At Marine Weather Center, a client recently asked why the computer model forecast predicted small seas on the windward side of Saint-Martin, when what was really happening were strong winds and large seas. The answer had everything to do with how weather models are designed, how they work and how they don’t.

Weather forecast models are ubiquitous for good reason: They provide precise forecasts for weather information, such as wind, waves, temperature and precipitation that we’re looking for, at precise times and locations—or so it would seem. But weather models have limitations, and understanding these limitations will help you better interpret their forecasts and be a safer boater.

What is a Weather Model?

Weather models break the Earth down into grid boxes, with each grid box representing a specific area on Earth’s surface and the atmosphere above. Forecast models start by approximating the current state of the atmosphere, using recent observations from satellites, airplanes, weather balloons, buoys, airports and other land-based observations, as well as from ships at sea. If recent observations are not available, the model interpolates to “guess” an initial value for all weather parameters, in all grid boxes.

Once the model approximates the current state of the atmosphere, it performs mathematical calculations to predict the state of the atmosphere, sea surface and land surface at future forecast times.

In general, the smaller the grid boxes, the better the forecast. But, more grid boxes require more computing power, and even today’s best supercomputers cannot generate forecasts for enough grid boxes to resolve (and accurately predict) many weather phenomena—especially convective thunderstorms, tornadoes, hurricanes and other severe weather.

Also, guesses about the initial state of the atmosphere introduce uncertainties and inaccuracies, which can be amplified at future forecast times. To compensate for this, many global models run “ensemble” forecasts that assume different initial states of the atmosphere. The U.S.-based Global Forecast System model assumes 30 different states. The European Center for Medium-Range Weather Forecasts assumes 50. Each then runs a set of streamlined calculations to yield, respectively, 30 or 50 different forecasts.

Ensemble forecasts are typically averaged, resulting in an “ensemble mean” forecast, which can provide additional insight into what might happen if the “operational” (or primary) forecast is not correct.

The Space-Time Factor

Weather is fluid—it changes over time and distance. Sometimes, weather changes are subtle, gradual or linear, changing at a constant rate; sometimes, changes are dramatic, sudden or at a variable rate. Weather models handle the former well, but they lack the temporal (time) and spatial (size and location) resolution to predict many dramatic or sudden changes, as well as those that occur at a variable rate.

Spatial resolutions are determined by the size of the grid boxes that output forecast data. The U.S.-based GFS model spatial resolution is about 8 miles, so each grid box covers about 64 square miles. The European model has a finer spatial resolution of just under 6 miles; thus, each grid box is a bit less than 36 square miles.

In terms of time, both have a temporal resolution of one hour for short-range forecasts, out to 120 hours, or five days, for the U.S.-based model, and 90 hours, or just under four days, for the European model. That resolution drops for each as they extend to 16 days for the U.S.-based model and 10 days for the European model. In both models, there is only a single forecast for each weather parameter (wind, temperature and so forth) within each grid box, and only a single forecast for the top of each hour for each grid box.

If a weather event—such as an increase in wind speed, change in wind direction, or decrease in temperature—occurs over an area larger than the resolution of the model (one grid box), then the model has some chance of properly predicting the event. Similarly, if such a change occurs over a period longer than one hour (within the 90 or 120 hours that the model has a fine temporal resolution), then the model has some chance of predicting the event. But, often, that’s not what happens in the real world.

Temporal Limits

Let’s focus on temporal resolution for a moment. If the wind increases from 10 knots at 6 a.m. to 15 knots at 7 a.m., peaks at 20 knots at exactly 8 a.m., then abates to 15 knots at 9 a.m. and decreases to 10 knots at 10 a.m., then the model might provide a fairly accurate forecast. The graphic below shows this, with the forecast model (blue bars) describing the actual wind (red curved line) fairly accurately, although it slightly under-forecasts increasing wind and over-forecasts decreasing wind.

But what if the wind increases from 10 knots at 6 a.m. to 12 knots at 7 a.m., then triples to 36 knots at 8:30 a.m., before settling to 14 knots at 9 a.m. and decreasing to 10 knots at 10 a.m.? Assuming that the rate of change from 7 a.m. to 8:30 a.m. is linear, the model’s 8 a.m. forecast of 28 knots could be about right at exactly 8 a.m. But what about at other times? The graphic below shows that the forecast model (blue) is a poor predictor of actual wind (the red curved line), missing peak wind badly, as well as under-forecasting increasing wind and over-forecasting decreasing wind.

In these examples, we assume the forecast model predicts wind perfectly—all of the inaccuracy is due to the model’s temporal resolution limitation. But if you were boating in this weather, how would you rate the accuracy of this model?
Just imagine if we added model inaccuracy into this scenario.

Spatial Limits

Remember that boater who asked about the inaccurate forecast on the windward side in Saint-Martin? The reason is that within about 5 miles on the windward side of the island, the grid box includes protected waters where seas are small, and that’s the forecast for the entire grid box. To get a better idea of actual seas on the windward side of the island, a boater would be better off using the forecast for a bit farther from the island, to ensure that the entire grid box is over open waters.

Grid size and shape matter. Today’s best models use different shapes; the U.S.-based model uses cubed spheres, the European model uses an octahedral grid, and the experimental Model for Prediction Across Scales uses a hexagonal mesh. You can’t assume the forecast grid box covering your area is square in shape.

Weather events occur at different scales as well. Pressure-gradient-driven wind—blowing generally from areas of higher pressure toward areas of lower pressure—occurs at a scale of hundreds or thousands of miles, is relatively uniform and linear, and persists for long periods of time. Computer models handle this sort of weather well.

But convection—vertical motion of air due to temperature differences within the air mass—occurs at a scale of hundreds of feet to a few miles, and may persist for only minutes to a couple hours. Today’s global forecast models do a poor job predicting convective thunderstorms and other severe weather, in large part because they lack the spatial and temporal resolution to resolve these events.

Additional Factors

Earth is big. The grid boxes on our best global models are smaller than 40 square miles in area. The U.S.-based model predicts more than 100 different weather parameters (wind, pressure, temperature and so forth) at 127 different vertical altitudes for 173 forecast times. That’s more than 10 trillion forecast values.

Today’s best supercomputers run these calculations in about an hour. (The U.S.-based model runs on a pair of supercomputers that, as of late 2022, were the 49th and 50th fastest in the world. The European model runs on the world’s fourth-fastest supercomputer.) The European model estimates that in the next decade, maybe by 2030, a combination of different grid box schemes and improved supercomputing capabilities may allow less-than-1-mile-resolution global models.

Forecast models and computing power are constantly evolving. The U.S. and European models undergo major revisions every few years (for instance, in 2021, the U.S.-based model increased from 64 to 127 vertical layers) with minor revisions in between. And, global model spatial resolution has been doubling every few years.

In the meantime, to better predict thunderstorms and other severe weather, many government meteorology agencies generate shorter-term, higher-resolution models offering, in some cases, less-than-1-mile resolution out to about two days. Typically, these are for coastal and near-shore waters only.

Remember that when you query any computer model forecast, the answer you’re looking for is: What is the weather going to do over a continuous timeline in my location? But the answer the model gives you is: Here is this model’s best guess about the weather in this geographic area, and just at these specific times.

Finally, don’t get caught in the “precision trap.” We often assume that if something is more precise, then it is more correct.Computer model forecasts are precise, but they can be precisely wrong. I always recommend that boaters use computer model forecasts in conjunction with forecasts from a local meteorological office or a private forecasting service. 

This article was originally published in the July/August 2023 issue.