. Forecasters arrive at a T Number (which estimates a tropical cyclone's intensity) by comparing cloud patterns on a single satellite image (sometimes referred to as the "satellite presentation") to a set of statistical guidelines. Once forecasters determine a T Number, they can then modify it in an attempt to preserve the continuity of past (recent) estimates and account for recent trends in the satellite presentation (indicative of intensification or weakening). The final value, after any modifications, represents the Current Intensity (CI) Number.

Manually conducting a complete Dvorak analysis to arrive at a specific T Number (and adjust to a CI Number) is a fairly complex process, which requires a great deal of experience to perform well. Don't worry, you won't be asked to perform such detailed analyses in this course, but if you're interested in seeing some more details, you may be interested in some of the links in the Explore Further section below. Still, it probably won't come as a surprise to you that some subjectivity exists when forecasters attempt to classify cloud patterns, which is one drawback to the technique. More recently, forecasters at NHC have relied on objective computer analyses that have been developed to take the subjective element out of Dvorak estimations. If you're interested in learning more about this evolution and the details of these objective schemes, check out the Explore Further section below. One standard objective technique is the Advanced Dvorak Technique (ADT), which attempts to achieve the accuracy of the original Dvorak Technique without the subjective limitations. Importantly, the ADT can be applied to any tropical cyclone across the globe, in any phase of its life-cycle (previous objective techniques weren't applicable during certain parts of the life-cycle).

When monitoring tropical cyclones, you can access ADT estimates and imagery at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) ADT page, from National Environmental Satellite, Data and Information Service (NESDIS), and from the Regional and Mesoscale Meteorology Branch of the Cooperative Institute for Research in the Atmosphere (RAMMB-CIRA). As an example of the type of data available from CIMSS, check out the time series below, which plots "adjusted T Numbers" and CI numbers for Super Typhoon Haiyan in early November 2013. You can think of the "Adjusted T Numbers" as the objective counterpart to human-derived T Numbers.

By examining the time series above, you can get a sense of just how close T and CI Numbers typically are. You can also see that Super Typhoon Haiyan "maxed out" the Dvorak Scale according to ADT analysis, reaching the highest CI Number possible (8.0). To achieve such tropical cyclone "perfection" is very rare, indeed, and not surprisingly, Haiyan's satellite presentation was absolutely stunning, with a wide, symmetrical ring of deep convection (marked by very cold cloud tops) surrounding the eye (check out this enhanced infrared image of Haiyan at 0930Z on November 7). In fact, a statement from the Satellite-Services Division of NOAA stated at the time that the Dvorak Technique "makes no allowance for an eye embedded so deeply in cloud tops as cold..." as those seen around Haiyan's eye. In fact, you can tell from the time series above that the adjusted T numbers actually went slightly off the scale for a brief time!

Today, most forecasters use automated and objective Dvorak analyses, and there are many advantages to using the ADT, but performing subjective analyses manually still has value. Indeed, analysts and researchers still regularly conduct manual Dvorak analyses. While you won't have to do complete Dvorak analyses in this course, conducting some basic Dvorak classifications can still help you become "one with the atmosphere" so that you can really be in tune with how a particular storm is evolving. As your experience grows in tropical weather forecasting, you will discover that tropical cyclones appear in a variety of sizes and shapes on satellite imagery. A major component of the Dvorak Technique hinges on forecasters classifying the shape and pattern of clouds they observe on visible and infrared satellite imagery into four basic categories (which you should be sure to know):

1. Curved-band pattern: Often observed in the early stages of tropical cyclone development, this pattern is characterized by a band of dense cloudiness that begins to curve around the center of the storm. In weak hurricanes, the band coils entirely around the center of the storm. For example, Check out this infrared image that shows the curved-band pattern associated with Tropical Storm Jeanne at 1030Z on September 20, 2004. At the time, the maximum sustained winds were 60 miles per hour, and the curved band wrapped around most of the center of the storm.
2. Shear pattern: Typically observed in the formative stages of a tropical cyclone or during weakening, the shear pattern is characterized by deep convective clouds moving to one side of the storm's center. For example, check out this satellite image of a sheared Tropical Storm Nicholas at 1145Z, October 21, 2003. On this particular satellite image, low clouds are marked in yellow, while higher clouds are in bright whites and faint blue shadings. Note that the center of low-level circulation lies to the west of the deep convection, indicative of the relatively strong westerly shear between 850 mb and 200 mb. Recall that a tropical cyclone is in a weakened state when upper-level winds push deep convection away from the storm's low-level circulation.
3. Central Dense Overcast (CDO) pattern: The CDO pattern describes the region of dense cirrus clouds that shrouds the core of a tropical cyclone, which is sometimes observed in stronger tropical depressions, tropical storms, and weak hurricanes. For example, this satellite image showing the tropical-depression stage of Hurricane Alex at 1155Z on August 1, 2004 (at the time, the maximum sustained wind speed was 30 miles per hour) displays a CDO pattern. Prior to a tropical cyclone attaining a maximum sustained wind speed of 64 knots, the CDO appears fairly homogeneous (uniformly cold cloud-top temperatures on infrared imagery). In other words, no eye is readily apparent.
   
   I say "readily" here because an embryonic eye may have already "secretly" formed. As a tropical cyclone intensifies, an eye typically starts to develop near the center of the tightening spiral associated with the cyclone's primary curved band. But, the CDO typically masks most of this emerging pattern from the view of conventional satellite imagery (high cloud tops shield lower-level features from detection by visible and infrared imagery). Forecasters do have tools for detecting these "secret" eyes, which we'll explore later in the lesson, but forecasters continue to use the Dvorak CDO pattern until an eye appears on conventional satellite imagery.
4. Eye Pattern: Once an eye is evident on conventional satellite imagery, an "eye pattern" exists, although I should note that a large portion of the "CDO cloud" remains, as with this enhanced infrared image of Hurricane Emily from July 17, 2005. Clearly, the eye appears as an oasis of relative warmth within the cold CDO. Eye patterns can be somewhat subtle like the example from Emily to very obvious as in the case of Super Typhoon Haiyan.

Eye patterns can characterize tropical cyclones of widely varying intensities. For example, a storm that has an eye could be a Category 1 or a Category 5 hurricane. That's a huge difference, but both would fall under the eye pattern! To further help forecasters refine their assessments based on eye patterns, they look at specific characteristics of the eye. For example, recognizing the eye of a hurricane is banded helps meteorologists to better estimate the intensity of the storm as in this satellite image of Hurricane Jeanne at 1815Z on September 22, 2004. Essentially, a curved band had coiled entirely around the center of the storm (forming a "banded eye"), suggesting that it was a weak hurricane. Tropical forecasters also look at a specially enhanced infrared satellite image called a Dvorak image to help them distinguish between various eye patterns (see below). You can access the latest Dvorak imagery for storms around the globe, if you're interested. Forecasters use Dvorak imagery to determine the radiating temperature of the eye and compare it to the radiating temperatures of the surrounding cloud tops. As a general rule, the larger the difference in temperatures between the eye and the surrounding cloud tops, the stronger the hurricane.

