Prioritize...
When you've completed this page, you should be able to define the mesoscale's three subdivisions -- meso-α (meso-alpha), meso-β (meso-beta), and meso-γ (meso-gamma), as well as identify some common weather phenomena in each size scale, and place features on weather maps into the proper size scale using reference measurements.
Read...
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:
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).
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.
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.
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.
Key Skill...
In the absence of a distance scale on a particular weather map, using reference measurements to distinguish meso-α, meso-β, and meso-γ weather features is a good approach, but it can be challenging. When analyzing mesoscale weather features, the weather maps we use often only cover a single state (or less), or at best, a region of the country. There's no guarantee that the map domain will contain a nice, easy reference against which we can base our measurements.
Still, I want to offer some basic guidelines to get you started. One handy reference can be the scanning area of a single NEXRAD Doppler radar site (like the example below from Melbourne, Florida, on September 14, 2015). Recall from your previous studies that the range of the radar is 230 kilometers (about 143 miles). That means the radius of the circle in the image below is 230 kilometers, or very near the boundary between meso-α and meso-β.
So, if a weather feature is smaller than the range of the radar (the radius of the circle), then it's meso-β or smaller. Furthermore, since meso-γ features only span from 2 to 20 kilometers, they're smaller than most individual counties, which can also be a useful reference. Of course, there's a caveat that county sizes vary greatly, so a meso-γ feature may be much smaller than a particularly large county. In the image above, then, it's safe to say that the area of precipitation just south of Melbourne would qualify as meso-γ, while collectively, the cluster of showers offshore to the east would be meso-β.
If a weather feature is larger than the range of the radar (the radius of the circle), then it's meso-α, or larger. But, once we start analyzing features on those size scales, some of the references discussed on the previous page can come into play.
Explore Further...
If you follow severe weather (particularly tornado outbreaks), you may have wondered why I made reference to the Fujita Tornado Damage Scale when discussing the LaPlata, Maryland tornado of 2002 above. After all, the Enhanced Fujita Scale has been the standard for rating damage from tornadoes for years now. Succinctly, I include the Fujita scale for historical perspective. In 2002, it was still the standard scale for assessing tornado damage.
However, in the aftermath of an outbreak of killer tornadoes across north-central and northeast Florida in the wee hours on February 2, 2007 (which caused 21 fatalities), meteorologists switched to the Enhanced Fujita Scale to estimate the maximum winds of twisters. The Enhanced Fujita Scale was developed to correct some known weaknesses of the original Fujia Scale, namely that it overestimated wind speeds, especially on the high end of the scale (F3 and greater). The original Fujita Scale also did not account for differences in construction between damaged structures.
The Enhanced Fujita Scale employs more damage indicators on a greater variety of structures, which allows for a more realistic assessment of the damage from a tornado. Meteorologists got their first opportunity to apply the new scale with the "Groundhog Day Tornado Outbreak" of February 2, 2007. A long-tracked supercell thunderstorm spawned a family of three tornadoes as it crossed the central peninsula of Florida, and after meteorologists completed their damage surveys, two of the twisters were rated EF-3. An aerial view of damage near Lake Mack and the photograph (below) give you a sense of the incredible devastation.
Since February 2, 2007, all tornadoes have received Enhanced Fujita ("EF") ratings, but all storms prior to that date still retain their "F" ratings on the original scale. If you're interested in reading some brief history, the Storm Prediction Center has a summary of the two scales and the transition. You may also enjoy this Weatherwise Magazine article about the introduction of the EF-scale.