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The spatial scales of weather systems run the gamut from planetary scale to microscale. Before we get into defining each specific scale, I should point out that none of them have universally accepted definitions. That's right, the "boundaries" of each size scale can be somewhat murky. Therefore, think of the size scales more as a continuum, instead of having hard, fixed boundaries. In any event, I still want to give you some general guidelines, and in this course, we'll base our definitions on some of the more commonly used criteria. Just keep in mind that the exact boundaries are somewhat artificial.

The planetary scale typically includes long waves, which have wavelengths exceeding 5000 kilometers (about 3000 miles). For example, the analysis of the daily average 500-mb heights on May 10, 2010 (see below), reveals several long waves encircling the Northern Hemisphere. Note the long-wave trough over eastern North America and the long-wave ridge farther downstream over the Atlantic Ocean. Technically speaking, the wavelength of this trough-ridge couplet is the distance between the trough axis over eastern North America and the trough axis off the west coast of Europe (marked by the dashed white lines). This distance is right around 5000 kilometers, so it falls into the planetary scale.

The average daily 500-mb heights on May 10, 2010. Note the long-wave trough and ridge over eastern North America and the North-central Atlantic Ocean, respectively. This long wave qualifies as a feature on the planetary scale.

Credit: Earth System Research Laboratory

Next in our spectrum of spatial scales is the synoptic scale, which refers to features ranging from about 1000 kilometers (about 600 miles) to 5000 kilometers. However, I want to again emphasize some murkiness here. Many meteorologists take the smaller end of the synoptic-scale to be 2000 kilometers (about 1200 miles), so just realize that when you encounter features between 1000 kilometers and 2000 kilometers, you may find some disagreement about their classifications. Regardless of that murkiness, you should already be familiar with many synoptic-scale features. The mid-latitude high- and low-pressure systems that you've studied in previous courses, along with warm and cold fronts associated with mid-latitude cyclones are typically considered synoptic scale features, when measured by their lengths.

That qualifier I added at the end, "when measured by their lengths" is very important because whenever you're attempting to categorize the scale of weather systems, always keep in mind that your classification depends on the axis along which you're measuring. For example, if we look at the surface analysis from 03Z on August 23, 2015, the length cold front that snakes from the Upper Midwest back through the Rockies qualifies as synoptic scale (and that's typical of most cold fronts). However, cross-sectional views of fronts associated with mid-latitude cyclones reveal that the air motions along (and near) the front are much smaller, and are typically less than 1000 kilometers, so they're smaller than synoptic scale.

The bottom line is that that any classification of the spatial scale of weather systems often depends on the horizontal axis along which you focus your analysis. You may find that along its major (longer) axis, a feature fits into one size scale, but along its minor (shorter) axis, a feature fits into another size scale. It's fairly common for surface fronts to be synoptic-scale in terms of their lengths (major axis), but have vertical motions that occur across the front (minor axis) which qualify as mesoscale.

Speaking of the mesoscale, it's time to finally complete our definition. Mesoscale weather features are between roughly 2 kilometers (1.2 miles) and 1000 kilometers. Many various mesoscale weather features exist, and we'll study a lot of them in this course. For now, however, we'll use a thunderstorm as a common example of a mesoscale weather feature. As you'll soon see, meteorologists actually subdivide the mesoscale even further, and we'll get into more details on that in the next section.

Finally, microscale weather features are those that span less than two kilometers. Even though we'll study tornadoes in depth in this course, technically, most of them are microscale features. Very few tornadoes exceed the two kilometers in width needed to qualify them as mesoscale features.

That's a quick run down on spatial scales from planetary scale to microscale. Up next, we'll take a closer look at how meteorologists subdivide the mesoscale. Before you move on, however, an important skill that you need to develop is the ability to identify features on weather maps, and classify their size scale properly. Check out the Key Skill section below for some important discussion and tips about properly sizing things up.

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In the previous section, we defined the mesoscale as ranging from 2 kilometers to 1000 kilometers. However, the reality is that weather features toward the small end of that range (nearly microscale) can behave much differently from those near the large end of that range (nearly synoptic scale). Therefore, meteorologists break the mesoscale down into three subdivisions, as illustrated in the image below:

The three subdivisions of the mesoscale -- the meso-γ (meso-gamma) scale (2 to 20 kilometers), the meso-β (meso-beta) scale (20 to 200 kilometers), and the meso-α (meso-alpha) scale (200 to 1000 kilometers).