After classifying the cloud pattern and looking at satellite-derived temperatures, forecasters completing the Dvorak Technique manually would take into account other factors such as trends in the cloud pattern that indicate a weakening or intensification and assign a T Number and CI Number, which range from 0 to 8 in increments of 0.5. Officially, T Numbers and CI Numbers appear in a coded format, which you may be interested in if you're into tracking tropical cyclones in real-time. But, how do these numbers translate to storm intensity? Below is a chart that links the estimated CI number with the basic patterns of clouds that I described above. Current Intensity Numbers have also been calibrated against aircraft reconnaissance of tropical cyclones in the Northwest Pacific and Atlantic Oceans. On average, the CI Numbers correspond to the specific wind speeds and central barometric pressures also shown in the graphic below.

In case you're wondering, the reason for the basin differences in central pressures at a fixed CI Number is that the overall mean sea-level pressures are lower in the Northwest Pacific (more details later in the course). So, given a central pressure and maximum sustained wind speed associated with an Atlantic tropical cyclone, the central pressure of storm in the Northwest Pacific must essentially be lower for it to generate the same wind speed. Remember, it's the pressure gradient that largely determines wind speed, which is why small tropical cyclones (such as Andrew in 1992) can generate stronger winds than a larger cyclone (such as Floyd in 1999) with the same minimum central pressure (see a comparison of these two hurricanes).

As you track tropical cyclones in real-time, you'll regularly see references to T Numbers and CI Numbers in discussions from NHC and JTWC. With what you now know about the Dvorak Technique, you should be able to interpret those references and understand what they suggest about a tropical cyclone's current status. The Dvorak Technique, however, is far from the only way that satellites are used in tropical cyclone forecasting. We'll explore another intriguing use of satellite data on the next page with a discussion of "Cloud-Drift Winds."

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As you've already seen, remote sensing from satellites can be used to estimate the intensity of a tropical cyclone via the Dvorak Technique. Indeed, several other satellite-based remote sensors help forecasters observe various aspects of a tropical cyclone's structure and intensity as well. We'll cover several more of these sensors as we continue through the lesson. In this section, I'm going to focus on a remote-sensing technique that has broader applications than just tropical cyclone analysis.

The same geostationary satellites used to execute the Dvorak Technique can also be used to remotely retrieve tropospheric wind information by calculating what are formally called atmospheric motion vectors (AMVs) -- you'll also sometimes hear AMVs referred to as cloud-drift winds (CDWs) or cloud-tracked winds. On the Web, you may encounter any of these phrases, so just realize that they refer to the same thing. For simplicity however, I'm going to stick with the term "cloud-drift winds" because it intuitively describes how this product is created. In a nutshell, this technique retrieves estimates of wind speeds and directions at various altitudes by tracking the movement of clouds on satellite loops. The process sounds pretty simple, but it can actually be quite challenging.

Before we really explore cloud-drift winds, I should point out that they rarely get a mention in any NHC discussions. So, what's the role of CDWs in tropical weather forecasting? Well, as you know, there's a serious dearth of routine radiosonde observations over the oceans, where the lack of data introduces errors into numerical simulations of the atmosphere. Thus, the capability of getting a proxy for winds by measuring how fast clouds drift over open seas is invaluable. With numerical weather prediction in mind, it should come as no surprise to you that CDWs are assimilated into computer models. Cloud-drift winds also have applications to aviation and mesoscale meteorology, and if you're interested, you can learn more about the utility of cloud-drift winds from this 2005 article published in the Bulletin of the American Meteorological Society (BAMS).

Cloud-drift winds are also used to help in assessing a tropical cyclone's wind field. For example, one of the remote observations that contributed to this H*Wind analysis of Hurricane Isabel from 0130Z on September 18, 2003 was "GOES from 0102 - 2202Z." Indeed, the technique of determining wind speeds from geostationary satellites via cloud-drift winds helped forecasters construct an estimate of Hurricane Isabel's wind field by tracking cloud movement over a period of 21 hours.

More commonly, cloud-drift wind data are displayed as in the image below, which shows wind barbs annotated on infrared satellite imagery over the Southeast Indian Ocean (if you need to get your geographical bearings on this image, please refer to this political map of the Indian Ocean). The various colors are described by the key in the upper-right corner of the image (green wind barbs are in the layer between 800 mb and 950mb; yellow = 600 - 799 mb; blue = 400 - 599 mb). For example, look off the west coast of Australia and note the closed, cyclonic circulation. Remember that this image is from the Southern Hemisphere, so "cyclonic" refers to a clockwise circulation. Using infrared imagery alone, the system would only appear as an innocuous blob of relatively low clouds, but the yellow barbs derived from cloud-drift winds showed that a closed circulation existed between 799 mb and 600 mb. These CDW observations helped forecasters not be fooled by the unremarkable blob of low clouds and refer to the system as "Tropical Cyclone 08S" (which was a tropical depression at the time).

If you're interested in accessing satellite images that include cloud-drift wind data, you can get the latest imagery from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) site (look for "Winds & Analyses" for any of the tropical basins). A variety of CDW products are available (they're not all based on infrared imagery), and now that you have a little background on CDWs and their applications, let's look at the technique of retrieving winds from various types of satellite imagery.

As its name suggests, cloud-drift winds are derived from a sequence of satellite images. In the simplest sense, you could spot a cloud and watch it move with time; but, as you can imagine, in reality, it's not really that straightforward. As an example, I'll describe the technique for retrieving winds in the lower to middle troposphere from infrared satellite data. First, the technique requires three successive images (the images are typically 30 minutes apart, but can be more frequent). Next, "target clouds" are selected according to brightness gradients (large gradients in brightness, for example, typically mark cloud edges). The pressure altitudes of the cloud targets are then estimated from the intensity of infrared radiation detected by the satellite.

An important caveat here is that the brightness gradients associated with candidate targets must remain relatively consistent in time, which means that not all clouds provide viable targets. To get a feel for what a sample of suitable cloud targets might look like, check out the dots on the infrared satellite image below. Indeed, multilayered clouds (decks of low, middle, and/or high clouds lying over the same geographical area) are eliminated as potential targets because trying to assign altitudes to multilayered clouds poses nightmarish challenges. So, ultimately, we can't accurately determine CDWs everywhere using infrared imagery because of these challenges, and the fact that some areas have no cloud cover at all.

Fortunately, we're not limited to infrared imagery for CDW observations. As you already know, loops of water-vapor images can be used to assess winds in the upper troposphere (even without using a formal CDW technique). For example, study this loop of water-vapor images of Hurricane Isabel that spans from 1415Z on September 13, 2003, to 1215Z on September 16 (the interval between successive images is two hours). For the most part, the high cloud-tops associated with Hurricane Isabel make up the prominent feature in the Atlantic Ocean (remember that high cloud-tops "contaminate" water-vapor images). Without high clouds to mark the upper-level steering winds along the path of Isabel, however, water-vapor imagery becomes indispensable because it allows us to follow "vapor targets" (like cloud targets on infrared imagery) in time. Following "vapor targets" allows us to deduce the speed and direction of upper-level winds over tropical seas. Indeed, by looking at the loop, you get the idea that Isabel encountered upper-level winds blowing from the west (noted in the first paragraph of this NHC discussion) that disrupted and weakened the westbound storm.

