The ECMWF, or Euro for short, is the model produced by the European Centre for Medium Range Weather Forecasts, an intergovernmental organization of over 30 European nations. The ECMWF is a global model, meaning it produces forecasts for the entire world. The ECMWF produces forecasts out to 10 days.
ECMWF data is updated twice each day, between 1:00 and 2:00 AM/PM. These times are given in Eastern Standard Time.
How is the ECMWF’s forecast data best used? Because the ECMWF is a global model that produces medium range forecasts, it isn’t run at a resolution as high as some of the shorter range models. Its 9km resolution is the highest among global models, but compared to, for example, the 1km Swiss model, it has a coarse resolution. Because the ECMWF is focused on medium range weather forecasting, the three to ten day timeframe is where the model’s highest skill is.
Statistically speaking, the ECMWF is the best performing model in the world for the three to ten day timeframe, so if its forecast differs from that of the other models, it’s often wise to side with the ECMWF, unless there’s evidence that argues otherwise. However, while the ECMWF is statistically the best model in the medium range, it isn’t perfect.
Insider Tip: The ECMWF is known to have what’s called an “overamplified bias” which means it tends to overdo the amplification, or strengthening, of a developing storm. Keep this in mind when looking at ECMWF data.
The ECMWF’s EPS is a series of 50 ensemble members, plus a control, that is used to present a range of possible outcomes for a given forecast. The EPS is run globally out to 15 days. The ensemble system works by running the same weather model repeatedly, slightly changing the initial conditions each time. Any errors in the model’s initial understanding of the atmosphere become exponentially larger through time, due to the chaotic nature of the atmospheric system. Because the model doesn’t know what each and every air molecule is doing at the start of the run, there will inherently be some errors due to an incomplete understanding of what’s happening now. The ensemble system takes the shotgun approach to this issue by starting the model many times with many different guesses for what the atmosphere could be doing right now. For example, one run might have slightly higher humidity, while another will set the temperature a bit lower than the “best guess” set of observations.
EPS data is updated twice each day, around 3:30 AM/PM EST.
How is the EPS data best used? Don’t look at any one ensemble member and expect a correct forecast. Because each ensemble member is deliberately perturbed, any one member will be less accurate than the master run. The EPS data is best taken altogether, as a portfolio of possible forecast outcomes. If the lowest ensemble member forecast for snowfall at your location is 3”, and the highest is 33”, you know that snow is likely, but because of the wide range of possible outcomes, uncertainty is high. Likewise, if the all the member forecasts fall between 10 and 14”, the range of possible outcomes is pretty narrow, and you can have high confidence in the forecast. This works for many applications, far beyond snowfall. Explore the range of possible outcomes for temperatures, storm tracks, precipitation, and much more to gauge the uncertainty in many different types of forecasts.
Insider Tip: The EPS is the most powerful tool in a forecaster’s model toolbox. While a wide variety of knowledge is needed to make a sound forecast, the EPS provides users with an astounding about of information about the predicted state of the atmosphere. In addition to accurate forecasts of large scale patterns and weather systems, the EPS provides a way to gauge forecast uncertainty. Communicating and understanding forecast uncertainty is crucially important as our predictions remain inherently imperfect.
The GEFS is NOAA’s ensemble forecast system, used to present a range of possible outcomes for a given forecast. The GEFSis run globally out to 16 days. The ensemble system works by running the same weather model repeatedly, slightly changing the initial conditions each time. Any errors in the model’s initial understanding of the atmosphere become exponentially larger through time, due to the chaotic nature of the atmospheric system. Because the model doesn’t know what each and every air molecule is doing at the start of the run, there will inherently be some errors due to an incomplete understanding of what’s happening now. The ensemble system takes the “shotgun” approach to this issue by starting the model many times with many different guesses for what the atmosphere could be doing right now. For example, one run might have slightly higher humidity, while another will set the temperature a bit lower than the “best guess” set of observations.
GEFS data is updated four times each day, around 12:30 and 6:30 AM/PM EST.
How is the GEFS data best used? Don’t look at any one ensemble member and expect a correct forecast. Because each ensemble member is deliberately perturbed, any one member will be less accurate than the master run. The GEFS data is best taken altogether, as a portfolio of possible forecast outcomes. If the lowest ensemble member forecast for snowfall at your location is 3”, and the highest is 33”, you know that snow is likely, but because of the wide range of possible outcomes, uncertainty is high. Likewise, if the all the member forecasts fall between 10 and 14”, the range of possible outcomes is pretty narrow, and you can have high confidence in the forecast. This works for many applications, far beyond snowfall. Explore the range of possible outcomes for temperatures, storm tracks, precipitation, and much more to gauge the uncertainty in many different types of forecasts.