Credit: David Babb © Penn State is licensed under CC BY-NC-SA 4.0 

At the large end of the mesoscale, we have the meso-α (meso-alpha) scale (200 to 1000 kilometers), followed by the meso-β (meso-beta) scale (20 to 200 kilometers), and the meso-γ (meso-gamma) scale (2 to 20 kilometers) at the small end of the mesoscale.

A tropical cyclone, which is the generic name for a low-pressure system that forms over tropical seas (it has a distinct low-level cyclonic circulation), is representative of a meso-α (meso-alpha) weather system because its spatial scale usually falls within 1000 kilometers. For example, the satellite-based radar and cloud image below shows the structure and spatial scale of Hurricane Frances on August 30, 2004. Given the distance scale along the bottom of the image, you can see that Frances easily qualified as a meso-α feature (it spanned about 400 kilometers).

A satellite-based radar and cloud image of Hurricane Frances at 10:21Z on August 30, 2004 (to the north of the Leeward Islands). Tropical storms and hurricanes typically qualify as meso-α features.

Credit: NASA-TRMM

Of course, tropical cyclones vary markedly in size, and indeed, not all tropical cyclones are meso-α features. Certainly, most are meso-α features, but we can't make sweeping generalizations to say that they all are, and that's the case with many atmospheric phenomena. On the one hand, the very largest hurricanes can cross the threshold into the synoptic scale. Hurricane Sandy (2012), for example, was one such storm that spilled over into the synoptic scale since its circulation exceeded 1000 kilometers. Meanwhile, the smallest hurricanes are small enough to be considered meso-β. Hurricane Danny (2015), for example, was a pipsqueak by hurricane standards (it was one of the smallest Atlantic hurricanes on record). Danny's area of winds greater than 34 knots (tropical-storm force) had a diameter less than 100 miles (160 kilometers), classifying the storm as meso-β.

Speaking of the meso-β subdivision, I offer a single band of lake-effect snow that formed over Lake Michigan on February 20, 2008 (check out the 1553Z image of radar reflectivity from Grand Rapids, Michigan, below). Obviously, I'm referring to the length of the band of snow when I classify the band as a meso-β feature.

The 1553Z radar reflectivity from Grand Rapids, Michigan, on February 20, 2008, shows a single band of lake-effect snow over Lake Michigan. Lake-effect bands typically qualify as meso-β features.

Credit: Used by permission, Gibson Ridge Software / National Weather Service

Other examples of typical meso-β features are sea and lake-breeze circulations, which we'll study later in the course. An interesting feature associated with this lake-effect band was the swirl toward its southern edge. Appropriately, that signature was from an aptly named, "mesovortex" that formed over the southern bowl of Lake Michigan (you can think of a mesovortex as a meso-γ low-pressure system). You'll encounter mesovortices again later in the course, as well.

Taking another step down to the meso-γ scale, we finally get to the typical scale of individual thunderstorm cells. For example, this supercell thunderstorm (a supercell is just a thunderstorm with a persistent, rotating updraft) over Southern Maryland on April 28, 2002 qualifies as a meso-γ feature. The photograph was taken on a commercial flight by a former Penn State meteorology student! Along its destructive path, this storm spawned large hail and an F4 tornado on the Fujita Tornado Damage Scale. The tornado reached F4 intensity over La Plata, Maryland, where it killed three people and injured 100. The La Plata twister was the strongest ever to hit Maryland since weather records began. For more on the Fujita Tornado Damage Scale, check out the Explore Further section below, if you're interested.

(Left) The La Plata tornado over Chesapeake Bay after it rampaged through La Plata. At this point, the tornado was an F2 (based on damage assessments at nearby shore communities). (Right) In the aftermath of the F4 twister that struck La Plata, Maryland, on April 28, 2002, NASA's EO-1 satellite captured the swath of destruction through the town.