Forecasters use such qualitative approaches for assessing middle- and upper-tropospheric winds frequently when looking at water-vapor loops, but quantitative cloud-drift wind observations can be determined from water-vapor loops, too. The schemes used to generate middle- and upper-tropospheric winds from water-vapor loops are similar to the technique used to generate lower-altitude winds from infrared-satellite loops. For starters, three successive water-vapor images are required. From this loop, horizontal gradients in water-vapor (or high cloud tops) that remain coherent in time serve as potential "vapor targets." Note that even though many more "vapor targets" appear on this image compared to the image of cloud targets on infrared imagery above, many of the vapor targets end up being removed because of various quality control issues. After applying the same principles involved in retrieving CDWs from infrared imagery, out pops water-vapor images with middle to upper tropospheric winds -- just like the one below that shows upper-level winds from the west disrupting Hurricane Isabel at 09Z on September 16, 2003.

The different colors of the wind barbs correspond to their layers as described by the key at the top left of the image. It's clear that ahead of Isabel (to its west), winds above 500-mb were from the west and southwest, which disrupted the high-altitude circulation of the storm just as we noted from the water-vapor loop above. Note however, that color schemes for plotting various layers of cloud-drift winds can vary from Web site to Web site.

Cloud-drift winds aren't limited to just traditional infrared and water-vapor imagery, though. On the CIMSS Web site, there are products involving shortwave infrared and visible radiation that become available when active tropical cyclones exist. The upside to cloud-drift winds from visible imagery is that the higher resolution of visible imagery allows for smaller cumulus clouds to aid in tracking low-altitude winds. Cloud-drift winds based on shortwave infrared and visible wavelengths can help forecasters get a glimpse of the low-level wind fields in and around tropical cyclones, and can be empirically adjusted to the surface to estimate the surface wind field of a tropical cyclone (recall the contribution from CDWs to the H*Wind analysis toward the beginning of the page?).

In the final analysis, assessing CDWs at a variety of wavelengths (corresponding to those that create water-vapor and infrared imagery, as well as shortwave infrared and visible imagery if available) gives forecasters a more complete picture of winds throughout the depth of the troposphere. Such a "multi-channel" approach was essential for assigning heights to potential cloud targets. But, the utility of the multi-channel approach in satellite remote sensing has broader applications, which we're about to investigate.

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So far, the satellite remote sensing techniques that we've covered have originated from geostationary satellite data. For example, geostationary satellites effectively have the "market cornered" on retrieving cloud-drift winds because satellites in geostationary orbit have a fixed view. That fixed view makes the creation of satellite loops, from which CDWs are retrieved, feasible. Polar-orbiting satellites, however, do not have a fixed view, and without the ability to create loops of imagery from polar orbiters, they're not useful for retrieving CDWs.

Nonetheless, polar orbiters play a pivotal role in the remote sensing of tropical weather systems. You may recall that polar orbiters fly at much lower altitudes than geostationary satellites and are "sun synchronous" (meaning that they ascend or descend over a given point on the Earth's surface at approximately the same time each day). Multiple fleets of polar orbiting satellites currently circle the Earth (more in the Explore Further section below), and, like GOES, they provide multispectral (or "multi-channel" -- using multiple wavelengths of the electromagnetic spectrum) scans of the Earth and the atmosphere. Over the next few sections we'll explore the multispectral capabilities of polar orbiters and the roles that remote sensing by these satellites play in tropical weather analysis and forecasting.

Among the key instruments aboard these polar orbiters are the Advanced Very High Resolution Radiometer (AVHRR), and the Visibile Infrared Imaging Radiometer Suite (VIIRS). These instruments collect data at multiple wavelengths ("channels") across the visibile and infrared portions of the electromagnetic spectrum, which allows us to collect information about day and nighttime cloud cover, snow and ice coverage, sea-surface temperatures, and land-water boundaries. If you're interested in learning more about the details of these instruments or their applications, you can read more about the AVHRR and VIIRS. In case you're wondering about the reference to "very high resolution" in AVHRR, you may be interested in exploring the topic of resolution more in the Explore Further section below.

These instruments' broad capabilities for detecting clouds during the day and at night come from the fact that they scan at multiple wavelengths across the visible and infrared portions of the electromagnetic spectrum. Radiation detected across several channels can be combined to create composite images that provide additional information to weather forecasters. One product that has long been common in tropical forecasting is a three-channel color composite like the one below of Hurricane Katrina captured by NOAA-16's AVHRR at 2011Z on August, 28 2005.

The yellowish appearance of Katrina's eye really stands out, doesn't it? That yellowish shading corresponds to low-topped, relatively warm clouds within Katrina's eye (remember that the eye of a hurricane often contains low clouds). Meanwhile the thick, tall convective clouds with cold tops surrounding the eye appear bright-white on this three-channel color composite, and high, thin cirrus clouds appear blue/white.

In terms of tropical forecasting, multispectral satellite images of tropical cyclones can sometimes be very helpful in assessing the tendency of a system's intensity. Focus your attention on the daytime, three-channel color composite of Tropical Cyclone Heta (07P) at approximately 00Z on January 8, 2004 (on the left below). At the time, Heta's maximum sustained winds were 35 knots, with gusts to 45 knots. Thus, Heta had minimum tropical-storm strength as it weakened over the South Pacific Ocean.

The fact that the clouds near Heta's center appear yellowish on this image indicates low clouds and a lack of deep convection near its core. Without organized deep convection near its core, Heta was in a sorry state indeed since there was no catalyst for deep (but gentle) subsidence over its center. Thus, Heta could not maintain formidable strength. However, just two days earlier on January 6, 2004, (image above right) deep convection surrounded the eye of Tropical Cyclone Heta as evidenced by the very bright white clouds near the eye. At the time, Heta had maximum sustained winds of 125 knots.

Furthermore, when a tropical cyclone is highly sheared, the color scheme of three-channel color composites can really expose the structure of the storm. For example, check out this loop of three-channel color composite images of Tropical Depression 8 in the Atlantic from August 28, 2016. Not long after the storm was classified as a depression, the deep convective clouds (bright white) got displaced to the northwest thanks to strong southeasterly wind shear. The yellow swirl of clouds left behind clearly marks the storm's low-level circulation. It was obviously not a "healthy" storm at this time.

How does it work?

How are these useful three-channel color composites created? The process really isn't too complex, and is outlined by the graphics below. We'll use the details of the AVHRR for our example. First, we start with standard grayscale visible (channel 1), near (or "shortwave") infrared (channel 2), and infrared (channel 4) images (check out the top row of satellite images in the graphic below). Next, we apply a red filter to the visible image, a green filter to the near-infrared image, and a blue filter to the infrared image, and we get strange looking satellite images like the ones in the second row of the graphic below. But, if we combine those "false-color" images together, we get a three-channel color composite!

Breaking down this three-channel color composite helps us to understand why high, thin clouds appear in blue on the final product -- they're brightest on the infrared channel (which had blue hues added to it). Meanwhile, tall, thick convective clouds that show up bright white on the final product are bright on the individual images from all three channels, and low clouds appear yellow because they're brightest on the visible (red) and near-infrared (green) images. The combination of green and red provides the yellow shading (if you're unfamiliar with why yellow results, you may want to read about additive color models if you're curious).

