LITMIR.BIZ

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Powerful as they are, the big national and regional forecast models don’t always pick up the fine details of the landscape such as nearby lakes and small mountain ranges that can affect local weather. An individual local forecaster’s knowledge and skills fill in these gaps.

       Observing current conditions. Pictures of local conditions are a big part of the daily weather shows. Observing local conditions is not as important to local weather forecasting as it was before computer forecasting models got so good. Still, it is an important part of any forecasting process. When local weather becomes hazardous, observing current conditions becomes critical. This method is most important for short-range forecasting (deciding how weather will change in the next 6 to 12 hours), and for nowcasting, predicting weather for the next one minute to 4 hours.

      What is going to happen in this area during the next 12 hours? Forecasters have to try to answer this question every time they issue a new regularly scheduled public forecast. (Imagine how it must be to have your professional judgment about the future put to such a public test so often!)

      For special short-range forecasts, such as impending severe thunderstorms, a local forecaster is a busy person. In the United States, the National Weather Service Storm Prediction Center issues “watches” that alert the public to the possibility of trouble. But then it’s up to the local forecasters to keep a close eye on things. On the lookout for damaging hail, local flooding, or tornadoes, forecasters rely heavily on local radar observations and use their own training, experience, and understanding of local conditions to decide whether to issue a public warning. (The section “We interrupt this program …” explains the different watches and warnings.)

      PLAYING THE SPREAD

      High-powered computer models solving numerical weather prediction equations are a vast improvement in accuracy from the early days of forecasting, no doubt about it, but they are not perfect. As Ed Lorenz and the “Butterfly Effect” make clear, the atmosphere is inherently chaotic. The slightest error in defining the state of the atmosphere can lead to big effects down the road. You can’t get a 100 percent perfect rendering of the state of the atmosphere, and you can’t get a 100 percent perfect solution to the equations. What you get at the end of a single run of the numerical equations is not a forecast of absolute certainty, but rather a “best fit.”

      Dealing with this perpetual state of uncertainty, weather scientists have developed another way to tackle the problem. Rather than running a single numerical model with a single set of initial conditions, they run more than one model, and they run them more than once and on different models — tweaking the incoming data ever-so-slightly to get a slightly different result. This method is known as ensemble forecasting, and it produces a broader range of possible weather outcomes. The output of ensemble forecasting produces a variety of forecasts with different probabilities — like the odds at a racetrack. Users are able to see which outcome is the most likely, the favorite, and which is the longshot, and place their bets accordingly.

      It represents a big improvement: being able to measure the level of uncertainty in a forecast. An ensemble forecast with a wide trail of possibilities inspires less confidence than an ensemble with a narrow trail. Check out the forecast the next time a hurricane is threatening landfall somewhere. Most likely, you will see a band of approach much wider than the storm. The width of the band is the footprint of ensemble forecasting.

For forecasts beyond the 12-hour period, and especially for general “outlooks” of weather beyond the next three to five days, the mix of forecasting tools may change. The projections of a variety of computer models still are consulted, but the climatology of a place — its average weather for this time of year — is weighed more heavily. This approach focuses on the question, “What usually happens here this time of year?”

      Take what is happening now …

The weather forecasting process begins with the need to know what the atmosphere is doing now. This information is the data that describes what computer modelers call the “initial conditions” that are the starting points for their powerful forecast software. You might think of it as a nowcast. This information has to be as absolutely accurate and as detailed as possible. Forecast modelers know that the slightest error in their description of the current state of the weather can fairly quickly lead to large errors in the forecast. If the data describing initial conditions is a little wrong, the forecast can be very wrong. (That’s what “The Butterfly Effect” later in this chapter is all about.)

      Weather forecasting is such a complicated and difficult business because it is trying to predict the behavior of a system that has many features that are all changeable, all the time. Observations of every sort need to be gathered. How warm or how cold is it? Which way is the wind blowing? Is it cloudy, or is it clear? Details about all these features and more are gathered from every source available.

      From the ground, these measurements come from human weather observers and from instruments on automatic weather stations. From the air, they come from weather balloons and airplanes and satellites orbiting the planet in space. From the sea, they come from ships and from instruments on anchored moorings and drifting buoys, as well as satellites. (See Chapter 16 for more about these measurement tools.)

      Every day, 24 hours a day, data from thousands and thousands of weather observations around the world is streaming electronically into National Weather Service computers and other major weather centers around the world. With these millions and millions of bits of data, the computers are constantly updating and refining their highly detailed descriptions of the current state of the weather — the nowcast.

      WHAT’S IN A NAME?

      When you think about it, meteorology is a funny word for weather science, isn’t it? A meteor is an object from space, usually a tiny bit of comet dust, that leaves a flash as it burns up in Earth’s upper atmosphere. What exactly does that have to do with weather? The answer is, exactly nothing!

      But Aristotle, the great Greek philosopher, didn’t know all of this at the time he first used the word meteorology back around 350 B.C. Everything that happened above the Earth was considered astronomy in those days, and Aristotle was trying to define a new science. Astronomy was the study of all the stuff that goes on in the distant heavens, he figured, and meteorology was the study of the stuff that happens closer to Earth.

      The Greek word meteoron means something that falls from the sky. (Hence, the phrase: “Don’t be such a meteoron.”) Anyway, what Aristotle had in mind, mostly, was weather, the study of rain and snow and hail. But he also threw in such things as comets and earthquakes. Go figure… .

Later, of course, comets were given back to astronomers, and earthquakes became part of the science of geology.

      … And add a little future

      Computer models, or software programs, of numerical weather prediction divide the atmosphere over the surface of the Earth and above it into imaginary individual blocks, or gridpoints. Different models use different techniques to force changes in their virtual atmospheres. Some are better at depicting the progress of one type of weather change, and some are better at others.

      

Each of these separate blocks of air has features, such as temperature, pressure, wind, and humidity, with their own values. The computers take the data of all these values from all these blocks of air and apply a set of equations that represent basic physical laws. (No, I know you must be deeply disappointed, but the only equation you are going to find in this book is 1 Weather For Dummies = 0 equations.)

      Anyway, the output of this incredibly intensive process of computation produces

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