Credit: National Weather Service / NASA

I should point out that the thunderstorm that spawned the tornado is the mesoscale feature, not the tornado. This tornado (and the vast majority of tornadoes) are actually microscale features. To give you a better sense of the scale of this tornado, focus your attention on the satellite image on the right above. Clearly, the width of the twister's damage swath was confined to several rows of houses, indicating that the tornado was only a few hundreds of meters across. Although this course is about mesoscale forecasting, we will, of course, study microscale features as they relate to the parent mesoscale weather systems.

Before we move on, I want to point out that you might also occasionally encounter the term "storm scale" around the World Wide Web. Most informal definitions suggest that "storm scale" refers to the "scale of individual thunderstorms" and have equated storm scale with the meso-γ subdivision. Yet, I have also seen "storm scale" linked to the meso-β subdivision. The bottom line is that no official guidelines regarding the use of "storm scale" exist, so I won't use the term in this course, and will stick with the three subdivisions shown above.

Up next, we'll shift from talking about spatial scales to talking about time-scale issues involving mesoscale systems. But, before we move on, check out the Key Skill box below, which will give you some exposure to reference measurements and the mesoscale subdivisions.

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Now that you have a good handle on where the mesoscale fits into the range of spatial scales, it's time to shift gears and talk about time scales. To launch our discussion, let's cover a couple of definitions:

• The Lagrangian time scale (or "time scale" for short) of a weather system, is the amount of time it takes for an air parcel to move through the entire system. The word, "Lagrangian," means that we follow an air parcel on its trek through the weather system.
• The duration of a weather system refers to its lifetime -- how long the feature itself lasts.

To understand the difference between the time scale of a weather system and its duration, I'll use a supercell thunderstorm as an example. Recall that supercell thunderstorms possess a persistent, rotating updraft that sometimes (although not always) produces a tornado. The duration of most supercells is, as a general rule, between one and four hours, which means that most supercells "live" for one to four hours, before they dissipate. I should note, however, that long-lived supercells can last as long as eight hours.

Now, what about the time scale of a typical supercell? For starters, check out this nifty computer simulation of a tornadic supercell showing the motions of various streams of air that flow through the storm. It's clear from the animation that individual air parcels flow all the way through a supercell during its lifetime. The peach-colored ribbons indicate the paths that air parcels took through the updraft of an idealized supercell.  Relative to the moving storm, air parcels enter the storm near the ground, rise, and then get whisked downstream by westerly winds near the top of the storm. The trip through the updraft usually lasts about 20 minutes, which serves as a fairly good approximation for the Lagrangian time scale of a supercell. In case you're wondering, the blue ribbons follow the paths of air parcels entering the rear of the storm and ultimately sinking toward the ground.

A single frame from a computer simulation of a supercell thunderstorm. The peach-colored ribbons indicate the paths that air parcels took through the storm's updraft (relative to the moving storm).

Credit: University of Illinois

The bottom line here is that the time scale of a weather feature might be a lot different from its duration. Think back to some mid-latitude weather features that you studied previously.  A jet streak, for example, moves along in the flow at 300 mb at 30 to 50 knots, on average, during the winter. But, individual air parcels are moving much faster, and they accelerate right through the jet streak. In other words, a jet streak's duration is much longer than its Lagrangian time scale.

We can apply similar thoughts to shortwave troughs. The troughs themselves move along in the synoptic-scale flow, but individual air parcels move right through the shortwave (causing divergence downstream, if you recall). So, because the shortwave lasts much longer than the amount of time it takes for a parcel to travel through it, the duration of a shortwave trough is longer than its Lagrangian time scale.

Most of the references that you'll run across will typically categorize weather systems by their spatial scales and their duration (not their Lagrangian time scales). But, I like to make a clear distinction here because we'll talk a lot this semester about how air parcels move relative to the parent weather systems.

Still, there's a general relationship between the size scale of a weather feature and its duration. The duration of a dust devil (photograph courtesy of David DiBiase), a microscale rapidly rotating wind that is made visible by the dust, dirt or debris it picks up, is typically on the order of a few minutes or shorter. Under optimum conditions, dust devils can last as long as a few tens of minutes, but such "long-lived" dust devils are rare. On the other hand, keeping in mind that the duration of a supercell thunderstorm (a meso-γ or meso-β feature) is typically one to four hours, you should now get the impression that, as the spatial scales of weather features decrease, so do their duration.