Insider Tip: The GEFS is known to have an issue known as underdispersion. This means that the ensemble members will be overly susceptible to errors present in the operational run, and won’t give a fully representative picture of the range of possible forecast outcomes. While the GEFS is a good tool to use, keep this in mind. Underdispersion means that a forecast the GEFS seems to think is a sure bet might be a little bit more uncertain.
The GFS is the weather model run by the US government’s National Oceanic and Atmospheric Administration (NOAA). The GFS is a global model, meaning it produces forecasts for the entire world. The GFS produces forecasts out to 16 days, but predictions past 7 or 10 days are often fairly unreliable.
GFS model data is updated four times each day, from 10:30 to 12:00 AM/PM and 4:30 to 6:00 AM/PM. These times are given in Eastern Standard Time.
How is the GFS’s forecast data best used? Because the GFS is a global model that produces fairly long range forecasts, it isn’t run at a very high resolution. This means that the grid points for which the model calculates the forecast are spread farther apart than many other models. As a result, the GFS shouldn’t be leaned on too heavily for specific, short range forecasts. The GFS works best in the medium range, generally between three and ten days. Use it to spot major, large scale features like high pressure areas, low pressure systems and their warm/cold fronts.
Despite the fact that the model is run out to 16 days, beyond 10 days, the specifics of the GFS forecast shouldn’t be trusted. It can’t tell you the exact location of a storm half a month from now. You can still use this data though, by looking for general trends in the pattern over a large area. Does the model forecast a train of storms to your north? Perhaps your odds for warmer weather are increasing. Is the model consistent in keeping high pressure nearby? Perhaps odds favor drier weather.
Insider Tip: The GFS is known to have what’s called a “progressive bias” where it tends to depict storms as faster, farther east, and weaker than they usually end up becoming. Keep this in mind when looking at GFS data, and make a mental note of the potential impacts of this bias if you see a weak “flattened” system forecast by the model.
The HRRR is one of the weather models run by the US government’s National Oceanic and Atmospheric Administration (NOAA). The HRRR is a regional model, meaning that it produces forecasts only for a specific region, in this case the Contiguous US. The HRRR only produces forecasts out to 18 hours, but its higher resolution allows much more accurate forecasts for many phenomena, especially including thunderstorms, lake effect, and other convective processes.
HRRR model data is updated every hour.
How is the HRRR’s forecast data best used? Because the HRRR is a regional model, it works best at short time scales, and with smaller scale features. Use the HRRR to pick out areas and times favored for thunderstorm development, or for heavy bands of snow. It typically does a fairly good job accounting for terrain, due to its higher resolution, though for the best terrain approximations, use the 1x1km Swiss Super HD model. The HRRR will do a pretty good job predicting the evolution of large scale features such as high and low pressure systems, but because many factors outside the model’s domain impacts those large scale systems. If a feature begins the run outside the model’s domain, the model must rely on a less-accurate estimate of what that feature is doing that’s provided by a global model. Large scale high and low pressure systems can be influenced by features scattered across entire hemispheres, making mesoscale models less reliable for them. Smaller scale features, such as snow bands or thunderstorms, depend much more on the atmosphere in their immediate vicinity, making mesoscale (regional) models like the HRRR better equipped to handle them.
Insider Tip: The HRRR is probably best known for its simulated radar parameter, which produces a forecast of what a radar composite map would look like at a given should the model’s prediction come true. These maps can be deceptively precise. Even our best models can’t predict the exact time/place of the formation of an individual thunderstorm, or an individual heavy snow band. This means that it’s wise to look at these maps on a regional scale instead of a local scale. For example, think about a hypothetical forecast showing several developing storms in the New York area one afternoon. While the simulated radar map will show individual cells impacting specific towns, it’s better to think about that forecast in terms of “scattered thunderstorms developing near New York this afternoon” as opposed to “a developing thunderstorm cell will impact Lower Manhattan at 4:00 PM before moving into Queens as a fully formed storm at 5:00”.
The ICON model is the German Meteorological Service’s global model. Its name references two parts of the model’s “under the hood” mechanics, its grid shape (Icosahedral) and the fact that it doesn’t assume the atmosphere is in hydrostatic balance (which lets the model consider the potential for thunderstorms). The ICON model is a global model, meaning it doesn’t have the highest resolution, but it extends out 5-8 days in time, and provides forecast data for the entire globe.
