Weather Forecasting

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Weather Forecasting

Weather forecasting is the attempt by meteorologists to predict the state of the atmosphere at some future time and the weather conditions that may be expected. Weather forecasting is the single most important practical reason for the existence of meteorology as a science. Knowing the future of the weather can be important for individuals and organizations. Accurate weather forecasts can tell a farmer the best time to plant; an airport control tower what information to send to aircraft that are landing and taking off; and residents of a coastal region when a hurricane might strike.

Humans have been looking for ways to forecast the weather for centuries. The Greek natural philosopher Theophrastus wrote a book, Book of Signs, in about 300 BC, listing more than 200 ways of knowing when to expect rain, wind, fair conditions, and other kinds of weather.

Scientifically based weather forecasting was not possible until meteorologists were able to collect data about current weather conditions from a widespread system of observing stations and organize that data in a timely fashion. By the 1930s these conditions had been met. Vilhelm and Jacob Bjerknes developed a weather station network in the 1920s that allowed for the collection of regional weather data. The weather data collected by the network could be transmitted nearly instantaneously by use of the telegraph, invented in the 1830s by Samuel F. B. Morse. The age of scientific forecasting, also referred to as synoptic forecasting, was under way.

The national weather service

In the United States, weather forecasting is the responsibility of the National Weather Service (NWS), a division of the National Oceanic and Atmospheric Administration (NOAA) of the Department of Commerce. NWS maintains more than 400 field offices and observatories in all 50 states and overseas. The future modernized structure of the NWS will include 116 weather forecast offices (WFO) and 13 river forecast centers, all collocated with WFOs. WFOs also collect data from ships at sea all over the world and from meteorological satellites circling Earth. Each year the Service collects nearly four million pieces of information about atmospheric conditions from these sources.

The information collected by WFOs is used in the weather forecasting work of NWS. The data is processed by nine National Centers for Environmental Prediction (NCEP). Each center has a specific weather-related responsibility: seven of the centers focus on weather prediction—the Aviation Weather Center, the Climate Prediction Center, the Hydrometeorological Prediction Center, the Marine Prediction Center, the Space Environment Center, the Storm Prediction Center, and the Tropical Prediction Center—while the other two centers develop and run complex computer models of the atmosphere and provide support to the other centers—the Environmental Prediction Center and NCEP Central Operations. Severe weather systems such as thunderstorms and hurricanes are monitored at the National Storm Prediction Center in Norman, Oklahoma. Hurricane watches and warnings are issued by the Tropical Prediction Center in Miami, Florida, (serving the Atlantic, Caribbean, Gulf of Mexico, and eastern Pacific Ocean) and by the Forecast Office in and Honolulu, Hawaii, (serving the central Pacific). WFOs, other government agencies, and private meteorological services rely on NCEP’s information, and many of the weather forecasts in the paper, and on radio and television, originate at NCEP.

Global weather data are collected at more than a thousand observation points around the world and then sent to central stations maintained by the World Meteorological Organization, a division of the United Nations. Global data are also sent to NWS’s NCEPs for analysis and publication.

Types of weather forecasts

The less one knows about the way the atmosphere works, the simpler weather forecasting appears to be. For example if clouds appear in the sky and a light rain begins to fall one might predict that rain will continue throughout the day. This type of weather forecast is known as a persistent forecast. A persistent forecast assumes the weather over a particular geographic area simply will continue into the future.

The validity of persistent forecasting lasts for a few hours, but not much longer because weather conditions result from a complex interaction of many factors that still are not well understood and that may change very rapidly.

A more reliable approach to weather forecasting is known as the steady-state or trend method. This method is based on the knowledge that weather conditions are strongly influenced by the movement of air masses that often can be charted quite accurately. A weather map might show that a cold front is moving across the great plains of the United States from west to east with an average speed of 10 mph (16 km/h). It might be reasonable to predict that the front would reach a place 100 mi (1,609 km) to the east in a matter of 10 hours. Since characteristic types of weather often are associated with cold fronts it then might be reasonable to predict the weather at locations east of the front with some degree of confidence.

A similar approach to forecasting is called the analogue method because it uses analogies between existing weather maps and similar maps from the past. For example suppose a weather map for December 10, 1996, is found to be almost identical with a weather map for January 8, 1993. Since the weather for the earlier date is already known it might be reasonable to predict similar weather patterns for the later date.

Yet another form of weather forecasting makes use of statistical probability. In some locations on Earth’s surface one can safely predict the weather because a consistent pattern has already been established. In parts of Peru it rains no more than a few inches per century. A weather forecaster in this region might feel confident that he or she could predict clear skies for tomorrow with a 99.9% chance of being correct.

Long-range forecasting

The complexity of atmosphere conditions is reflected in the fact that none of the forecasting methods outlined above is dependable for more than a few days. This reality does not prevent meteorologists from attempting to make long-term forecasts. These forecasts might predict the weather a few weeks, a few months, or even a year in advance.

