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Your focus on this page should be on interpreting scatterometry and synthetic-aperture radar (SAR) data, which requires an understanding of the abilities and limitations of the instruments that create them. Specifically, you should be able to discuss the primary use of scatterometry data and SAR data and interpret a variety of scatterometry and SAR data.
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One of the reasons that remote sensing from satellites is so important to tropical cyclone observation and forecasting is that tropical cyclones spend so much time away from land-based observational networks (such as radar networks, among others). But, did you know that some polar-orbiting satellites also have radars mounted on them, which provide critical data to tropical forecasters? The primary use of these radars is actually in detecting the wind field in tropical cyclones, but like all tools, they have some limitations. So, let's get right into the first of these tools -- scatterometers.
Scatterometry
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 (opens in a new window)"). 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.
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.
A number of scatterometers have been mounted on polar-orbiting satellites and have made key contributions to tropical forecasting since the 1990s. 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. In fact, when scatterometers were a relatively new tool around the turn of the century, researchers determined that they helped forecasters identify potential tropical cyclones an average of 43 hours before the National Hurricane Center formally classified the systems as tropical depressions. Identifying potential tropical cyclones from scatterometer data involves the detection of a developing cyclonic circulation of surface winds. So, how do forecasters interpret scatterometry data for this purpose?
Interpreting Scatterometry Data
Let's start with examining what scatterometry data actually looks like in practice. Once the data have been processed by computers, the output looks something like the image below. 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, notable swaths of missing data exist across the western Gulf and across much of the Caribbean into the Atlantic Ocean off the East 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 clear cyclonic swirl, which corresponded with Hurricane Milton (opens in a new window) in 2024.
Note that most of the wind barbs near Milton'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 (such as dots at the end of a wind barb) to indicate unreliable data. Scatterometers have trouble collecting good data in areas of heavy rain because raindrops severely attenuate microwave radiation, which weakens the signal received by 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, the reliable observations suggest that Milton's maximum surface wind speed was only a little more than 50 knots, which was an underestimate since Milton was actually a Category 4 hurricane at the time.
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. Scatterometers can differ in their frequency of radiation emitted, resolution, sensitivity to rain contamination, scanning width, etc. While each scatterometer has its own unique set of characteristics and limitations, I'm going to highlight the most significant ones. First, while scatterometers use higher frequencies of microwaves than land-based Doppler radars, they don't all use the same frequencies. Typically, they are either Ku-Band (opens in a new window) radars (frequencies between 12 and 18 GHz) or C-Band (opens in a new window) radars (frequencies between 4 and 8 GHz). Employing these different frequencies has some tradeoffs, which I've summarized in the table below.
Note that each type has its advantages and disadvantages. I should also note that while C-Band scatterometers are a bit less sensitive to attenuation in areas of heavy rain than Ku-Band scatterometers, rain contamination isn't eliminated entirely. Furthermore, the ASCAT scatterometer series (maintained by the European Space Agency) also has a documented low bias at moderate to high wind speeds (greater than about 20 meters per second, or 39 knots).
Each scatterometer passes over a region twice per day (one "ascending" pass and one "descending" pass), but scatterometers can differ greatly in how much they "see" in a single pass. To gain a better understanding of these differences in coverage for a single pass, check out the image below, which shows a coverage comparison between ascending passes from an ASCAT series scatterometer (left) and an OSCAT series scatterometer (right). The superior spatial coverage of OSCAT 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).
ASCAT passes have larger coverage gaps since they view the earth differently than OSCAT. ASCAT views the earth in two parallel swaths 550 kilometers wide (opens in a new window), 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 pass sees much less, but having more than one ASCAT unit orbiting the earth helps to compensate.
You may encounter data from any of these scatterometers or others (scatterometers have even been launched by private sector companies) 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.
Synthetic-Aperture Radar (SAR)
Much like scatterometry, Synthetic-Aperture Radar (SAR) data comes from active remote sensors (they emit pulses of energy and measure what gets back-scattered), so in that sense SAR and scatterometry have some basic similarities for tropical cyclone detection (faster winds produce a rougher ocean surface, which causes more back-scatter to the satellite). However, SAR can produce imagery of much higher resolution than scatterometry. In addition to tropical cyclone detection, SAR data is also useful for glacier and sea ice monitoring, surveilling coastal erosion, mapping forest cover, and monitoring disasters like floods, volcanic eruptions, and oil spills.
So, how does SAR produce such high-resolution imagery from a satellite? It turns out that the spatial resolution of radar data depends on the wavelength of radiation emitted and the length of the radar antenna. Generally, for any given wavelength, a longer antenna will produce higher spatial resolution. Producing imagery with a resolution of hundreds of meters (compared to 12-25 kilometers for scatterometry) would require a huge antenna, which isn't really practical. But, by combining a rapid sequence of emissions targeted at the same spot from a shorter antenna on a moving satellite, the effect can simulate a much longer antenna and produce a higher-resolution image. It's this "simulation" of a much larger antenna that puts the "synthetic" in synthetic aperture radar. Determining wind direction is a bit tricky, and SAR relies on supplemental wind data (like that from scatterometers) or applying established models of wind flow in tropical cyclones to determine direction.