To confirm your impression, check out the schematic below; it displays the spatial scales (horizontal axis) and duration (vertical axis) of selected weather features. Pay close attention to relationship between spatial scale and duration. As a general rule (with a few exceptions), the smaller the spatial scale, the shorter the duration.

A schematic showing the typical spatial scale and duration of selected weather features. In the case of synoptic-scale fronts, the indicated spatial scale corresponds to a representative length (not a width). Similarly, the indicated spatial scale of dry lines also corresponds to a representative length. Full-sized image.

Credit: David Babb © Penn State is licensed under CC BY-NC-SA 4.0 

Given the relatively short duration and small spatial scale of mesoscale phenomena, forecasters require computer models that are higher resolution and incorporate hourly observations so as to more accurately model rapidly evolving weather patterns. We'll investigate in the next section.

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On February 10, 2009, supercells erupted over parts of the Southeast States. The 2238Z radar reflectivity (below) from Maxwell Air Force Base (KMXX) indicates the rather small coverage of the severe thunderstorms over eastern Alabama and western Georgia. Only the most favorable local environments supported deep, moist convection at this time. Of course, there was no way to predict exactly where these supercells would have developed, but accurately identifying the general area (Alabama, Georgia and parts of the surrounding states) where storms were likely to "initiate" on this day would have been a pretty good forecast. It turns out that these storms spawned several reports of tornadoes and numerous reports of large hail across the region.

The 2238Z radar reflectivity from Maxwell Air Force Base (KMXX) in south-central Alabama on February 10, 2009. By this time, discrete supercells had erupted over parts of eastern Alabama and western Georgia, producing severe weather.

Credit: Used with permission, Gibson Ridge Software / National Weather Service

To successfully identify regions at risk for severe thunderstorms, forecasters first assess the background synoptic-scale pattern by looking at progs from models like the ones you learned about in your previous studies (the GFS, NAM, or others). Assessing the "big picture" from these models is a crucial step in the forecasting process. But, for outbreaks of thunderstorms like the one shown above, these models have some serious flaws. One is that important convective processes are occurring on spatial scales that are smaller than the model's grid-point scheme. The end result is that convection in these models is greatly oversimplified (formally, "parameterized"), which leads to struggles with forecasts for convective precipitation.

Another major problem stems from the fact that, as you just learned, many mesoscale weather features have a relatively short duration. Supercell thunderstorms typically last one to four hours before dissipating (some other types of thunderstorms last less than one hour). But, models like the NAM and GFS are only initialized every six hours (00Z, 06Z, 12Z, and 18Z).

In terms of mesoscale weather, a lot can change in six hours! This relatively long time lag between successive runs, in addition to the inability to infuse hourly observations into the operational GFS and NAM, make these two models less viable for predicting the changing, smaller-scale environments that might favor the initiation of  thunderstorms in the next hour (or even a couple of hours).

Forecasters require "mesoscale" models, with a fine spatial resolution, that are continually updated with timely weather observations so that they can more reliably refine and update their forecasts as weather conditions change in time. Do such models exist? Indeed they do. In 2012, NCEP implemented the Rapid Refresh Model (RR), a short-range model that incorporates GFS forecast data and an analysis / assimilation system to update the model with hourly observations. The Rapid Refresh Model runs every hour, providing crucial short-range forecasts. Forecasters at the Storm Prediction Center, as well as forecasters in the aviation community, frequently incorporate RR analyses (0-hour forecasts) and predictions into their forecasting routines.

The RR model provides data that have a relatively high resolution in space and time (forecasts are available at one-hour intervals). There's also a high-resolution version of the Rapid Refresh that mesoscale forecasters use operationally (the High-Resolution Rapid Refresh or, more simply, the HRRR). For the record, the HRRR model has an even higher spatial resolution, and offers forecasts at 15-minute intervals (read more about the details of the HRRR, if you're interested).