The basis for long-range forecasting is a statistical analysis of weather conditions over an area in the past. For example a forecaster might determine that the average snowfall in December in Grand Rapids, Michigan, over the past 30 years had been 15.8 in (40.1 cm). A reasonable way to try estimating next year’s snowfall in Grand Rapids would be to assume that it might be close to 15.8 inches (40.1 cm).

Today this kind of statistical data is augmented by studies of global conditions such as winds in the upper atmosphere and ocean temperatures. If a forecaster knows that the jet stream over Canada has been diverted southward from its normal flow for a period of months, that change might alter precipitation patterns over Grand Rapids over the next few months.

Numerical weather prediction

All forms of forecasting make use of such numerical data as temperature, atmospheric pressure, and humidity. Numerical weather prediction, however, is a term that refers to forecasts obtained by using complicated mathematical calculations carried out with powerful computers.

Numerical weather prediction is based on mathematical models of the atmosphere. A mathematical model is a system of equations that attempt to describe the properties of the atmosphere and changes that may take place within it. These equations can be written because the gases that constitute the atmosphere obey the same physical and chemical laws that gases on Earth’s surface follow. For example, Charles’ law says that when a gas is heated it tends to expand. This law applies to gases in the atmosphere as it does to gases in a laboratory.

The technical problem that meteorologists face is that atmospheric gases are influenced by many different physical and chemical factors at the same time. A gas that expands according to Charles’ law may also be decomposing because of chemical forces acting on it.

In numerical weather predition, meteorologists select a group of equations that describe the conditions of the atmosphere as completely as possible for

KEY TERMS

Analogue method of forecasting —A prediction of future weather conditions based on the assumption that current conditions will produce weather patterns similar to those observed in the past.

Cold front —The leading edge of an advancing mass of cold air.

Hurricane warning —A notice issued when a hurricane has been observed either visually or on a radar screen.

Hurricane watch —A notice to the general public that a hurricane may be expected within a particular area.

Long-term forecast —A prediction of weather conditions over a matter of weeks, months, or a year.

Mathematical model —A system of equations that attempts to describe the properties of the atmosphere and changes that may take place within it.

Numerical forecast —A prediction of future weather patterns obtained by using high speed computers to carry out complex mathematical calculations derived from mathematical models of the atmosphere.

Percent skill —The likelihood that a weather forecast will be better than a pure chance prediction.

Persistent forecast —A prediction of weather conditions based on the assumption that the weather over a particular geographic area will remain constant over the near future.

Short-term forecast —A prediction for weather conditions over a matter of hours or days.

Statistical probability forecast —A prediction of future weather conditions based on an analysis of the likelihood of various conditions having occurred in the past.

Steady-state forecast, Trend method —A prediction of weather conditions based on the movement of air masses over a given geographical area at about the same direction and approximately the same speed as they have been moving.

Synopic forecasting —Scientifically based forecasts derived from the rapid collection and analysis of weather data from as extensive an area as possible.

any one location at any one time. This set of equations can never be complete because even a computer is limited as to the number of calculations it can complete in a reasonable time. Thus, meteorologists pick out the factors they think are most important in influencing the development of atmospheric conditions. These equations are incorporated into a computer program, and the computer will print out the changes that might be expected if atmospheric gases behave according to the scientific laws to which they are subject. From this printout a meteorologist can make a forecast of the weather in an area in the future.

The accuracy of numerical weather predictions depend primarily on two factors. First, the more data that is available to a computer the more accurate its results. Second, the faster the speed of the computer the more calculations it can perform and the more accurate its report will be. The difficulties inherent in numerical weather forecasting are underscored by the fact that, during the early 1960s, meteorologist Edward Lorenz was performing computer simulations of weather when he made discoveries that were important in the development of modern chaos theory. In the period from 1955 (when computers were first used in weather forecasting) to the current time, the so-called percent skill of forecasts has improved from about 30% to more than 60%. The percent skill measure was invented to describe the likelihood that a weather forecast will be better than pure chance.

Accuracy of weather forecasts

Weather forecasters have long been the subject of jokes, probably as much today as they were in Theophrastus’s time. One reason for this is that there is no standard measure of a correct weather forecast. Suppose that a forecaster predicts heavy rain will fall tomorrow. Is the forecast correct if some rain falls, but it is not perceived as heavy rain by some people?

Forecast accuracy also is difficult to judge because the average person’s expectations probably have increased as the percent skill of forecasts also has increased. A hundred years ago, few people would have expected to have much idea as to what the weather would be like 24 hours in the future. Today, a good next-day forecast is a reasonable expectation.

In general, the shorter the time period and the more limited the geographic area involved, the more accurate a forecast is likely to be. For periods of less than a day, a forecast covering an area of 100 sq mi (259 sq km) is likely to be quite dependable. Predictions about weather patterns over large areas weeks or months in the future are likely to be much less reliable.