The end result is that SAR imagery can provide forecasters with a highly-detailed look at a tropical cyclone's wind field (far more detailed than scatterometry), including fierce winds in the eye wall and within individual spiral bands. For example, check out the SAR image of surface winds in Hurricane Melissa on October 27, 2025 (below). Note that while SAR imagery does have moderate issues with rain contamination, it's still able to depict areas near the center of the storm with winds greater than 100 knots.
Furthermore, because SAR can give forecasters a detailed look at the entire wind field of a tropical cyclone, it can help forecasters analyze the winds in each quadrant of the storm, identifying the radius of maximum wind speed, and the maximum extent of hurricane-force winds, 50-knot winds, and 34-knot winds in each quadrant. The corresponding horizontal wind speed profile for Hurricane Melissa is below. The vertical axis represents wind speed, and the bottom axis represents distance from the center. It's easy to pick out the peak winds in the eye wall, with wind speeds steadily decreasing at greater distances from the center. Each quadrant has a line representing the average winds in that quadrant with distance from the center, but you might also note that each quadrant has a dotted line that represents something called "2 sigma" (which represents "2 standard deviations"). These dashed lines give forecasters an idea of the "high-end" wind speeds at that distance from the center (wind speeds within any quadrant obviously have some variation along a ring at a given radius).
The graphic showing Melissa's horizontal wind speed profile also provides some text information, which gives us more information about the storm's wind field if we decipher the abbreviations:
- QUA = Quadrant (Northeast, Southeast, Southwest, and Northwest)
- R34, R50, and R64 = The maximum radius of 34-, 50-, and 64-knot winds in each quadrant in nautical miles
- VMAX = The maximum wind speed in the analysis in knots ("mean" = 1-minute average; "peak" = roughly instantaneous)
- RMW = The distance of the location of the maximum wind speed from the center in nautical miles
You might have noticed that some of the maximum radii of the various wind thresholds are listed as zero, which means the maximum radius can't be determined because winds of those speeds extend outside the swath covered by the SAR or they may extend onto land (where there is no data). The estimated "VMAX mean" in this case was 123 knots (142 miles per hour), which is on the low side, considering that the National Hurricane Center listed Melissa as a Category 5 storm with 155-knot maximum sustained wind speed at this time. Why the large discrepancy? Well, In addition to some vulnerability to rain contamination, what the SAR actually detects are essentially instantaneous wind speeds based on the roughness of the ocean surface. But, forecasters classify tropical cyclones using a 1-minute average (some forecasting centers use a 10-minute average) of wind speeds at an altitude of 10 meters. Therefore, SAR winds get converted to 1-minute averages so forecasters can make "apples to apples" comparisons with other types of wind observations. This conversion (which involves statistical correlations with other types of wind observations) results in some spatial averaging of the very high-resolution SAR data (note that the pixel size was listed as 3.0 km in the graph above, even though this SAR data was collected with a resolution of 500 meters). The end result is a more "spatially smoothed" product which can underestimate maximum wind speeds a bit.
Still, the maximum sustained wind speeds retrieved from SAR imagery tend to be closer to reality than what scatterometry can detect. But, SAR data isn't without other limitations. Unlike scatterometers, which collect continuous swaths of data as they orbit Earth, SARs aboard polar-orbiting satellites must be given specific targets to analyze, which means coverage is more limited. For example, check out this map showing SAR acquisitions (opens in a new window) across the Atlantic and Pacific Oceans on a single day from two polar-orbiter missions.
We've covered a lot of information in this section (satellite-based radars are complex topics), but there's also plenty of details that we didn't cover. For folks who are interested, I've packed the Explore Further section below with some additional goodies -- data sources for scatterometry and SAR imagery, a resource for satellite-based surface wind analyses that incorporate several of the remote sensing types that we covered in this lesson, as well as a more in-depth explanation of how scatterometry works. This wraps 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!
Explore Further...
Key Data Resources
If you want to access scatterometry or SAR data for analyzing current or past tropical cyclones, you should bookmark these links:
- NESDIS Center for Satellite Applications and Research (opens in a new window): Includes data from the major scatterometers, including an archive. This page also includes data from some passive microwave sensors, which we did not cover, that determine surface wind vectors. Feel free to explore those sensors on your own, if you wish.
- CIMSS Tropical Cyclones (opens in a new window): Selecting an active tropical cyclone brings up an interface that allows you to overlay data from active scatterometers, if available.
- Naval Research Lab--Tropical Cyclones (opens in a new window): After selecting a storm, by clicking on "windspeed" you can access a variety of scatterometry data (if available) along with other surface wind products from other microwave sensors.
- SAR Winds for Tropical Cyclones (opens in a new window): The NESDIS Center for Satellite Applications and Research home for SAR imagery for current and past tropical cyclones.