The ICON model is updated four times each day, generally around 5 and 11 AM/PM. The midday/midnight runs extend 180 hours (7.5 days) out, while the morning/evening runs extend only 120 hours (5 days) out.
How is the ICON’s forecast data best used? Because the ICON is a global model, it’s not run at a very high resolution. This means it can’t “see” certain small scale features very well, but it will get a pretty good handle on overall patterns/large scale storm features. For example, use the ICON (in combination with other guidance and non-model forecasting techniques!) to pick out the potential for an East Coast cyclone in the medium range. But don’t trust it to figure out exactly which towns might see a heavy snow band as that storm moves up the coast. Note that the accuracy of any model decreases quite a bit by the day 7 mark, so don’t take the ICON’s longer range forecasts too seriously.
The NAM-WRF is one of the weather models run by the US government’s National Oceanic and Atmospheric Administration (NOAA). The NAM is a regional model, meaning that it produces forecasts only for a specific region, in this case North America. The NAM only produces forecasts out to 60 hours, but its higher resolution allows much more accurate forecasts for many phenomena, especially including thunderstorms, lake effect, and other convective processes.
NAM model data is updated four times each day, from 9:00 to 9:45 AM/PM and 3:00 to 3:45 AM/PM. These times are given in Eastern Standard Time.
How is the NAM’s forecast data best used? Because the NAM is a regional model, it works best at short time scales, and with smaller scale features. Use the NAM to pick out areas and times favored for thunderstorm development, or for heavy bands of snow. It typically does a fairly good job accounting for terrain, due to its higher resolution, though for the best terrain approximations, use the 1x1km Swiss Super HD model. The NAM will do a pretty good job predicting the evolution of large scale features such as high and low pressure systems, but because many factors outside the model’s domain impacts those large scale systems. If a feature begins the run outside the model’s domain, the model must rely on a less-accurate estimate of what that feature is doing that’s provided by a global model. Large scale high and low pressure systems can be influenced by features scattered across entire hemispheres, making mesoscale models less reliable for them. Smaller scale features, such as snow bands or thunderstorms, depend much more on the atmosphere in their immediate vicinity, making mesoscale (regional) models like the NAM better equipped to handle them.
Insider Tip: The NAM has a notorious “wet bias” for QPF (quantitative precipitation forecast that looks at how much liquid or liquid equivalent falls as opposed to in what form (snow/sleet/freezing rain/rain) it falls). The NAM often predicts that more rain/snow will fall than actually does, sometimes by a fairly sizable margin. Remember this when looking at rain/snow total predictions from the NAM!
The RGEM is the regional version of the Canadian Meteorological Center’s global GEM model. Because it is a regional model, it has a fairly high resolution, but only produces data 2-3 days out, and doesn’t cover the entire world. The RGEM does cover Canada and the US though, which is fairly good for a regional model (compared to for example the HRRR which is US-only).
RGEM model data is updated four times each day, just before 6 and 12 AM/PM.
How is the RGEM’s forecast data best used? The RGEM is a regional model, so its strength is depicting smaller scale features that the global models may miss. The model is especially good at figuring out the overall structure of storms, and often does pretty well with heavy snow bands. Of course, no model is perfect, but generally this one is pretty good.
Insider Tip: The RGEM can be a valuable tool, but it’s important to remember the limitations of even the best small scale models. No model, including the RGEM, can predict the exact location of a thunderstorm, or small scale band of heavy snow. Use this as guidance, to get a general idea of what to expect, instead of a precision forecast. For example, if the RGEM shows a band of heavy snow over one part of your county, with much lighter snow over another part, know that some towns near you are likely to get dumped on, while others will see much less snow. You might not be able to tell for sure which category any particular one town will fall under, but at the very least you’ll have a good sense of what might transpire with that given storm.
Dynamic Tropopause Potential Temperature - a measure of how far up you’d have to go to get to the tropopause, defined as the layer with 2 PVU’s (Potential Vorticity Units). The higher the potential temperature (also known as theta), the higher up you’d have to go to get to the 2 PVU surface. Because warm air masses are less dense than colder air masses, in general, high theta values correspond to warm temperatures, and vice versa. A number of different features are visible on the 2 PVU surface, but Tropospheric Polar Vorticies (TPV’s for short) stand out particularly well, and are often hidden on other maps. TPV’s are visible as small white “fuzzballs” that indicate very low theta values, generally below 280K. These TPV’s bring cold air wherever they go, and if they are forced to fun into a high theta (warm) airmass, they can produce large storms due to the strong temperature gradient as well as the energy associated with the TPV itself.
