What is forecasting?

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What is forecasting? This might seem like a silly question, but it's an important one! Of course, to forecast means to make a prediction about the future state of something. So, in weather forecasting, we're trying to predict the future state of the atmosphere (duh!).

But, what exactly are we trying to forecast? That question could have a variety of answers. It depends on the atmospheric variable(s) that you are trying to forecast and what exactly you want to know about those variable(s). Indeed, before making any weather forecast, we have to start with a few important questions:

1. What do I want to know (what variables am I forecasting)?
2. What time period am I interested in?
3. What geographic region am I interested in?

Answering these questions helps you to define the objective(s) of your forecast. These questions can have many answers, and thus, many different types of weather forecasts exist. But, before we address these questions, we have to cover a basic distinction that you're already aware of -- the difference between deterministic and probabilistic forecasts.

• A deterministic forecast is one in which forecasters provide only a single solution. For example, "tonight's low will be 31 degrees Fahrenheit," or "0.46 inches of rain will fall tomorrow."
• A probabilistic forecast is one in which forecasters convey uncertainties by expressing forecasts as probabilities of various outcomes. For example, "the probability that tonight's low will be below 32 degrees Fahrenheit is 40 percent," or "the probability of receiving at least 0.25 inches of rain tomorrow is 60 percent."   

I'm sure you have encountered both classifications of forecasts before. The daily high and low temperature forecasts produced by the National Weather Service, most private forecasting companies, and your local TV weathercaster are usually deterministic. Probabilistic forecasts, on the other hand, are more commonly produced for precipitation (for example, "tomorrow's chance of rain is 80 percent"), but the Storm Prediction Center also uses a probabilistic approach for their convective outlooks (see the tornado, damaging wind, and hail forecast graphics in this example from May 20, 2019).

A forecast for nearly three feet of snow on day 6? Good luck with that!

Credit: David Babb

In many cases, probabilistic forecasting is a more realistic approach because it allows forecasters to acknowledge and express the fact that the forecast contains uncertainty (and just about all weather forecasts do contain some degree of uncertainty). But, despite their shortcomings, deterministic formats still rule the day in the form of the five, seven, or even 10-day "tombstone forecasts" that you see on TV (see the example on the right) or on many weather apps.

Some private forecasting firms produce similar deterministic forecasts out a few months into the future, which is a completely ridiculous format for that length of time. Such deterministic forecasts imply that the forecaster has the exact same confidence in the first day of the forecast period as he or she does in the 50th day, for example (which isn't true), and the accuracy of the exact forecast values that far into the future is laughable. If you're interested in reading more about the differences between deterministic and probabilistic forecasts, check out the links in the Explore Further section below.

Forecasting Issues of Space and Time

Now that we've discussed probabilistic and deterministic forecasts, we can deal with those key questions that I mentioned at the top of the page. Think about it: The format of your forecast and the variables that you forecast will differ based on what you want to know (as well as when and where). If you're forecasting for a sailing race between Chicago, Illinois and Mackinac Island, Michigan, you need to predict weather conditions (especially wind and threats of thunderstorms) along your race path for more than a day. On the other hand, if you just want to know whether the afternoon will be hot enough to take the kids to the pool, you're interested in the afternoon weather conditions (particularly temperature, sky conditions, wind, and chance of precipitation) at a single specific location for a time period of a few hours.

To account for the variety of variables, geographic areas, and time periods that forecasters may be interested in, forecasts take on a variety of formats. The most common are point forecasts (or zone forecasts) and areal forecasts.

• Point forecasts, as you might have guessed, are forecasts valid at a given point in space (or a very small area), such as your backyard, neighborhood, or zip code. When you look at daily forecasts (which often include high and low temperature, probability of precipitation, amount of precipitation, and wind speed) from the National Weather Service or most private forecasting companies, you're looking at point forecasts.

  Another twist on a point forecast is a "zone forecast."  The forecast format is similar to that of a point forecast, but instead of the forecast being valid for a single point (or very small area), it's valid for a "zone"--an area with similar topographical and climatological characteristics (usually the size of a county or part of a county). In the modern era of automation in forecasting, point forecasts are the norm because computers are able to interpolate gridded computer model output to many specific points. Thus, zone forecasts are being utilized less and less.

• Areal forecasts are typically issued for individual forecast parameters over a much larger area (portions of states, or entire regions of a country, for example). Because of the size of the forecast area, it's common to only view one forecast variable at a time (an areal forecast for precipitation, for example).

You're already quite familiar with areal forecasts, too, I'm sure. The example below is a Day 1 quantitative precipitation forecast (QPF) map from the Weather Prediction Center.

The 24-hour QPF map valid from 12Z Wednesday, October 28, 2020 through 12Z Thursday, October 29, 2020 indicated several swaths of very heavy rain in the eastern half of the United States. Such forecasts are examples of areal forecasts for a single variable (amount of precipitation in this case).

Credit: Weather Prediction Center

During the 24-hour period covered by this forecast, a large area of the central and eastern U.S. was predicted to receive at least 0.01 inches of rain (light green shading). Successive colors (darker greens, blues, purples, and reds) indicate areas of heavier rain, with a few areas predicted to receive at least 3.00 inches of rain during the 24 hour period (associated in part with Hurricane Zeta). Although areal QPF maps are quite common, other regularly published areal forecasts include snowfall accumulation maps, as well as daily Convective Outlooks from the Storm Prediction Center (SPC).

Of course, in addition to the point or area that we're forecasting for, we also need to define what time span we're interested in. We can forecast for the very near future (perhaps a few hours), or produce short (1-2 days), medium (3-7 days), or long-range forecasts (you may find varying definitions of these forecast ranges, but these will give you the basic idea). Obviously, as we move from the short range, to the medium range, to the long range, the types of forecasts that we can reasonably make change, because the uncertainties become greater. For example, check out the format of the medium and long range temperature and precipitation forecasts produced by the Climate Prediction Center. They merely identify areas (probabilistically) where temperatures and precipitation will either be above, below, or near long-term averages for the given time period.

So far, I've just scratched the surface in describing various types of forecasts because I want you to realize that forecasting can be a very broad field! Different forecasting scenarios will have different objectives, so answering the three questions near the top of the page is important any time you begin to make a forecast. Check out the WxChallenge Application section below to better understand our forecasting focus in this course.