We covered a lot of remote sensing products in this lesson. None of them are perfect, nor do they provide a complete look at everything a forecaster would want to know about a tropical cyclone. But, by combining data sources we can sometimes get a more complete picture. One product that takes this approach is the "Multiplatform Satellite Surface Wind Analysis" developed at Colorado State University. 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. The analyses include data from microwave sounders (winds can be calculated from the height fields derived from the sounding data), cloud-drift winds, winds derived from infrared satellite data, and scatterometry. Of these sources, only scatterometry provides actual surface wind observations, so data from the other sources has to be adjusted to the surface using statistical models. You can read more about how the product was developed (opens in a new window), if you're interested, and you can find these analyses for current and past storms at the RAMMB-CIRA at Colorado State (opens in a new window) site. Just select your storm of interest, and you should see a link for the "Multiplatform Satellite Surface Wind Analysis." The interface even allows you to see the data from each source that contributed to each analysis. While these wind fields are obviously imperfect, they are more regularly available than a product that depends on individual polar-orbiting satellites (since some sources come from geostationary satellites, the analyses can be produced for any storm even if all data sources aren't available).
How does scatterometry really work?
Although you have a basic idea of how scatterometry works, the process of determining surface wind speed and direction is actually quite complex. The short video below should give you more of an appreciation of how it really works.
How Scatterometry Really Works (4:24)
Transcript: How Scatterometry Really Works (4:25)
Let’s take a closer look at how scatterometry really works. To start, let’s imagine you're canoeing on a pond or lake. The wind is light, but occasionally a slight breeze kicks up and blows across the relatively smooth water. If you look down at the water, you might notice tiny ripples on the surface of the water, like the ones labeled in this image. Those tiny ripples are likely capillary or gravity waves, with wavelengths on the order of centimeters. We’ll call these “short water waves.” For all practical purposes, these waves are a measure of the "roughness" of the sea surface, which, in turn, depends on wind speed. As wind speed increases, the air exerts a greater drag on the water, making the sea surface rougher.
When transmitted pulses of microwave energy strike the ocean, microwaves are scattered in all directions, but depending on the angle that microwave energy strikes the ocean, there is a "select" size of short water waves with wavelengths comparable to that of the transmitted microwaves, which promote sufficient backscatter to the satellite. This unique kind of scattering is called Bragg scattering, and the "select" short water waves are Bragg waves. Of course, short water waves often ride on larger waves, which has an effect on how the satellite perceives their size. Here we can see the perceived size of the waves in the inset in the upper-left, and the effective distance between wave crests is rather large due to the viewing angle.
But, as those short-water waves ride along the larger waves, their perceived size changes. Note the effective distance in between waves is now smaller in the inset. At this point, these "tilted" waves no longer have a strong Bragg-scatter signal, but other tilted waves now have the optimal perceived size to contribute to the overall signal. With all of these effects, extracting the wind speed is actually a pretty messy process; however, the basic idea that faster wind speeds lead to rougher seas holds true. As a result, as the surface becomes rougher, the intensity of backscattering microwaves that reach the satellite increases, and the intensity of backscattering microwaves is then correlated to surface wind speed.
Wind direction gets even trickier. Although most wind-generated waves move with the wind, the small waves that backscatter microwaves to the radar travel every which way, and the scatterometer "sees" them all! So, there's definitely some "ambiguity" associated with determining wind direction from scatterometry. For each swath, a scatterometer actually gets three looks at the ocean surface, which helps to reduce the ambiguity associated with wind direction. This image actually shows us the scatteometer ambiguities from a pass over parts of the Gulf, Caribbean, and western Atlantic Ocean. Each line originating from a point represents a possible wind direction for that location, and you might notice that most observation points have two or three possibilities.
From these possibilities, computers are able to determine the most likely wind direction based on the multiple looks that the scatterometer had, and here we have the final product from this pass, showing winds across this area. There was actually a tropical storm in the northern Gulf on this day, and we can pick out the circulation toward the top left of the image, even with quite a few rain contaminated observations in the area. I should note that ambiguity selection is not a perfect process, and if the final scatterometer analyses look a bit odd to experienced forecasters, they will sometimes take a plot of the scatterometer ambiguities and conduct their own hand analysis to better determine wind direction based on their experience.
Understanding scatterometry's reliance on short water waves helps us also understand its problems in areas of heavy precipitation. In addition to rain's significant attenuation of microwaves, raindrops splashing down on the ocean surface can also dampen out Bragg waves. This image provides a good example. It shows the ocean footprints from strong surface winds generated by a cluster of evening thunderstorms over water. The horseshoe-like footprints correspond to the winds caused by downdrafts of rain-cooled air impacting the water and then spreading radially outward from the cores of the storms. The dark areas inside the footprints represent areas where heavy rain splashing down on the sea surface erased the Bragg waves that backscatter the radar signal to the satellite. The weakened return signal to the scatterometer leads to erroneous results in wind speeds and directions over regions where rain rates are high, and the rain-contaminated data get marked as unreliable.