This map displays the forecast for the K-Index which is a measure of how favorable the atmosphere is for thunderstorm development. The K-Index uses factors like the mid level lapse rate (how fast the air cools with height in the mid levels of the atmosphere) and lower atmospheric moisture to predict how likely thunderstorm formation is. Generally speaking, values less than 20 don’t support thunderstorms, values between 20 and 35 get increasingly supportive of thunderstorms, and any values over 35 indicate a very high chance of thunderstorm formation. However it’s important to note that no single index can predict thunderstorm formation with any certainty. The K-Index, much like CAPE, is intended to give a general indication for how likely thunderstorms are.
The maximum 2m temperature over a given time interval (usually 6 hours)
The minimum 2m temperature over a given time interval (usually 6 hours)
Mean Sea Level Pressure - The air pressure over a given area, if that area was at sea level. This will correspond quite closely to the actual air pressure (what you’d see if you looked at a barometer at that location) for areas at low elevation, but will differ significantly for higher elevation areas.
Anomaly - How unusual a forecasted parameter (temperature, snowfall, etc.) is compared to normal. This map gives values in units of the parameter (i.e. meters of geopotential height or degrees of temperature).
Normalized Anomaly - How unusual a forecasted parameter (temperature, snowfall, etc.) is compared to normal. This map gives values in standard deviations, a measure of how unusual an event is compared to what’s expected for a given area. The advantage to using standard deviations is that it places everyone on the same playing field in terms of anomalies. Weather patterns vary more in the mid latitudes than in the tropics, so a 100m height anomaly that looks impressive in the mid latitudes, might only be 1 standard deviation from the mean (not that unusual an event) while the same 100m anomaly in the tropics might have a standard deviation of 3 or 4 (a very unusual event). The classic anomaly map only shows anomalies in meters, which can lack the proper context depending on the area. Normalizing the anomalies helps you put an event into context more easily.
Precipitable Water - How much rain or liquid equivalent snow would fall if every drop of moisture were condensed at a given point in the atmosphere. Note that this rarely correlates well to actual precipitation totals.
The STP is a way of combining multiple ingredients needed for strong tornadoes onto one map. The STP “looks at” CAPE (instability), Lifted Cloud Levels (how high the cloud base is off the ground), Storm Relative Helicity (rotational energy), wind shear, and Convective Inhibition (the “resistance” a particular column has to convective activity). Generally speaking, it’s very hard to get significant (greater than EF2) tornadoes without an STP value of at least 1.0, though not all locations with STP values >=1 are guaranteed to get a significant tornado, or even a tornado at all.
A simulation that shows what the radar would look like if a given model’s forecast pans out. This is an easy way to compare what the model thinks is happening to what is actually happening.
The SCP is a way of combining multiple ingredients needed for supercells onto one map. The SCP “looks at” CAPE (instability), effective storm-relative helicity (rotational energy) and wind shear. The higher the SCP value is, the more likely supercells are, providing other ingredients (such as a lifting mechanism, and favorable winds aloft) are present. It’s important to note that while it’s very hard to get supercells without high SCP values, the relationship does not work in the other direction. Just because you have high SCP values doesn’t mean you’re guaranteed to see supercells, or even any thunderstorms at all!
The surface skin temperature parameter is a measure of the outgoing longwave radiation emitted by the surface of the earth. As increasing amounts of radiation are emitted, the surface skin temperature increases. Note that the surface skin temperature is a measure of radiation, not kinetic energy (i.e. the “normal” definition of temperature). So while higher surface skin temps generally correlate to higher actual temps, there are occasionally some discrepancies, especially just after sunset when there’s no more sunlight, so the total amount of radiation available to be emitted (and hence the surface skin temp) is much lower, but there’s still lots of kinetic energy in the air molecules, so the actual temperature doesn’t fall nearly as fast.
The temperature at (number) pressure level.