Models like the RR and HRRR have a couple of key advantages. First, because they're initialized every hour, they're more "in touch" with rapidly changing weather situations than models that are initialized every six hours (like the GFS and NAM). Second, with forecast intervals of an hour or less, the RR and HRRR are able to depict the evolution of mesoscale weather systems with greater detail than models having longer forecast intervals.

Furthermore, convection in the the HRRR is not parameterized. It has a sufficiently high spatial resolution that it can actually simulate real convection. Such models are called "convection allowing" models and need to have a grid spacing no larger than four or five kilometers. Because it doesn't have the great oversimplifications that come with convective parameterizations in coarser models, the HRRR is able to depict much more realistic convective structures. As you can see from the HRRR forecast below, its prediction of radar reflectivity looks pretty realistic, doesn't it?

The six-hour forecast of reflectivity from the 17Z run of the High Resolution Rapid Refresh Model on April 27, 2011 (valid at 23Z), accurately predicted the timing, location, and structure of a squall line moving eastward across northern Pennsylvania and western New York.

Credit: NCEP

In the six-hour forecast of radar reflectivity from the 17Z run of the HRRR on April 27, 2011, valid at 23Z (shown above) note the placement and structure of the narrow squall line in western New York and northern Pennsylvania. Now, compare the forecast to the actual 23Z mosaic of composite reflectivity. As you can clearly see, the HRRR had an awesome forecast, capturing the timing and structure of the squall line really well. On the other hand, the HRRR didn't predict the severe storms that formed out ahead of the squall line at all. The HRRR forecast also had problems in Maryland, Ohio and West Virginia, so this forecast was far from perfect.

I hope this example makes it clear that even though such "convection-allowing" models create detailed, realistic-looking convective structures, that does not mean their solutions are always accurate. Indeed, while such models are skillful in predicting the mesoscale details and structure of convection, they do not show consistent skill in predicting the exact timing or location of individual convective cells.

Another problem with "convection-allowing" mesoscale models is that they are prone to huge run-to-run variability (successive solutions may look nothing alike). To combat the large run-to-run variability, forecasters often look for a degree of consistency in three consecutive runs of the HRRR. If the model's solution is similar for three runs in a row, then forecasters have a bit more confidence in the solution. Researchers involved in the Vortex2 project routinely weighed HRRR forecasts to help them formulate plans to intercept storms. If the HRRR was producing consistent solutions for three consecutive runs, chasers would adjust their intercept plans accordingly.

Because these mesoscale models require great computer power to run, they are only run over a short forecast period (a day or less for most runs). Furthermore, their performance is somewhat at the "mercy" of the GFS model's initialization. Remember that the GFS feeds its initial conditions into the Rapid Refresh, so any major errors in the GFS initialization will be transferred into the Rapid Refresh, which can wreak havoc on its forecast accuracy.

Regardless of these limitations, the analyses and forecasts based on the Rapid Refresh Model are still often useful for timely short-range mesoscale prediction. For much of our work in this course, we'll focus on real-time mesoanalyses from the Rapid Refresh model available on SPC's Web site. As outbreaks of severe weather unfold you can rely on these SPC analyses to gain insight about the background synoptic and mesoscale environments.

To give you an example of the types of analyses that are available, check out the SPC mesoanalysis of vertical wind shear between the ground and an altitude of six kilometers over Deep South at 23Z on April 27, 2011. Vertical wind shear refers to a change in wind speed and / or direction with increasing altitude, and it's an important variable in determining the organization and longevity of thunderstorms that develop. On this particular date, very strong vertical shear existed over the Deep South, which played a role in one of the biggest tornado outbreaks in U.S. history that occurred over the region.

The 23Z analysis of vertical wind shear (in knots) between the ground and an altitude of six kilometers over the Deep South on April 27, 2011.

Credit: Storm Prediction Center

Later on, we'll get into the basics on how you can interpret these and other mesoanalysis images, and discuss their connections to the development of deep, moist convection. If you're interested in seeing more about this outbreak, and getting some links where you can access RR and HRRR forecasts, check out the Explore Further section below. Before we end this lesson, however, allow me to introduce the 3-kilometer NAM, which also has some utility for creating short-term mesoscale forecasts. Read on.