Similar satellite composites can be created from data collected by geostationary satellites, too, by adding red and green filters to visible imagery and a blue filter to infrared imagery. I should add that the number of multispectral products available from satellites is increasing as satellite technology has improved, allowing for data collection via more channels (wavelengths). Not all multispectral satellite products use the same color scheme demonstrated on this page, however, so keep that in mind before attempting to interpret images you may encounter online. In case you're wondering, the false-color approach of multispectral images has a number of other applications. The Hubble and James Webb Space Telescopes employ a similar approach, as do polar-orbiting satellites that study features on the Earth's surface, such as these before and after images of the Texas Coast surrounding the landfall of Hurricane Ike (2008). Meanwhile, if you're interested in looking at images of past hurricanes, Johns Hopkins University has a spectacular archive of three-channel color composites.

There's no doubt that this multispectral approach to satellite imagery can produce some striking and very insightful images, but the uses of multiple wavelengths of electromagnetic radiation don't stop with three-channel color composites. It turns out that other remote sensing equipment aboard polar-orbiting satellites can detect things like rainfall rates, temperatures, and wind speeds by employing different wavelengths of radiation. We'll begin our investigation of those topics in the next section.

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Our studies of remote sensing from satellites so far have mostly focused on techniques and products that are based on conventional satellite imagery. Even multi-spectral images are merely created by using various wavelengths used to create visible and infrared images. Now, however, we're going to transition into some more sophisticated applications of remote sensing from satellites. In this section, I'm going to focus on satellite-based detection of precipitation structures and rates. Satellites play a crucial role in this area because tropical cyclones spend so much time outside of the range of land-based radar networks. First, we'll investigate imagery created from satellite detection of microwave radiation between 85 GHz and 91 GHz.

85-91-GHz Imagery

One of the characteristics that you've learned about a tropical cyclone's eye is that it is generally rain free, but it is not often completely cloud free. Either some low clouds exist in the eye and/or high clouds obscure the presence of the eye altogether on conventional satellite imagery. For example, check out the enhanced infrared satellite image at 15Z on September 1, 2009 (below), which shows Hurricane Jimena near the southern tip of Baja California. At the time, Jimena had maximum sustained wind speeds of 130 knots, and a central pressure of 933 mb. Given these data, you might suspect that Jimena would display a well-defined eye on conventional satellite imagery. But, alas, high clouds almost completely obscured Jimena's eye, and it would be tough to get a fix on the storm's center under these circumstances.

Even though enhanced IR imagery didn't provide a good look at Jimena's core structure, thanks to passive microwave imagery utilizing single frequencies between 85 GHz and 91 GHz, forecasters could still see that Jimena had an eye. Such imagery comes from passive microwave sensors like the Special Sensor Microwave Imager (SSMI), the Special Sensor Microwave Imager / Sounder (SSMI/S), and the Advanced Microwave Scanning Radiometer (AMSR). These instruments are mounted aboard the polar-orbiting satellites you may have encountered in the previous section. Feel free to explore these links if you're interested in learning more about these sensors.

The image below shows data collected by the SSMI/S mounted aboard the U.S. Air Force Defense Satellite, F-16. This particular image was created using 91-GHz microwave radiation, and note that Jimena's eye now shows up much more clearly than on the enhanced IR image above. Detecting the high-level structure of the core of tropical cyclones is a primary use of 85-91-GHz imagery because at the wavelengths used to create these images, we can "see" right through high-altitude cirrus clouds into the eye.

So, how should we interpret this image? Why is Jimena's eye evident on this image, but not the enhanced infrared image? After all, both images are plotting the same variable, called "brightness temperature," which is the temperature of a hypothetical object that absorbs all radiation that strikes it (brightness temperature is also sometimes referred to as "equivalent blackbody temperature"). But, because the two images are utilizing different wavelengths (frequencies) of radiation, they're showing us different things. The 91-GHz image doesn't really show us high, cold cloud tops like conventional infrared imagery does.

Focusing on the spiraling pattern of low brightness temperatures associated with Hurricane Jimena (red, green and yellow), it stands to reason that not much 91-GHz radiation was reaching the satellite at this time. To better understand why, check out the schematic below which outlines the plight of 91-GHz radiation emitted upward from the ocean, raindrops, and cloud droplets. To summarize, 91-GHz radiation weakly upwelling from the ocean surface gets mostly absorbed or scattered away by raindrops and cloud droplets below the freezing level in a tall thunderstorm (assume that the storm developed in the eye wall or spiral rain band of a hurricane). Raindrops and cloud droplets also emit some 91-GHz radiation upward. This upwelling 91-GHz radiation from the top of the "rain layer" is primarily what reaches the satellite, but not before it gets scattered and absorbed above the freezing level by precipitation-sized ice particles like hail and graupel. Higher up in the storm, tiny ice crystals in cirrus clouds are virtually transparent to 91-GHz radiation (it's transmitted through the tiny ice crystals), but the damage has already been done. Without reservation, the 91-GHZ signal reaching the satellite in a tall thunderstorm is very weak indeed.

The weak 91-GHz radiation reaching the satellite correlates to very low brightness temperatures. So, when we see very low brightness temperatures on 85-91-GHz imagery, we're really seeing the signature of deep convection (characterized by the areas where the signal from 91-GHz radiation has been weakened the most by large ice particles like hail and graupel). For practical purposes, this trait of 85-91 GHz imagery:

• allows forecasters to see the eye of a hurricane that's shrouded by high clouds
• allows forecasters to assess the structure of hurricanes over remote seas by revealing the patterns of deep, moist convection in the storm's eye wall and outer rain bands

For the record, a few "twists" on 85-91-GHz images actually exist. Scientists have made some tweaks to the basic product in order to make it more useful. If you're interested in reading about these "twists," check out the upcoming Explore Further page.

One of the major limitations of 85-91-GHz imagery is that one of the several satellites equipped with a microwave sensor passes over a tropical cyclone, on average, every four to five hours (time lags can be as brief as 30 minutes or as protracted as 25 hours). So, there can be long gaps between data for any tropical cyclone. Researchers at the University of Wisconsin devised a creative technique to fill in the time gaps with morphed 85-91-GHz images. The product is called MIMIC (Morphed Integrated Microwave Imagery at CIMSS) and it can be very helpful for assessing changes to the structure of a tropical cyclone's core structure (and thus, its intensity). For example, check out this MIMIC loop of Hurricane Ike as it made landfall on the upper Texas Coast on September 13, 2008. The loop really shows the breakdown of Ike's eye wall (the partial ring of yellows and oranges) after landfall. Pretty cool, eh? If you really enjoy following tropical cyclones in real-time, I highly recommend keeping an eye on the recent MIMIC loops posted on the CIMSS site.