The thickness of a given layer is exactly what it sounds like: how far from the top to the bottom. The most common thickness parameter is 1000-500mb, and is the distance between the level where the pressure is 1000mb (note that this can be “underground” for areas with surface pressures <1000mb) and the level where the pressure is 500mb. As an airmass gets warmer, its density decreases, meaning that its mass is spread out over a wider area. This results in the atmosphere expanding (ever so slightly, in that particular area) and higher thicknesses developing. So while near-surface dynamics can occasionally disrupt the relationship, generally speaking higher thicknesses mean warmer temperatures.
The sum of the U and V component vectors of the wind at (number) pressure level. This reflects two-dimensional movement of air at the specified pressure level.
Pressure level forecasts are those given for the altitude at which an altimeter would read a given pressure. The most widely used pressure levels are 925mb (~2,500ft), 850mb (~5,000ft), 700mb (~10,000ft), 500mb (~18,000ft), and 300mb (~30,000ft). These levels are approximate because they change over both space and time.
So what kind of features should you look for at each level?
925mb isn’t very far above the ground. You’ll see many of the same features here as you will on surface maps. Height lines will show lows, highs, warm fronts, and cold fronts just like a surface map would. If a feature like a front or low is steeply sloped, the positions may not match the surface map exactly, but it’ll be pretty close. Use this map primarily for temperature and moisture information about the near-surface environment. This is especially important for winter weather forecasting where low level temperatures and moisture can mean the difference between 12” of snow, 1” of rain, or dry ground. 2500ft is below a good amount of terrain especially in the Rockies, but also in the Appalachians. 925mb forecast data will still reflect the influences of this terrain, and for much of the Rockies, this pressure level is actually below ground level, and therefore not applicable.
850mb displays many of the same features as 925mb, but many of them will start to look a little different. Fronts will be displaced from their surface locations, usually to the west. Some disturbances that are weak will appear as troughs- axes of lower heights accompanied by a wind shift. These troughs behave differently than low pressure systems in that they don’t have fronts, and are oftentimes subtle ripples in a fast moving stream. One feature that’s especially visible on 850mb maps is the low level jet, or LLJ for short. The LLJ is basically a miniature version of the upper level jet stream that drives our weather patterns. To spot a LLJ, look for a region of winds at 850mb that are much stronger than winds in the surrounding area. Active weather often occurs right at the nose of a LLJ, where the jet bends out of the 850mb layer, often in an upward direction.
700mb is where weather systems begin to look a bit different. Instead of warm and cold fronts, many systems appear as troughs- axes of lower heights accompanied by a wind shift. These troughs behave differently than low pressure systems in that they don’t have fronts, and are oftentimes subtle ripples in a fast moving stream. One thing that’s important to watch at 700mb is the Relative Humidity. 700mb RH above 75% is often a fairly accurate predictor of clouds and precipitation. Similarly, 700mb winds that drive moisture advection (movement) in this layer are very important to the movement of clouds and precipitation overall. In cyclogenesis (storm formation) events, the formation of a 700mb low is an important milestone in the development of the new storm. NW of these new 700mb lows is where you’ll find some of the strongest dynamics and heaviest precipitation. In severe weather forecasting, 700mb temps can be an important indicator of favorability for thunderstorms. Very warm temperatures at 700mb can be a sign of a warm air “cap” that acts as a lid on thunderstorm development. Similarly, very cold temperatures at 700mb can help to create instability by steepening the vertical temperature gradient, also known as the lapse rate.
500mb is an extremely important layer for weather forecasting. This is where the features that are responsible for a lot of our weather patterns are found. 500mb troughs are much more predictive of active weather than troughs in the lower levels. The sharper and deeper the trough, the more impactful the weather around it will be. The orientation of the trough axis is another key thing to look for. Troughs oriented NE-SW (known as “positively tilted” for their positive slope) tend to bring weaker systems that move quickly through an area with low-moderate impacts. Troughs oriented NW-SE (known as “negatively tilted” for their negative slope) tend to bring stronger systems that slow down and amplify, often bringing high impact weather. Vorticity is another important thing to watch at the 500mb level. Vorticity can be thought of most easily as a measure of energy in the atmosphere. High vorticity air is energetic, and that energy can manifest itself in a number of different practical applications. The biggest things to watch for with vorticity are vorticity maxima and vorticity advection. Vorticity maxima are areas of locally maximized vorticity. These maxima indicate disturbances that oftentimes contribute to active weather. Vorticity is moved from place to place by winds, in the same way air masses are transported. This transportation process is known as advection, and vorticity advection can be an important predictor of surface weather. Positive vorticity advection (the transportation of positive vorticity values from one place to another) favors rising air and stormy weather, while negative vorticity advection favors improving weather conditions.
