Last week, I discussed several of the challenges in making a snowstorm forecast. They include: 1) many of the physical processes that govern the atmosphere act non-linearly, 2) uncertainty about the initial state of the atmosphere, 3) certain part of a model’s physics have to be approximateds.
Today, I’ll walk you through the additional three factors which can lead to bad forecasts. They are:
* There is often more than one stream of flow that has to be handled correctly by the models
* The ocean to our east and mountains to the west
* Forecasters may make poor decisions
There is often more than one stream of flow that has to be handled correctly by the models
The ingredients needed to get a snowstorm are often governed by more than one stream of flow and can be impacted by what is happening both upstream and downstream of the approaching storm.
In the figures above, note the differences in the surface pressure patterns to the northeast of the storm (over western Pa. in left panel, northern Al. and Ga. in right panel).
In the right panel, the pressure gradient (change in pressure over some distance) implies that surface winds are still from the northwest (see arrow) over New England as there is a strong low located near the Canadian Maritimes. That cyclonic circulation helps to force the storm approaching the East Coast to take a southerly track.
On the left panel, the low is weaker and farther to the east allowing the winds across New England to be southeasterly (see arrow). Without the north winds over New England and the strong low over near Nova Scotia, the low approaching the East Coast has more room to come northward and develop.
Upper air challenges
On the bottom panel, the ridge is located much farther west (near the West Coast) than on the top panel (over the Rockies). It also has two distinct upper level impulses that have not yet phased (merged together) and it therefore has a weaker low located farther off the coast than solution shown on the top panel. By contrast, the top panel with more eastward location of the ridge has essentially phased the two upper level disturbances producing a sharper upper level trough which produces a strong low that is tucked in closer to the coast.
In the above maps, the ECMWF forecast (the top two panels) was forecasting a major snowstorm while the GFS (bottom two panels) was predicting a near miss. The more upper level features that are in play as a potential storm approaches, the tougher it is for the models to get the forecast right.
The ocean to our east and mountains to the west
Our proximity to mountains to the west and the ocean and Gulf Stream to the east also complicates our forecasts.
Most of our potential snow storms track across the southeast and then turn up the coast. They therefore usually tap into some of the air coming northward from near the Gulf Stream setting up a very tight thermal gradient (how quickly the temperature changes as you move across the front).
Having a strong frontal boundary along the coast is a double edged sword. If you get a favorable track there, lots of energy is available to crank up a storm. However, it also means that there is plenty of warm air nearby that can mess up a forecast with a slight deviation in the storm track.
In the image shown above right, if you shift that center of the low to the west a little, it would introduce freezing rain or rain where heavy snow actually fell. Shift the storm track a little east and there would have been no mixing problems east of I-95, where snow changed to sleet for a time.
Most major storms are associated with a very tight temperature gradient so D.C. is usually right near the rain-snow line. Any small last minute shift in the storm track can make a forecaster look really foolish.
Whereas the oceans supply warm air that can mess up a forecast, the mountains help promote cold air damming that tends to keep low level cold air across the area longer than it would last without them there. It’s often tricky to determine how long the cold air will stick around before being eroded by warmer air from the ocean that might trickle in (see above).
Cold air damming requires cold high pressure to our north. The presence of the high results in cold flow from the north at low levels. The mountains then essentially act as barrier, keeping cold air trapped to their east. Because the cold air is dense and difficult to dislodge, sometimes it can linger longer than forecast by the models leading to unexpected icing problems.
Just as the mountains can be conducive to wintry weather, they can also impede it. When flow is from the west and northwest, the mountains also lead to downsloping winds. These winds produce drying when a storm tracks just to our north, cutting off moisture, even if the temperatures are cold enough to support snow.
Meteorologists may have bias or misinterpret the data.
The most common reason meteorologists err in their forecasts is misjudging the probabilities associated with a storm especially in those tricky situations where there is no consensus among the models.
Failure to communicate probabilities
Sometimes the errors are a result of hubris in trying to make a deterministic single forecast during an iffy forecast situation. The general public wants a best guess so we try to provide that. However, if we fail to a good job describing the uncertainty of the forecast situation, we can really get burned. That certainly was the case during the infamous Decenber 26 non-storm last year.
Despite cloaking the Dec. 26 forecasts in probabilistic terms, I made the mistake of saying I was “bullish” about accumulating snow giving a false impression of the certainty of the forecast. The CWG forecasters were then slow pulling away from the snowy solution even though radar and satellite were indicating that the heavier clouds and weather to our southwest were moving more eastward than northward. For an in depth discussion of what went wrong with that forecast, click here. In that case CWG forecasters were afraid to write off the storm because of the rapid changes that sometimes occur to the precipitation shield during snowstorms (the 1987 Veteran’s day storm comes to mind).
Overreliance (and underreliance) on models
Sometimes meteorologists bust in the shortest time ranges by relying too much on models. However, those same models are powerful tools that also correctly predicted this year’s October snowstorm along the East Coast and suggested that the precipitation would linger longer than indicated by extrapolation of the radar images. It’s very doubtful that anyone would have forecast such a storm without the computer simulations.
Human psychology and emotions can also sometimes lead to forecast mistakes. For example, if a forecaster predicts that a storm will miss the area and then the area is gridlocked due to that same storm he or she is often lambasted with severe criticism from the media and general public. The next time a similar looking storm appears on the models he or she might let their recollections of the previous bust creep into their forecast.
While it’s important to learn from forecast mistakes, it’s also possible to be blinded by them. The CWG forecasters guard against that by sharing forecast ideas with other members of the team prior to issuing a forecast.
Forecaster bias (wishcasting and hero syndrome)
Most meteorologists quickly learn to reign in any bias that might result from their love or hatred of snow. However, a few suffer from the hero syndrome (no Capital Weather Gang forecaster) wanting to be first to call for a major snowstorm based on one or two model runs when the storm is still several days from actually occurring. Trumpeting such a forecast is usually a mistake that implies more skill than actually exists in the longer time ranges. The same forecasters may then sometimes be slow to back away from their deterministic forecasts.
Anytime you hear someone calling for a major snowstorm 4 or 5 days in the future, view it with lots of skepticism. The only sane way to forecast any storm is by assessing the probabilities of a storm and then conveying the probabilities to the public. That is why the Hydrometeorological Prediction Center routinely issues probability forecasts for various snowfall amounts and why the CWG team also tries to provide probabilistic forecasts of a storms potential.
Next time you get ready to wail about a bad forecast, think about all the different ways that a forecast can go wrong...