36-37-GHz Imagery

While 85-91-GHz imagery is useful for identifying areas of deep convection within a tropical cyclone, it's not particularly useful at looking at the low-altitude structure of a storm because of the impacts that the large ice particles above the freezing level have on upwelling 85-91-GHz radiation. To get a better view of the low-level structure of a tropical cyclone, forecasters turn to imagery based on 36-37-GHz radiation, which works much like 85-91-GHz imagery, with one key difference. The 36-37-GHz radiation that upwells from the top of the "rain layer" is not scattered and absorbed by large ice particles or tiny ice crystals above the freezing level (here's a visual schematic outlining the process).

As a result, brightness temperatures are higher because the passive microwave sensor aboard the satellite detects a relatively large portion of the upwelling 36-37-GHz radiation from its source -- raindrops below the freezing level. And, because the majority of the radiation from lower altitudes reaches the satellite, 36-37-GHz imagery gives forecasters a better sense of the overall low-level structure of tropical cyclones. For example, we can see the signature of the small eye of Hurricane Wilma from this 36-GHz image from 1845Z on October 20, 2005. This utility of 36-37-GHz imagery also makes it a better choice than 85-91-GHz imagery for pinpointing a tropical cyclone's center. For a more in-depth explanation of this advantage of 36-37-GHz imagery, check out upcoming Explore Further page.

Before moving on, however, I want to point out that forecasters can use 36-37-GHz imagery in tandem with 85-91-GHz imagery to assess the vertical structure of tropical cyclones. Since 36-37-GHz imagery gives a better look at the low-level structure, and 85-91-GHz imagery gives a better look at the high-level structure, forecasters can compare the locations of the low-altitude center and high-altitude center to see if the center of the storm tilts with increasing height. If the center notably tilts with height, that's often a sign that the storm isn't healthy and may be hindered by strong vertical wind shear.

Quantitative Precipitation Estimates

While 85-91-GHz and 36-37-GHz imagery do a good job of showing us the overall precipitation structure of a tropical cyclone (by highlighting deep convection and the details of the low-level rain layer, respectively), they don't quantitatively indicate rainfall rates. Remote sensing from satellites can help with that, too, as the rainfall estimates in the image below (in millimeters) suggest. The data in the image were collected from the Tropical Rainfall Measuring Mission (TRMM) satellite as Hurricane Dolly approached the southern Texas coast from July 20 - 25, 2008.

TRMM was launched in 1997 through a partnership between NASA and the Japan Aerospace Exploration Agency, and its launch revolutionized precipitation detection from satellites. Given that "tropical" is part of its name, the satellite's focus on low latitudes should be no surprise. TRMM's orbit ranged from 35 degrees North to 35 degrees South (basically covering the tropics and subtropics) as illustrated by this artist's rendition of TRMM's orbital path.

TRMM contained five instruments, but the last of them became inoperable in April, 2015. I'll still briely describe TRMM's two instruments for measuring rain rate since they're basically the prototypes for instruments aboard other satellite missions: TRMM's active remote sensor--Precipitation Radar (PR), and TRMM's passive microwave sensor--TMI (TRMM Microwave Imager). I'll only provide a quick summary, but you're welcome to read more about their capabilities and limitations if you would like (PR overview; TMI overview).

TRMM PR was the first space-borne instrument designed to provide the three-dimensional structure of storms. PR transmitted pulses of microwave radiation and waited for return signals, much like a ground-based radar. TRMM PR's main uses were depicting vertical rain structure, surface rain-rate, and it could discriminate between convective and stratiform rain.

Meanwhile the TMI carefully measured weak microwave energy naturally emitted by the Earth and the atmosphere and used it to infer rainfall rates. What makes the TMI different from 85-91-GHz imagery and 36-37-GHz imagery (which do not quantitatively estimate precipitation)? TMI's use of multiple frequencies (10.7, 19.4, 21.3, 37, and 85.5 GHz) allowed for the quantitative estimation of rainfall rates. Imagery generated using a single frequency between 85-91-GHz or 36-37-GHz can't display precipitation rates. While TMI had a broader scanning swath than PR, it also collected data at a lower resolution, so the bottom line here is that TMI provided an estimate of surface rain across a broad swath, and coarse information on the vertical structure of rain. PR, meanwhile, provided a narrower footprint but higher 3-D resolution. To see the trade-off between the data collected by these two instruments, check out the annotated image below.

The image above is the PR / TMI image of Hurricane Wilma at 1740Z on October 19, 2005. Rain rates are expressed in inches per hour. Note that the PR / TMI data were superimposed on the 1615Z visible satellite data from GOES-12. The wider swath, bounded by the two thicker yellow lines, corresponds to the data collected by TMI. The narrower swath, bounded by the two thinner yellow lines corresponds to PR data. Note that the PR data, which cuts through the rain bands north of Wilma's core, is much more detailed compared to the TMI data. But, the PR scan completely missed Wilma's core. On the other hand, the TMI data is less detailed, but has wider coverage.

More recently, another precipitation-measuring satellite mission, the Global Precipitation Measurement (GPM) was launched in 2014 to expand upon TRMM's substantial legacy. One main difference between the two missions is that GPM has nearly global coverage, as its name implies, so it provides data at higher latitudes than TRMM. Like TRMM, GPM includes a precipitation radar (active sensor) and a passive microwave imager.

The GPM's precipitation radar is the first satellite-based dual-frequency precipitation radar (its acronym is "DPR" for this reason--the "D" stands for "dual"). The dual-frequency nature of DPR makes it more sensitive to areas of light precipitation and snow compared to TRMM PR. Meanwhile, the GPM Microwave Imager (GMI) works much like TMI, except that it utilizes more channels and has a higher resolution. As with the instruments on TRMM, DPR's scanning swath is narrower than GMI's (although both are slightly larger than their TRMM predecessors). If you're interested in learning more details about these key instruments aboard the GPM satellite, feel free to read more (DPR overview; GMI overview).

Now that you're familiar with satellite-based qualitative and quantitative looks at precipitation within tropical cyclones, you might be wondering, "where can I access all of this data?" For more on data resources and some of the products available, check out the Explore Further page that follows. Otherwise, we'll stick with the theme of remote sensing using microwaves and explore a special microwave sounder (a sounder provides a vertical profile of a meteorological variable) used in tropical forecasting.

Read on.

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Our tour of remote sensing instruments aboard satellites will now focus on the Advanced Microwave Sounding Unit (AMSU), which is a sophisticated instrument carried on some satellites in NOAA's fleet that can be used to remotely estimate the strength of tropical cyclones. These estimates can be particularly helpful when storms swirl outside of the range of reconnaissance aircraft, as Hurricane Isabel did at 11 A.M. on September 8, 2003 (Isabel was located over the eastern Atlantic at the time). Even without aircraft reconnaissance, however, forecasters were able to use data from the AMSU (along with some of the other remote sensing techniques we've discussed) to help gauge Isabel's intensity, as the 11 A.M. discussion indicates:

ZCZC MIATCDAT3 ALL
    TTAA00 KNHC DDHHMM
    HURRICANE ISABEL DISCUSSION NUMBER  10
    NWS TPC/NATIONAL HURRICANE CENTER MIAMI FL
    11 AM EDT MON SEP 08 2003
 
    ISABEL HAS CONTINUED TO RAPIDLY INTENSIFY. THE INITIAL INTENSITY IS
    INCREASED TO 100 KT BASED ON SATELLITE INTENSITY ESTIMATES OF 115
    KT FROM TAFB AND AFWA...102 KT FROM SAB...AND 102 KT/T5.5 3-HOUR
    OBJECTIVE DVORAK INTENSITY ESTIMATES. THE 100 KT INITIAL INTENSITY
    IS ALSO CONSISTENT WITH THE LATEST AMSU INTENSITY ESTIMATES OF 100
    KT AND 960 MB.