250/300mb is where you’ll find the jet stream. The jet stream is incredibly important to weather all through the atmosphere. You’ll be able to see the same features at 250mb as you will at 300mb, so don’t worry if some models show one level and not the other. While parameters like geopotential height, temperature, and moisture content were important to each of the other pressure levels, wind data is far and away the most useful product at 250/300mb. Wind forecasts for this level will reveal the jet stream, and smaller pockets of even stronger winds known as jet streaks. Look for active sensible weather (i.e. rain/snow) in the right entrance region of jet streaks that curve to the right, and in the left exit region of jet streaks that curve to the left. If two of these regions from different jet streaks overlap, look for exceptionally strong rising motion and boosted chances for heavy precipitation.
Accumulated Cyclone Energy - A measure of the total energy produced by a tropical cyclone over the course of its lifetime. This takes into account not only the storm’s max wind speed at one time, but also the winds produced during the rest of its lifecycle
the transport of some atmospheric characteristic (temperature, moisture, vorticity, etc.) from one place to another. Common acronyms for advection are WAA (Warm Air Advection) and CAA (Cold Air Advection). WAA is often accompanied by rising motion and clouds/precipitation, while CAA is often accompanied by sinking air and fair weather.
A Baroclinic Zone is an area where temperature changes rapidly along lines of equal pressure. When this setup occurs, there is lots of potential energy stored up in the atmosphere that can be ignited by an upper level disturbance passing through. Baroclinic zones are strongest when the difference in temperature is greatest along the same pressure contour and across the same distance. The Eastern US is a natural baroclinic zone because air just offshore is warm due to the Gulf Stream while air over the continent can get really cold should an arctic airmass spread south. When the edge of an arctic airmass sits along the East Coast, storms are more likely to form and develop rapidly due to all the available energy from the baroclinic zone.
This term describes explosive intensification of a low pressure system. Bombogenesis occurs when a storm’s central pressure deepens at a rate of 1mb/hr for 24 straight hours. When storms offshore undergo bombogenesis, look out for wild weather including heavy snow, rain, and strong winds! The term bombogenesis is usually used with extratropical systems, though tropical systems that are undergoing rapid intensification can achieve similar, and often times even greater, rates of deepening.
Cold Air Damming - CAD occurs when cold air is “dammed” against an area of higher terrain. Cold air is dense, and warm winds trying to push a cold airmass over a mountain range often have trouble accomplishing the task. As a result, cold air masses can linger upwind of higher terrain, even in the face of warm air advection.
CAPE stands for Convective Available Potential Energy and is a measure of how much energy there is in the atmosphere available for thunderstorms to tap into. For more on CAPE, check out this article: https://blog.weather.us/what-is-cape/
The CDO is the region of a tropical system near the center that has very strong thunderstorm activity. The CDO is where you can find strong winds and heavy rains that are associated with the hurricane. The very worst conditions are reserved for the eyewall, which is the innermost ring of thunderstorms surrounding a mature hurricane’s eye. Therefore, the CDO is not the strongest part of a mature hurricane, but it is the strongest part of weaker systems that do not yet (or no longer) have eyewalls.
Decoupling refers to the process where the boundary layer decouples from the atmosphere aloft. The boundary layer is the very lowest part of the atmosphere, can extend over a mile into the sky during the day. At night, however, as the turbulence generated by daytime heating dissipates, the atmosphere stratifies into smaller and better defined layers, resulting in the depth of the boundary layer shrinking. If the thin nocturnal boundary layer “decouples” from the air and winds surrounding it, the temperature inside the layer, and at the surface, can plummet as energy is lost to space, and not replaced by the kinetic action of the winds. The coldest nights observed in many areas happen when the boundary layer decouples. That’s why the very coldest mornings don’t have wind, and the actual temperature on a windy morning isn’t usually that cold, even though it might feel like it!
A deformation band is a band of heavy snow that can be found underneath a mid level deformation zone. A deformation zone is simply where winds blowing from different directions collide. This collision in the mid levels does two main things to contribute to heavy rain/snow. First, it strengthens the horizontal thermal gradient through a process known as frontogenesis (the formation of a front). With warm air moving in on southerly winds hitting cold air moving in on northerly winds, you can expect some action in the deformation zone where they meet. Second, the colliding winds pile air up in the deformation zone where they meet. With general rising motion often in place due to other factors, this air is forced to rise, and the added effects of two combined airstreams provide a significant boost to precipitation. Deformation bands can produce snowfall rates of 2-4” per hour and are often accompanied by wind gusts over 40mph leading to blizzard conditions. How long the deformation zone lingers over a given area will determine how much snowfall that area receives.