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The Rapid Refresh (RR) and High-Resolution Rapid Refresh (HRRR) aren't the only "mesoscale models" available. The National Centers for Environmental Prediction also run high-resolution, convection-allowing versions of models you're already familiar with, which also have use in mesoscale forecasting.

One such model is the NAM. For its high-resolution output, the NAM employs "one-way" smaller nests within the larger outer model domain. Within each nest, the model computes forecasts concurrently with the 12-km NAM parent run. For the record, "one-way nested" means that the inner (nested) model domain receives its lateral boundary conditions from the outer domain, but it does not feed back any information to the outer domain. In other words, the outer domain is not affected by the nest.

The parent 12-kilometer domain of the NAM, along with its three-kilometer nests for CONUS, Alaska, and Hawaii / Puerto Rico, respectively. The smallest rectangles represent very high-resolution nests for predicting fire weather.

Credit: National Centers for Environmental Prediction

The nested domains within the parent NAM have higher resolutions, with three-kilometer nests covering the contiguous U.S., Alaska, Hawaii and Puerto Rico (shown above). The resolution of the internal nests of the NAM is sufficiently high to realistically simulate convection, so while convection is parameterized in runs of the parent 12-km NAM, it's not in the higher-resolution forecast nests. In case you're wondering, the small unlabeled boxes in the image above represent small nests with even higher resolution that are used for predicting fire weather.

The GFS, on the other hand, actually runs on a dynamic model core called the "FV3" ("Finite Volume Cubed Sphere"), which runs on a "flexible" grid. The flexible grid gives modelers options for running higher-resolution versions that can realistically simulate convection over parts of the globe. The model also has the ability to run higher resolution "two-way" nests within its global domain (two-way nests receive their lateral boundary conditions from the outer domain and can feed back some information to the outer domain).

So, are the high-resolution versions of the NAM and FV3 every bit as useful as the HRRR? Not exactly. There's a key difference between the two. While the HRRR is initialized every hour, the high-resolution FV3 and NAM are still only initialized every six hours (06Z, 12Z, 18Z, and 00Z). The high-resolution FV3 and NAM do have forecast intervals of one hour, but they do not get infused with hourly surface observations, which makes them less viable for predicting the small-scale rapidly changing environments that may favor the initiation of thunderstorms.

While the high-resolution FV3 and NAM produce forecasts with realistic-looking convective structures (like in the example below), the same caveats that went along with HRRR forecasts apply. Just because the forecasts look realistic doesn't mean they're accurate, and remember, the fact that the high-resolution FV3 and NAM are only initialized every six hours is a notable drawback. On the flip side, one advantage to these models is that their forecasts go out a few days into the future, which is longer than forecasts from the RR and HRRR. Like with the HRRR, the timing and exact location of individual thunderstorms are often incorrect in high-resolution FV3 and NAM forecasts, but they can still give useful insights into the general coverage and structure of thunderstorms.

The 31-hour forecast of radar reflectivity and MSLP valid at 19Z on May 11, 2023 (from the run initialized at 12Z on May 10) from the high-resolution version of the FV3. Since the high-resolution FV3 does not parameterize convection, its forecasts include realistic-looking convective structures.

Credit: Tropical Tidbits

For comparison with the forecast prog above, the corresponding forecast of radar reflectivity and MSLP from the high-resolution NAM had general similarities to the high-resolution FV3 forecast, but lots of differences in the finer details of convective placement and structure.

Given the differences that regularly occur in high-resolution model output, high-resolution ensemble forecasts can also be of great use, and indeed, NCEP has developed the High-Resolution Ensemble Forecast (HREF) system for mesoscale forecasting. The HREF is comprised of HRRR forecasts, along with high-resolution versions of the NAM, FV3, and other convection-allowing models primarily used by the research community. So, mesoscale forecasters have multiple options for convection-allowing guidance and even a convection-allowing ensemble of models!

If you're interested in accessing forecasts from high-resolution, convection-allowing models, check out the Explore Further section below. Otherwise, we'll wrap up our introduction to mesoscale meteorology with a brief Case Study of a tornado outbreak, which illustrates the connections between spatial scales and the utility of real-time mesoscale model analyses. Read on.