Each AMSU unit consists of passive radiometers that sense microwave radiation emitted from the earth and atmosphere, and has two components -- AMSU-A and AMSU-B. The AMSU-B unit is primarily dedicated to detecting humidity profiles and liquid-water and ice profiles within atmospheric columns. I won't get into any more details about AMSU-B here, but you can feel free to study this overview of AMSU-B if you wish. Instead, our focus here is going to be the AMSU-A unit, which is primarily devoted to determining vertical profiles of temperature in the atmosphere.

The AMSU-A unit consists of two independent instruments (AMSU-A1 and AMSU-A2). As a whole, the AMSU-A unit detects microwave emissions at 15 different microwave wavelengths (frequencies). The AMSU-A1 module uses two antenna-radiometer systems to provide 12 channels in the 50- to 60-GHz band (0.50 cm to 0.60 cm in wavelength) for retrieving the atmospheric temperature profile from the Earth's surface to about 42 kilometers (or 2 mb, which lies near the "top" of the atmosphere). The other AMSU-A1 channel and the two AMSU-A2 channels provide forecasters with rain rate, sea ice concentration, and snow cover, but our focus here is on temperature profiles. By and large, each of the 12 AMSU-A1 channels are "tuned" to specific atmospheric layers. Having the capability to estimate temperatures in specific layers of the atmosphere is pivotal for getting a handle on the high-altitude warming above the core of a developing tropical cyclone, which is the primary use of AMSU-A data.

For example, check out the cross section of AMSU-derived temperature anomalies (below) through Hurricane Floyd at 2332Z on September 11, 1999. The anomalies were calculated by comparing AMSU-derived temperatures inside the storm with those outside the storm (the "storm environment"). The warming in the eye can be correlated to a reasonable estimate for minimum surface pressure (warming decreases mean column density, which results in a decrease in column weight, which, in turn, is closely related to surface pressure). It sounds simple, but deriving these temperatures is actually fairly complicated (more details coming shortly).

The deepest orange and red shadings represent the largest positive temperature anomalies (the warmest air compared to the storm environment), which appear to be in the middle and upper troposphere. Meanwhile, note the large cool anomalies that appear in the lower troposphere on the cross section through Hurricane Floyd. The two symmetric anomalies on either side of Floyd's eye correspond to the stormy eye wall and the other anomaly (to the "left" of the eye) likely coincides with a thunderstorm in a spiral band coiling inward toward the eye. Without mincing words, you should disregard these large cool anomalies because they are phony. Indeed, heavy rain in the eye wall and spiral-band thunderstorms grossly attenuates microwaves from the AMSU instrument (raindrops scatter and absorb microwaves), causing unrealistically weak upwelling that is accidentally interpreted as a large cool anomaly. So don't believe it! The attenuation of microwaves by heavy rain is one of the limitations of these kinds of remote sensors.

We can see how the vertical structure of temperature anomalies changes within a storm by investigating these interactive cross sections of Hurricane Erin at 1739Z on September 10, 2001. In the image on the left, click and drag the blue line to view various cross sections throughout the storm (on the right). Keep in mind that all of these cross sections were created at the same time; they simply represent different slices through the storm. As you drag the blue line closer to Erin's eye, note the dramatic warming over Erin's core (in deep red). Clearly, there is a connection between the magnitude of the compressional warming high above the core of Erin and the low central pressure at the ocean surface (and, thus, the powerful surface winds around the periphery of the eye).

In addition to viewing cross sections of tropical cyclones, we can also view data from individual AMSU-A1 channels to identify temperature anomalies near single pressure altitudes. Forecasters commonly monitor four specific channels that allow them to evaluate temperature in the upper half of the troposphere and lower stratosphere

• Channel 8 (55.5 GHz) approximately 100mb (about 15 kilometers)
• Channel 7 (54.9 GHz) approximately 200mb (about 12 kilometers)
• Channel 6 (54.5 GHz) approximately 350mb (about 10 kilometers)
• Channel 5 (53.6 GHz) approximately 550mb (about 5 kilometers)

For example, the image below shows the Channel 5 - 8 images from Hurricane Fabian from 02Z on September 5, 2003. The warm core of Fabian really stands out, especially on channels 6 and 7 (350 mb and 200 mb, respectively), marked by yellows, oranges, and reds.

Historically, the maximum warming over the eye of a hurricane was thought to occur near 200 mb, and it does appear there often on AMSU-A1 images. Therefore, channel 7 is closely monitored. However, more recent research suggests that instruments like the AMSU have insufficient vertical resolution to truly pinpoint the exact altitude of the maximum warm anomaly. In fact, the maximum warm anomaly may meander between the middle and upper troposphere at various times during the storm's life cycle and be located more frequently toward the middle troposphere. So, monitoring channels 5-8 is prudent to keep an eye on the entire upper half of the troposphere (and lower stratosphere).

Now that you've seen what kinds of data we can get from the AMSU-A1 unit, and how to interpret it, we'll get into how it works a bit more. By the way, if you're interested in finding out where you can access AMSU images like the ones shown on this page for current and past storms, check out the AMSU page at the Cooperative Institute for Meteorological Satellite Studies (CIMSS).

How does it work?

As I mentioned before, each of the 12 AMSU-A1 channels are "tuned" to measure brightness temperatures in specific atmospheric layers. Recall that brightness temperature (also known as "equivalent black-body temperature") is the temperature of a hypothetical object that absorbs all radiation that strikes it. Having the capability to estimate brightness temperatures in specific layers of the atmosphere is the key for assessing the high-altitude warming above the core of a developing tropical cyclone. But, how does the AMSU-A1 unit assign brightness temperatures to specific atmospheric layers? We've encountered a similar problem before, when we discussed the complicated methods of assigning altitudes to water vapor targets in order to derive cloud drift winds. That problem was particularly complex because vertical profiles of water vapor vary in time and space across the globe.

The AMSU-A1 unit, however, remotely senses microwave radiation emitted by molecular oxygen. That's a big deal because unlike water vapor, the decrease in the concentrations of molecular oxygen with increasing altitude is roughly the same at any place and at any time. Moreover, the presence of clouds does not meaningfully interfere with microwave emissions from molecular oxygen reaching the satellite. The bottom line here is that we know how oxygen is distributed in the atmosphere. And, this knowledge is the basis for how we can assign specific altitudes to brightness temperatures measured at microwave frequencies with the AMSU-A1 unit.

Between 50 GHz and 60 GHz (the microwave band that the key AMSU-A1 channels cover), molecular oxygen absorbs strongly at some frequencies, but not as strongly at other frequencies. For example, let's look at channels 3 and 7. Molecular oxygen weakly absorbs microwave radiation at a frequency of 50.3 GHz (channel 3), and virtually passes through the atmosphere without much absorption (see graph on the left below). As a result, the greatest contribution to upwelling microwave radiation at 50.3 GHz that reaches the satellite comes from the earth's surface (see graph on the right below).

