How to interpret a weather forecast

Whether it’s from your phone, your television screen, or on the radio, you can now get a weather forecast from more sources than ever. As a result, it’s crucial to know what different parts of a forecast mean. When I see a 50% chance of rain, what is that actually telling me? If I see a forecast for 10 days away of 60 degrees and partly cloudy, how reliable is that? Why do snow forecasts seem like they’re wrong more often than any other forecast?

Often when people see a weather forecast calling for a 50% chance of precipitation, they laugh and say something along the lines of “A 50% chance? I could have said that! It’s either going to rain tomorrow, or it might not.” Just at face value, yes, that is true; but that is completely ignoring the probabilities and science behind making a forecast. Let’s break it down step by step, going with the most basic method first. Let’s say that historically, it rains 30% of the time in New York City in May. Using that logic, I could give a forecast right now for all of May, saying that there’s going to be a 30% chance of rain every day for New York City. That’s extremely stupid, considering I’m ignoring everything that’s going on with weather patterns that are happening now and how they’ll be changing in the weeks to come.

The next step (and it is a huge step) is to use computer-generated forecasts. Not surprisingly, computing power is at an all-time high, to the point where billions and trillions of mathematical calculations are used to make a single forecast. Although they are pretty accurate, these computer forecasts are still not perfect; there is a high amount of chaos in the atmosphere. The complex processes that occur there are such that a very small change in the initial weather conditions that are put into a computer can make the difference between the computer forecasting a sunny day and forecasting a blizzard.

To account for these errors, the final step in the forecasting process is introduced: a meteorologist who can interpret all of the information that the computers are printing out, and figure out any biases in computer output so corrections can be made. A meteorologist must know how to look at weather maps and how atmospheric processes occur both physically and mathematically . Through these final two steps, the forecast that meets your eyes every day is produced. A 50% chance of precipitation (for some future point in time) means that in 50% of all of the scenarios that start with our current conditions, precipitation was produced, as determined by the combination of computer forecasts and human meteorologists.

Since I’ve said that the atmosphere is governed by rather complicated processes, it can be expected that a weather forecast will get less and less accurate the further we get from today’s date. Therefore, no one should take tomorrow’s forecast of 55 degrees and sunny with the same level of seriousness as the forecast of 10 days from now of 60 degrees and partly cloudy. That far out in time, forecasts aren’t supposed to be spot-on; however, there are certain bits of information that you can get out of a forecast that is 10, 12, or even 15 days away. Let’s say for the first seven days of your forecast, mild temperatures in the 60s are shown for your city. The remaining part of your forecast is much colder as temperatures stay in the 40s. The typical person, with no knowledge of meteorology, can say that somewhere around the eighth day of the forecast, much colder air is going to move into the region and will likely stay for a few days. Relying on that sort of information is going to be much more useful than taking to heart the high of 47 degrees with a 30% chance of precipitation predicted for nine days from now.

Forecast track of the center of Superstorm Sandy made on October 28, 2012. This perfectly illustrates the uncertainty in weather forecasting as we go out in time; note how the size of the cone increases by Day 5 of the forecast. Credit: NWS/NOAA
Forecast track of the center of Superstorm Sandy made on October 28, 2012. The cone represents the possibilities of where the center of Sandy can track. This perfectly illustrates the uncertainty in weather forecasting as we go out in time; note how the size of the cone gradually increases by Day 5 of the forecast. Credit: NWS / NOAA

Some parts of a weather forecast are much easier to predict than others. Generally, temperature is the easiest variable to forecast; changes in temperature are typically small over a local distance, and the mechanics that change temperature are usually simple to track and predict. Precipitation, while still relatively simple to forecast, can provide much more of a challenge. In the summertime, for example, isolated “pop-up” thunderstorms are common across many areas. While thunderstorms may be forecasted ahead of time, it is difficult to say exactly where they will happen, since these thunderstorms are usually only a few miles in diameter. In the wintertime, predicting snowfall amounts can be one of the toughest jobs a meteorologist will face in their career.

A prime example was seen earlier this year with the so-called “Blizzard of 2015”, where well-respected meteorologists ended up over-predicting snow amounts by as much as a foot in some places. There are a lot of advanced dynamics in the atmosphere that are evident in a large snowstorm, and sometimes their effect on snow forecasts might only be seen a day or two ahead of time. As a result, predictions for snow are least reliable when looking at your 7-day extended weather forecast, and predictions for temperature are most reliable.

After all of this hard work, it would be a shame if weather forecasts were communicated to the public in a way that misled them. Unfortunately for some very popular weather companies, this is exactly the case. The Weather Channel, for example, has a 10-day forecast on their website that displays temperature, wind speeds, and precipitation. As pointed out by a recent article on The Vane, there’s nothing wrong with that idea, except that the winds and precipitation shown are only for the daytime hours!

10-day forecast

The above picture seems like a normal forecast, from the perspective of a typical person. “Look at Wednesday: 0% chance of precipitation. That means a dry day, and I can finally plan outdoor activities! Oh wait, what is that orange banner on the top right corner?……”

10-daynight forecast

That’s right: on that week, there was a snowstorm forecasted for Wednesday night! Few people would have had a clue, though, had they not clicked on Wednesday’s forecast. And why should they click on it? Partly cloudy with a high of 48 and a low of 29 with 0% chance of precipitation is as typical as a late February forecast can get for North Carolina. Considering 3-5 inches of snow can be enough to cause serious travel problems, The Weather Channel needs to do a bit more on their forecast website pages to alert users to extreme weather.

AccuWeather does not escape blame, either. For years, their forecast range outlasted other major weather companies with 15-day forecasts. Over time, that has grown into their present 45-day forecasts available on their website, like the one shown below. AccuWeather has their own reasons for making this sort of change, which they detail in this tucked-away blog post on their website, but they don’t really make sense. If the purpose is to see general trends, why does the forecast include specific information like maximum UV index and hours of precipitation? What is the average person going to think when they see a forecast for 45 days away showing periods of rain and a high of 54? That sounds like a forecast for the upcoming weekend, perhaps, but not for a month and a half away. In my opinion, a forecast that specific for 45 days from now has no real value to it. Any long-term temperature trends like the ones I’ve discussed above are unforseeable that far into the future, even by people who do seasonal forecasting as a career.

accuweather

Oh, and those weather apps for your phone that everyone wants to think are perfect? Those forecasts are computer-generated. Do you think a meteorologist has enough time to make a forecast for every small town, such as Fairfax, Oklahoma or Eden, Idaho? Since there are so many locations and everyone wants a super-local forecast, computer-generated data has to be used to satisfy everyone’s needs. This is why mobile weather apps should not be used for exact accuracy during important weather events (although many of them will show if the National Weather Service has issued an alert for your area, such as a Severe Thunderstorm Warning).

All of the ideas above have hopefully improved your understanding of how to interpret a weather forecast. Yes, sometimes we are wrong, and we try to be accurate as often as possible; but if more people have a basic understanding of the science behind weather forecasting, then that will allow for clearer communication between scientists and the general public, and take away from some of the misguided anger when a forecast goes wrong. This clear communication has potential to save lives when extreme weather hits, which is what’s most important after all.

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