Advances in weather prediction

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![][1] Advances in weather forecasting are helping to improve environmental forecast, for example, of wildfire activity. PHOTO: ADAM SIMPSON/HEART AGENCY Weather forecasting provides numerous societal benefits, from extreme weather warnings to agricultural planning. In recent decades, advances in forecasting have been rapid, arising from improved observations and models, and better integration of these through data assimilation and related techniques. Further improvements are not yet constrained by limits on predictability. Better forecasting, in turn, can contribute to a wide range of environmental forecasting, from forest-fire smoke to bird migrations. In 1938, an intense hurricane struck the New England coast of the United States without warning, killing more than 600 people. Since then, death tolls have dropped dramatically even though coastal populations have swelled. Many people and organizations contributed to this improvement. But, as the American Meteorological Society celebrates its 100th anniversary, the improvement in forecasting stands out. Modern 72-hour predictions of hurricane tracks are more accurate than 24-hour forecasts were 40 years ago (see the figure), giving sufficient time for evacuations and other preparations that save lives and property. Similar improvements in forecasting tropical cyclone tracks have been achieved by other leading agencies worldwide. ![Figure][2] CREDIT: VLADIMIR MELNIKOV/SHUTTERSTOCK Weather forecasts from leading numerical weather prediction centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Oceanic and Atmospheric Administration's (NOAA's) National Centers for Environmental Prediction (NCEP) have also been improving rapidly: A modern 5-day forecast is as accurate as a 1-day forecast was in 1980, and useful forecasts now reach 9 to 10 days into the future ([ 1 ][3]). Predictions have improved for a wide range of hazardous weather conditions, including hurricanes, blizzards, flash floods, hail, and tornadoes, with skill emerging in predictions of seasonal conditions. Investment in weather forecasting pays large dividends, ranging from 3 to 10 times the costs ([ 2 ][4]). A 2009 study, for example, found that the value of weather forecasts to U.S. households is US$31.5 billion, from public expenditures of just US$3.4 billion and private expenditures of US$1.7 billion ([ 3 ][5]). U.S. adults obtain weather forecasts at a rate of 300 billion times per year, from numerous sources ([ 3 ][5]). Key developments in observation, numerical modeling, and data assimilation have enabled these advances in forecast skill. Improved observations, particularly by satellite remote sensing of the atmosphere and surface, provide valuable global information many times per day. Much faster and more powerful computers, in conjunction with improved understanding of atmospheric physics and dynamics, allow more-accurate numerical prediction models. Finally, improved techniques for putting data and models together have been developed. Because data are unavoidably spatially incomplete and uncertain, the state of the atmosphere at any time cannot be known exactly, producing forecast uncertainties that grow into the future. This sensitivity to initial conditions can never be overcome completely. But, by running a model over time and continually adjusting it to maintain consistency with incoming data, the resulting physically consistent predictions greatly improve on simpler techniques. Such data assimilation, often done using four-dimensional variational minimization, ensemble Kalman filters, or hybridized techniques, has revolutionized forecasting. Sensitivity to initial conditions limits long-term forecast skill: Details of weather cannot be predicted accurately, even in principle, much beyond 2 weeks. But weather forecasts are not yet strongly constrained by this limit, and the increase in forecast skill has shown no sign of ending ([ 4 ][6], [ 5 ][7]). Sensitivity to initial conditions varies greatly in space and time, and an important but largely unsung advance in weather prediction is the growing ability to quantify the forecast uncertainty by using large ensembles of numerical forecasts that each start from slightly different but equally plausible initial states, together with perturbations in model physics. Several features of the weather system are more persistent than day-to-day weather, allowing accurate predictions further into the future, from subseasonal to seasonal, annual, and interannual time scales and beyond, with even greater scope for improvement. For example, the Madden-Julian Oscillation (MJO) moves eastward around the tropics over 30 to 90 days, affecting rain, wind, clouds, air pressure, the onset and demise of summer monsoons, and more, with important agricultural and other implications. Weather prediction models have now shown predictive skills for the MJO phenomena up to 5 weeks ([ 5 ][7]). In parallel with improving forecasts, communication of the growing wealth of weather data has expanded greatly, enabling a timely flow of ever more detailed and accurate information to a rich diversity of users. Only a few decades ago, one had to wait for the morning newspaper or the evening news to get the latest forecast, and warnings of imminent arrival of severe weather were delivered mostly by flags, sirens, and police bullhorns. Today, detailed, geographically targeted weather information is available at the touch of a finger on a smartphone. ![Figure][2] Advances in hurricane prediction Data from the NOAA National Hurricane Center (NHC) ([ 13 ][8]) show that forecast errors for tropical storms and hurricanes in the Atlantic basin have fallen rapidly in recent decades. The graph shows the forecast error in nautical miles (1 n mi = 1.852 km) for a range of time intervals. GRAPHIC: N. DESAI/ SCIENCE (DATA) NOAA/NHC Weather-forecast improvement is the essential first step toward better predictions of many...

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