Why are clouds so difficult to predict?
Clouds are one of the most difficult weather parameters to predict even a few days in advance, let alone more than 10 days.
It's not just large storm systems that create extensive cloud cover. Those clouds are relatively easy to predict especially within a few days. Small, weak disturbances in the atmosphere can also form clouds, even if they do not have enough moisture to cause precipitation. Meanwhile, skies could change from completely cloudy to completely clear within 50 miles, while model forecasts for the front's location could double as much as a day or two ahead.
The challenge is that cloud cover often depends on processes occurring in the atmosphere at very small scales—too small to have sufficiently detailed data models to accurately resolve them.
Why do I need to know what a 'group' is?
In general we try to keep technical jargon to a minimum. However, for those of you invested in this forecast, it's worth learning what “team” is and why it matters.
There are two main flavors of forecasting models: deterministic and ensemble.
A solid model is the forecast you're used to seeing. This is a single-solution forecast: temperature is X, chance of precipitation is Y, and cloudiness is Z. Such forecasts are usually very accurate two to three days in advance.
For forecasts ranging from several days to two weeks, most meteorologists prefer to look at ensembles. These are essentially multiple runs of a firm forecast. In each run, or simulation, the initial weather observations fed into the models are slightly modified to account for imperfections in both the observations and the models. By analyzing similarities and differences between multiple simulations, forecasters can get a better idea of the range of possible weather outcomes and their probabilities and overall forecast confidence.
What does the solid forecast for April 8 show?
The image above is a deterministic model forecast for the afternoon of April 8th. It shows lots of clouds along most of the path of totality, about 115 miles wide, stretching across the United States from Texas to Maine. It could be completely right or completely wrong. It's still too many days to base anything from a firm forecast on.
With clear skies over Texas and increasing clouds in the Northeast, it's similar to the weather — or what the average cloud levels will be this time of year. However, there are pockets of clear skies in the northeast, and off the east coast, linked to a predicted area of nearby high pressure.
What do clusters represent?
The figure above shows an ensemble forecast of atmospheric pressure, using the average of predicted pressure from 30 simulations from the US Modeling System. Generally, areas of low pressure – shown in shades of blue – are cloudy, while areas of high pressure – shown in yellow and orange – are sunny.
Some individual simulations suggest low pressure and thus cloudy skies across a significant portion of the path of totality. There is some high pressure and sunshine to the east or northeast of the track of totality, but more low pressure is moving to the west of the track of totality, especially in areas north of Texas.
The cloudiness forecast shown at the top of this article suggests that the low pressure area is far enough north of Texas that if the simulation of the location of the low pressure is correct, cloud cover may be low.
Of course, this far away, not only is there less confidence in the presence and location of weather systems, but models can be a day or two too slow or too fast in how the systems will progress across the country.
ExcardaAn artificial intelligence weather forecasting start-up has launched Solar Eclipse Tracker Provides cloud projections along the path of the eclipse.
The company is one of several that have developed AI weather models, which create forecasts by learning to recognize patterns in historical weather data, whereas traditional models crunch complex mathematical equations that represent the physics of the atmosphere.
“This allows for highly accurate, hourly, global forecasts to be made in minutes instead of hours,” Vivek Ramavajjala, CEO and founder of Excarda, said in an email. “We can also take advantage of the improved speed and cost to produce ensembles of weather forecasts, which is critical in quantifying forecast uncertainty beyond a few days.”
A Press release The company says its predictions are 20 percent more accurate than traditional models.
Here is the AI model's current forecast for several cities along the path of totality during the peak eclipse:
- Dallas: 53 percent cloudy (+/-20 percent uncertainty).
- Little Rock: 58 percent cloudy (+/- 15 percent uncertainty).
- Indianapolis: 54 percent cloudy (+/- 19 percent uncertainty).
- Cleveland: 55 percent cloudy (+/- 19 percent uncertainty).
- Buffalo: 53 percent cloud (+/- 19 percent uncertainty).
- Burlington, Vt.: 47 percent cloudy (+/- 22 percent uncertainty).
(The uncertain figure, for example, predicts that Dallas will have 33 to 73 percent cloud cover.)
Is the forecast off to a good start for those hoping for clear skies? Not exactly. But we're still a few days away from taking any cloud forecast too seriously, and forecast confidence won't be very high until April 8th.
The Washington Post will publish its own eclipse cloud forecast tracker on Friday, so stay tuned.