The Dendritic Growth Zone is the area in the atmosphere where dendrites (snowflakes) form. What happens in this zone (temperature, wind, humidity, etc.) will determine if we have light fluffy snow that piles up quickly or if we’re stuck with that sludge that sticks to everything and weighs a ton. The DGZ is usually located wherever temps are between -12 and -18 celsius though some times of snowflakes can form at warmer or colder temps. For maximum snowflake production, the DGZ must be deep (lots of air that’s between -12 and -18 C), it must contain air that’s rising, preferably rising quite quickly, and also must be saturated with a relative humidity of over 85%.
An ensemble is a variation on a weather model. Ensembles are created by running the same model (the ECMWF or the GFS for example) over and over again, but changing slightly the initial conditions. Shortcomings in our observation network yield errors in the initial representation of the atmosphere in the model. Such errors compound out through time. Ensembles help to show forecast uncertainty by providing a range of possible outcomes derived from running the model with a range of possibilities for what the atmosphere could be doing right now.
The eyewall of a hurricane is a rapidly rotating cluster of thunderstorms that contains the hurricane’s most vicious rain and wind. Wind speeds in the eyewall range from 75 mph in weak hurricanes to 180 mph in the strongest systems. The eyewall forms a ring around the calm center of the storm known as the eye which often features clear skies and little to no wind at all. Occasionally the storms in the eyewall can spin so fast they fall apart. When this happens, a secondary eyewall forms and takes over. This process is known as an eyewall replacement cycle, and can temporarily result in weakening of a system as the inner core sorts itself out.
A gravity wave is a feature produced by air rapidly rising through the troposphere due to either convective dynamics (thunderstorms) or due to mountains. When this air hits the stable stratosphere, it has inertia so even though there’s nothing propelling that air parcel upward, it continues in that direction due to inertia. Eventually, the parcel runs out of inertia in the same way a ball does when you throw it up in the air. It then begins to descend back into the unstable troposphere where other forces begin working to propel it back upward. Its downward momentum eventually runs out at which point it rises again and the cycle repeats with slightly less magnitude. You can see gravity waves in cloud formations that are organized as parallel lines of alternating cloud/no cloud areas. Where air is rising, it creates clouds and where air is sinking, cloud development is prevented.
An inversion occurs when the temperature of the air rises with height rather than cools. In the troposphere, as you get higher the air cools off. This is why mountains usually have more snow at the top then at the bottom and why airplanes have to be carefully insulated. Sometimes, however, there are narrow regions where the air warms with height. These are known as inversions and are responsible for some of the precip types discussed above such as freezing rain and sleet.
A jet streak is a pocket of extremely fast moving air embedded within the larger jet stream. Jet streaks can be up to a couple hundred miles long and the strongest ones can be a hundred or so miles wide. They range in intensity from 100 mph all the way up to 200+ mph in the strongest cases. While the air inside the jet streaks zooms by at well over 100 mph, the jet streak itself often only moves 20-30 mph. As a result, air parcels must accelerate into the jet streak and decelerate out of it. These accelerations and decelerations help to shape our weather in important ways, and can strongly contribute to the development of all types of storms. Jet streaks occur between 20 and 40 thousand feet in altitude and can be caused by a wide variety of factors such as air moving around/over mountain chains and the release of energy from strong, heat/moisture laden systems such as tropical cyclones.
The jet stream is a current of fast moving air between 20 and 40 thousand feet in altitude. The winds in the jet stream blow between 50 and 100 mph but isolated pockets known as jet streaks (see above for more) can feature winds exceeding 200 mph. The jet stream varies in latitude from day to day but it’s often located in the polar regions during the summer and the mid latitudes during the winter. The jet stream is often a basic demarcation between the cold polar air masses and the warm tropical air masses. Occasionally, the jet stream will split in to two branches: the polar jet and the subtropical jet. This is most common in the wintertime and so called “split flow patterns” are responsible for many of the high impact events seen in the Eastern part of the country. Split flow patterns are often dry for the West Coast as the jet stream diverts around a large area of high pressure before it moves over the North American continent either in Canada or Mexico.
