Why are clouds so difficult to predict?
Clouds are one of the most difficult weather parameters to forecast, even just a few days ahead of time, let alone more than 10 days out.
It’s not just large storm systems that produce extensive cloud cover. Those clouds are relatively easy to forecast, especially within a few days. Smaller, weaker disturbances in the atmosphere can generate clouds as well, even when they don’t have enough moisture to make precipitation. Meanwhile, skies can transition from completely cloudy to totally clear within just 50 miles or so of a weather front, while model forecasts for the location of a front can be off by twice that much a day or two beforehand.
The challenge is that cloud cover often depends on processes happening at very small scales in the atmosphere — small enough that models don’t have enough detailed data to accurately resolve them.
Why do I need to know what an ‘ensemble’ is?
Normally we try to keep the technical jargon to a minimum. However, for those of you invested in this forecast, it’s worth learning what an “ensemble” is and why it matters.
There are two main flavors of forecast models: deterministic and ensemble.
A deterministic model is the forecast you’re used to seeing. It’s a single-solution forecast: The temperature will be X, the chance of precipitation is Y and the cloud coverage will be Z. Such forecasts are typically most accurate up to two to three days beforehand.
For forecasts several days to about two weeks in advance, most meteorologists prefer to look at ensembles. These are essentially multiple runs of a deterministic forecast. In each run, or simulation, the initial weather observations fed into the models are slightly tweaked to represent imperfections in both the observations and the models themselves. By analyzing the similarities and differences among the multiple simulations, forecasters can get a better idea of the range of possible weather outcomes and their probabilities, and of the overall forecast confidence.
What is the deterministic forecast showing for April 8?
The image above is a deterministic model forecast for midday on April 8. It shows lots of clouds across a large portion of the path of totality, the roughly 115-mile-wide swath that stretches across the United States from Texas to Maine. It could be totally right, and it could be totally wrong. It’s just too many days away to base anything off a deterministic forecast.
What it shows is similar to climatology — or what average cloud conditions are around this time of year — with clearer skies over Texas and increasing clouds to the northeast. However, there are pockets of clear skies over the Northeast, too, tied to a predicted area of high pressure nearby, off the East Coast.
What are the ensembles showing?
The image above shows an ensemble forecast of atmospheric pressure, using the average of the pressure predicted from 30 simulations from the American modeling system. Generally speaking, areas of low pressure — shown in shades of blue — tend to be cloudier, while areas of high pressure — shown in yellow and orange — tend be sunnier.
Quite a few of the individual simulations suggest lower pressure and thus cloudier skies across a significant portion of the path of totality. There are some that hint at higher pressure and sunnier skies for eastern or northeastern parts of the path of totality, but still show lower pressure moving into western parts of the path of totality, especially areas north of Texas.
The cloud cover forecast shown at the very top of this article suggests the low pressure area is far enough north of Texas that cloud cover may be limited if its simulation of the location of the low is correct.
Of course, this far out, not only is confidence in the presence and location of weather systems low, but the models could be up to a day or two slow or fast on how the systems will progress across the country.
Excarta, an artificial intelligence weather forecasting start-up, has launched a solar eclipse tracker providing cloud forecasts along the path of the eclipse.
The company is one of several that have developed AI weather models, which make 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 us to produce highly accurate, hourly, global forecasts in minutes instead of hours,” Vivek Ramavajjala, CEO and founder of Excarta, said in an email. “We can also exploit the improved speed and cost to produce ensembles of weather forecasts, which are crucial in quantifying forecast uncertainty beyond a few days out.”
A news release from the company says its forecasts can be as much as 20 percent more accurate than traditional models.
Here is the AI model’s current forecast for several cities in the path of totality at the time of peak eclipse:
- Dallas: 53 percent cloud cover (+/-20 percent uncertainty).
- Little Rock: 58 percent cloud cover (+/- 15 percent uncertainty).
- Indianapolis: 54 percent cloud cover (+/- 19 percent uncertainty).
- Cleveland: 55 percent cloud cover (+/- 19 percent uncertainty).
- Buffalo: 53 percent cloud cover (+/- 19 percent uncertainty).
- Burlington, Vt.: 47 percent cloud cover (+/- 22 percent uncertainty).
(The figure for uncertainty means that, for example, it predicts Dallas could have anywhere between 33 and 73 percent cloud cover.)
Is the forecast off to a great start for those hoping for clear skies? Not exactly. But we’re still at least several days away from being able to take any cloud forecast too seriously, and forecast confidence may not be terribly high until just a day or two before April 8.
The Washington Post will launch its own eclipse cloud forecast tracker on Friday, so stay tuned.
Dr. Sarah Adams is a scientist and science communicator who makes complex topics accessible to all. Her articles explore breakthroughs in various scientific disciplines, from space exploration to cutting-edge research.