Meanwhile, at a frequency of 54.9 GHz (channel 7), molecular oxygen much more strongly absorbs microwave radiation. This means that microwave emissions from the ground at 54.9 GHz do not reach the satellite because this radiation is absorbed by molecular oxygen higher up. Nor do microwave emissions (at 54.9 GHz) from oxygen in the low-to-middle troposphere ever reach the satellite. In the final analysis, microwave emissions from molecular oxygen at approximately 200 mb (about 12 kilometers) provide the greatest contribution to upwelling radiation that reaches the satellite at this frequency.

Given the 12 channels on the AMSU-A1 unit, it's not difficult to imagine that it can generate a temperature profile through virtually the entire atmosphere. If you're interested in knowing the specific level of maximum contribution to upwelling microwave radiation for each AMSU-A1 channel, check out this graph of weighting functions. In simplest terms, you can think of a weighting function as the level of maximum contribution to upwelling microwave radiation that reaches the satellite at the given channel's frequency.

Now that we've covered the AMSU and its ability to detect vertical temperature profiles, we have one more stop on our tour of remote sensing from satellites. Up next, we'll be looking at the remote sensing of surface winds from space using scatterometry. Read on.

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Of all of the remote sensing instruments you've studied in this lesson, scatterometers are unique because they have the ability to remotely measure surface wind speed and direction over water. For the record, a scatterometer is a high-frequency radar ("high" compared to the standard network of ground-based Doppler radars, which are "S-Band radars"). So, a scatterometer is an active remote sensor--it emits pulses of microwave radiation and measures the radiation that backscatters to the unit, similar to standard weather radar.

A number of scatterometers have been mounted on polar-orbiting satellites and have made key contributions to tropical forecasting since the 1990's. Their ability to measure wind speed and direction give forecasters valuable data about tropical cyclones forming and developing over remote seas. For example, even though Tropical Storm Isabel (eventually Hurricane Isabel) was well outside the range of aircraft reconnaissance, forecasters at NHC used scatterometry data to classify the storm, as noted in their 5 P.M. discussion from September 6, 2003.

In a nutshell, scatterometers transmit pulses of microwaves with relatively short wavelengths (relatively high frequencies) and measure the backscatter from the wind-roughened ocean. The faster the winds are, the rougher the ocean surface, and the more radiation that backscatters to the scatterometer. In turn, meteorologists correlate backscattered microwave energy to wind speed and direction. As you'd expect, actually determining wind speeds and directions is a complex, imperfect process, but we'll explore those issues in a bit.

Before we get into the interpretation of scatterometry data and a deeper discussion of how scatterometry works, I want to quickly elaborate on the relevance of scatterometry. From a forecasting perspective, scatterometry gives forecasters the ability to detect tropical cyclones in their earliest stages of development. Early detection is important, of course, because it affords the general public and maritime interests greater lead time to prepare for any eventual threat. Scatterometry can detect centers of wind circulations that have the potential to develop into tropical cyclones many hours in advance of their attaining formal status as a tropical depression.

For example, the graph on the left below shows how indispensable scatterometry can be. The data apply to the 2001 hurricane season over the Atlantic basin and the vertical axis on the graph represents the number of lead-time hours provided by scatterometry. Using scatterometer winds, researchers forensically identified potential tropical cyclones an average of 43 hours before forecasters at the National Hurricane Center had formally classified the systems as tropical depressions. The equally impressive results for the eastern Pacific basin in 2001 are shown in the figure on the right below.

Identifying potential tropical cyclones from scatterometer data involves the detection of low-level relative vorticity (recall that vorticity is a measure of "spin") associated with a developing cyclonic circulation of winds. For example, check out the low-level cyclonic vorticity derived from scatterometer data at 11Z on September 1, 2001, that indicated the potential for a tropical cyclone to form. As it turned out, this low-level circulation served as the seed for Hurricane Gabrielle.

Interpreting Scatterometry Data

What does scatterometry data look like? Once the data have been processed by computers, the output looks something like the image below, which shows data from the QuikSCAT scatterometer on September 11, 2008. Notice a few important things. First, there's no scatterometry data over land (remember scatterometers measure backscattered radiation from ocean waves, so that makes sense). Secondly, there's a notable swath of missing data that extends south-southeast from the Carolina coast. Like the other sensors mounted aboard polar orbiting satellites, coverage gaps exist in the data (the satellite's "view" on any one pass is only so wide, so some areas naturally get missed). Finally, the surface wind barbs on this particular image show a large cyclonic swirl over the Gulf of Mexico, which corresponded with Hurricane Ike.

Note that most of the wind barbs near Ike's center are black and show very high speeds, which doesn't make sense with the color code used on the graphic (black represents speeds five knots or less). Furthermore, the circulation is hardly neat and tidy. It turns out that these black wind barbs have a special meaning -- they indicate that the data are unreliable. This "black flag" convention isn't universal, however. Some Web sites use other symbols to indicate unreliable data (a gray dot at the base of a wind barb is another common indicator). Scatterometers have trouble collecting good data in areas of heavy rain because raindrops severely attenuate microwave radiation, which weakens the signal received at the satellite. In addition, heavy rain splashing down on the ocean surface alters the small-scale structure of the surface ocean waves, which changes the nature of the backscattering to the satellite. Ignoring the unreliable "rain-contaminated" data on this particular image, it suggests that Ike's maximum surface wind speed was only about 50 knots (an underestimate since Ike was a hurricane).

This image provides a good example of why scatterometry data is primarily used to identify cyclonic circulations in embryonic tropical cyclones. Because heavy rain can prevent scatterometers from accurately discerning wind direction and speed, they typically don't provide useful data near the center of stronger tropical cyclones (because that's where lots of heavy rain falls in eye wall thunderstorms). So, scatterometry is generally not a good way to assess the intensity of a strong tropical cyclone. In weaker tropical systems, fewer organized areas of heavy rain exist, which yields a more useful data set.

You should also note that scatterometry has applications beyond the tropics, such as identifying sea ice in polar regions. Glacial snow and ice very effectively backscatter microwaves to the scatterometer (more effectively than even wind-roughened oceans), which allows scientists to identify boundaries of sea ice from their strong return echoes.

Characteristics and Limitations

Rain contamination isn't the only limitation of scatterometry data, however. A number of scatterometers have provided useful data in recent decades, and each one was a bit different. Therefore, each had its own unique set of characteristics and limitations. I'm going to briefly summarize the main characteristics and limitations of some significant scatterometers since you may encounter data from them if you're exploring current or past tropical cyclones online.