The lapse rate is how quickly air cools with height. In a perfectly dry atmosphere, a parcel (basketball sized chunk of air) rising through the atmosphere will cool at a rate of 9.8C per kilometer or 5.5F per 1000 feet. In a perfectly saturated atmosphere, air cools at a much slower rate but the lapse rate in a saturated environment is not a constant. Instead, it is dependant on the emperature and pressure of each given parcel. The steeper the lapse rate, the more potential energy there is for thunderstorm formation.
The Low Level Jet (LLJ for short) is a version of the jet stream that occurs in the lower levels of the atmosphere, usually around 5,000 feet. LLJ’s are smaller in scale, shorter in duration, and weaker in intensity than upper level jets, but they can play an important part in our weather at the surface. Southerly LLJ’s can transport warm, moisture laden air northward where it can meet and clash with cold Canadian air rushing south on a northerly LLJ. The ferocious winds contained in the LLJ can be mixed down to the surface as gusts, given the right conditions. LLJ’s form in response to divergence created by upper level jet streaks. For more on LLJ’s, check out the 850mb section of the pressure level glossary, where I talk more about the LLJ structures that often form at that altitude.
National Digital Forecast Database - a series of products produced by the National Weather Service that stitch together the forecasts of local NWS offices to create forecast maps for the entire country. Note that these forecasts are produced with the input of human NWS forecasters, so are generally more accurate than model maps.
Relative humidity represents how much moisture is present in the air relative to the air’s ability to hold moisture. If there is little to no moisture in the air, especially on a hot day when the air’s capacity for moisture is very high (warm air can hold more water vapor than cold air), RH values will be very low. If the amount of moisture in the air reaches the capacity of the air to hold moisture, you will see either fog or precipitation depending on the situation. Note that if the air is cold, the capacity of the air to hold moisture is reduced and thus the raw amount of moisture contained in the air may be low despite RH values being high. This is why it never feels “humid” when it’s snowing, even though the air is completely “saturated”.
A ridge is an area of high geopotential heights that generally results in warm and dry surface weather. The geopotential height of a given pressure level (for example, 500mb) is how far above sea level you’d have to go to achieve that pressure. So how can we use geopotential heights to forecast temperature trends? The volume of a gas and its temperature are inversely correlated. The colder a gas, the denser it is and the less volume it takes up (this is due to the ideal gas law PV= nRT where V (volume) and T (temperature) are on opposite sides of the equation, thus demanding they be inversely correlated assuming all other variables are constant). The opposite is true for warm air. A warm, low-density airmass will expand, pushing the height of a given pressure level higher. The opposite is true for a dense, cold airmass that will show up as an area of lower heights.
Outside of the eyewall, thunderstorms are organized in features known as spiral bands. Spiral bands can be hundreds of miles long and often contain individual cells that produce severe weather, including tornadoes. Winds in the spiral bands are not as intense as in the eyewall and the weather often takes on a squally appearance with bursts of heavy rain and wind punctuated by calmer conditions and even sunshine. Spiral bands and their impacts can extend hundreds of miles from the center of the storm.
Sea Surface Temperatures. Sea surface temperatures will be important when we discuss the winter outlook. Areas of abnormally warm or cold water at the surface of the ocean half way across the world can be critical in determining the winter pattern here. The staying power of SST patterns are determined by the subsurface ocean temps. If the subsurface ocean temp patern match the SST pattern, it will likely stick around. Trouble can arise when sub-surface temps don’t match SST’s and the SST pattern can be tempered or even reversed as that water gradually makes its way to the surface.
A teleconnection is a connection between the weather someplace far away and the weather here. They’re often an alphabet soup of acronyms- NAO, AO, PNA, PDO, ENSO, QBO, EPO, WPO, the list goes on. They can be used to predict the weather days or even weeks in advance without models or computers that are subject to inconsistency and error. Notice how most of them end in the letter “o”. “O” stands for oscillation because most of the teleconnections oscillate between positive and negative phases with at least some regularity.
A trough is the opposite of a ridge, and has low geopotential heights, signaling a cold, high density airmass. In addition to cold air, the front side of troughs often feature active weather. As the cold, dense air associated with the trough moves east with the jet stream, it displaces warm air associated with the downstream ridge. This displacement favors rising air, which is the root cause of clouds and precipitation. The higher the amplitude of the trough, the greater the magnitude of this displacement, thus resulting in stronger lifting and stormier weather. Meridional flow regimes are associated with high amplitude troughs, which is another reason they often feature active weather compared to the shallow troughs of zonal flow regimes.
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