• QuikSCAT (operational 1999 - 2009): The SeaWinds Scatterometer aboard the QuikSCAT satellite was a "K_u-Band" radar, which transmitted microwave energy at a frequency of 13.4 GHz. The use of this frequency had a couple of important consequences. First, QuikSCAT had a relatively high resolution (about 12 kilometers), but it was also highly sensitive to areas of precipitation (which led to more rain contaminated data). Like data from any polar orbiting satellite, gaps in coverage existed, but QuikSCAT did "view" the earth in relatively wide 1800-km swaths. If you're interested, you can read more about the QuikSCAT mission.
• ASCAT (operational 2006 - current): The Advanced SCATerometers are mounted aboard Europe's Metop satellites. Each is a "C-Band" radar, which transmits microwave energy at lower frequencies (longer wavelengths) than QuikSCAT (5.255 GHz, to be exact). The use of lower frequencies means that ASCAT's resolution (about 25 kilometers) is reduced compared to QuikSCAT; however, ASCAT is a bit less sensitive to attenuation in areas of heavy rain (although rain contamination isn't eliminated entirely). Despite a reduced sensitivity to heavy precipitation, ASCAT does have a documented low bias when wind speeds are high (especially higher than 20 meters per second, or 39 knots). ASCAT passes have larger coverage gaps since it views the earth differently than QuikSCAT. ASCAT views the earth in two parallel swaths 550 kilometers wide, with a nadir (the point on the earth directly beneath the satellite) gap of about 700 kilometers between them. The bottom line is that each ASCAT unit only sees roughly 60% of what QuikSCAT saw, but having more than one ASCAT unit orbiting the earth helps to compensate. Feel free to read more about the ASCAT mission, if you're interested.

Each scatterometer passes over a region twice per day (one "ascending" pass and one "descending" pass), and to gain a better understanding of the differences in coverage for a single QuikSCAT and ASCAT pass, check out the image below, which shows a coverage comparison between the "ascending pass" of QuikSCAT (right) and the "descending pass" of ASCAT (left). The superior spatial coverage of QuikSCAT is obvious, and note that the coverage gaps of both scatterometers are maximized at the equator, get smaller in the middle latitudes, and are eliminated entirely near the poles (which doesn't really help tropical forecasters).

A few other scatterometers have made important contributions to tropical cyclone forecasting:

• OSCAT / SCATSat: The Oceansat-2 SCATtereometer was part of a mission launched by the India Space Research Organization (ISRO) / Space Applications Center (SAC). Operationally, OSCAT was very similar to QuikSCAT in its capabilities and limitations, but only 4.5 years after its launch in 2009, OSCAT became inoperable due to a technical malfunction. It's initial replacement (SCATSat) became operational in 2016.
• ISS-RapidScat (operational 2014 - 2016 ): The RapidScat instrument was NASA's formal replacement for QuikSCAT, and was very similar to QuikSCAT in its instrumentation (it's also a K_u-Band radar, which is highly sensitive to rain contamination). Of note, RapidScat flew aboard the International Space Station (hence the "ISS" in its name). One key difference was that ISS-RapidScat has an orbital altitude only about half that of QuikSCAT, which resulted in a narrower viewing swath of earth (only about 1100 km). You're welcome to read more about the ISS-RapidScat Mission, if you're interested.

You may encounter data from any of these scatterometers or others (China and France have launched satellites with scatterometers aboard, too, for example) when looking at past or current tropical cyclones online, so it's important that you understand their basic characteristics and limitations (particularly with respect to problems in areas of heavy rain and any established biases in wind data). If you're interested in viewing scatterometry data for current or past storms, check out the links in the Explore Further section below.

Multiplatform Satellite Surface Wind Analyses

Despite the fact that scatterometry doesn't provide much help in assessing the maximum winds in a strong tropical cyclone, it can help meteorologists construct the overall wind field of a particular storm. Scatterometry contributes to a product called a "Multiplatform Satellite Surface Wind Analysis." The basic idea behind the product is to synthesize wind observations from remote sensors aboard satellites to construct a wind field for a tropical cyclone. These analyses are created with satellite-based data alone (no in-situ or aircraft reconnaissance data are involved), and since approximately 90% of the world's tropical cyclones aren't sampled by aircraft reconnaissance, you can appreciate just how important these analyses really are.

In order for you to be able to interpret these analyses and understand what data sources are used to create them, let's look at a sample of the product for Hurricane Ike at 18Z on September 11, 2008. Note that the product consists of two complete analyses. In the link provided, The first large image (top left) is the analysis of inner-core surface winds around Hurricane Ike at 18Z on September 11, 2008. The second complete analysis (shown below) displays a broader-scale surface wind analysis of the storm (the black contours are isotachs, expressed in knots). Finally, there are four other images, which show the building-block data for the inner-core and broader-scale surface wind fields.

Each complete analysis includes some text, which gives us additional information about the storm's wind field:

• QUA = Quadrant (Northeast, Southeast, Southwest, and Northwest)
• R34, R50, and R64 = The maximum radius of 30-, 50-, and 64-knot winds in each quadrant in nautical miles
• VMAX = The maximum wind speed in the analysis in knots
• RMW = The distance of the location of the maximum wind speed from the center in nautical miles
• BEARING = The direction of the location of the maximum wind speed from the center in degrees
• MSLP = The estimated minimum sea-level pressure of the storm in hectopascals (equivalent to millibars)

In addition to the two complete wind analyses, the product also includes images showing the building-block data used to create them. The image labeled "AMSU" represents surface wind data around Hurricane Ike that were derived from the Advanced Microwave Sounding Unit at 18Z on September 11, 2008. You may recall that AMSU-A can't directly measure surface wind speeds, but using complex equations that govern atmospheric motions (way beyond the scope of the course), AMSU-A brightness temperatures archived from past storms were correlated with QuikSCAT and other data to derive surface winds from AMSU-data.

The image labeled "CDFT" represents cloud-drift winds based on infrared and water-vapor imagery (about which you learned earlier in this lesson) around Hurricane Ike at 18Z on September 11, 2008. Of course, the winds directly derived from such techniques are not surface winds, but winds aloft are empirically adjusted downward to estimate winds at the ocean surface.

The image labeled "IRWD" indicates surface winds derived from cloud temperatures on infrared imagery around Hurricane Ike at 18Z on September 11, 2008. In a nutshell, researchers closely examined infrared imagery of 87 tropical cyclones and used IR temperature data in concert with observed and estimated wind data to create an algorithm to estimate low-level wind fields of tropical cyclones.

The image labeled "SCAT" (below) displays scatterometer winds from ASCAT (in red) and QuikSCAT (in blue) around Hurricane Ike at 18Z on September 11, 2008. In this particular case ASCAT completely missed much of Ike's circulation (only capturing the western and eastern edges with its scans), while QuikSCAT got a pretty good "look" at Ike. That's not surprising since the chances of sampling an entire circulation were much higher with QuikSCAT. The data void near the center of Ike's circulation resulted from unreliable, rain-contaminated observations.

I only gave very brief descriptions here about the various techniques for using AMSU, cloud-drift winds, and IR winds to determine surface winds, so if you would like more information, you can check out the product description, which includes some links to seminal research papers involved with the product's development.

I also only gave a simple overview of how scatterometry works in this section. If you're interested in the more complex nuances of scatterometry, check out the Explore Further section below. Otherwise, it's time to wrap up our extensive treatment of remote and in-situ sensing in the tropics. I hope that you can now appreciate the importance that remote sensing plays in analyzing tropical cyclones, but even with the application of new technologies and techniques, meteorologists face numerous challenges and can only make best estimates about the current state of tropical cyclones around the world!