Weather forecasting employs scientific and technological methods to predict atmospheric conditions for specific locations and times. Informal weather prediction has existed for millennia, while formal forecasting began in the 19th century. It leverages scientific understanding and technological tools to anticipate future weather patterns, aiding in various aspects of human life, from daily planning to disaster preparedness.
In 1911, the Met Office began issuing the first marine weather forecasts via radio transmission, including gale and storm warnings for areas around Great Britain. This marked a significant advancement in disseminating weather information to maritime communities.
In 1922, Lewis Fry Richardson, an English scientist, published "Weather Prediction By Numerical Process", detailing a method for numerical weather prediction using finite differencing schemes based on fluid dynamics equations. This publication marked a foundational step towards modern numerical weather forecasting.
In 1925, Edward B. "E.B." Rideout of the U.S. Weather Bureau made the first public radio weather forecasts in the United States on WEEI, the Edison Electric Illuminating station in Boston. This was a crucial step in bringing weather information directly to the public.
In 1931, G. Harold Noyes, also from the U.S. Weather Bureau, started providing weather forecasts on WBZ radio, further expanding the reach of weather information to the public.
In November 1936, the BBC experimentally broadcast the world's first televised weather forecasts, including the use of weather maps. This was a pioneering effort in visual weather communication.
In either 1940 or 1947, James C. Fidler made experimental television forecasts in Cincinnati on the DuMont Television Network, contributing to the early development of televised weather reporting.
In either 1940 or 1947, James C. Fidler made experimental television forecasts in Cincinnati on the DuMont Television Network, contributing to the early development of televised weather reporting.
In 1954, George Cowling presented the first televised weather forecast while standing in front of a weather map, marking a significant advancement in how weather information was presented on television.
In 1955, the practical application of numerical weather prediction began, spurred by the development of programmable electronic computers. A team including Jule Charney, Philip Duncan Thompson, Larry Gates, Ragnar Fjørtoft, John von Neumann, and Klara Dan von Neumann performed the first computerized weather forecast.
In 1963, Edward Lorenz proposed that long-range weather forecasts (two weeks or more) cannot definitively predict the state of the atmosphere due to the chaotic nature of fluid dynamics equations. He noted that small errors in initial values double roughly every five days for variables like temperature and wind velocity in numerical models.
In 1982, John Coleman partnered with Frank Batten to launch The Weather Channel (TWC), a 24-hour cable network dedicated to national and local weather reports. Coleman also pioneered the use of on-screen weather satellite data and computer graphics for television forecasts in the late 1970s and early 1980s.
In 2009, the United States invested approximately $5.8 billion in weather forecasting, which yielded benefits estimated to be six times that amount. This highlights the economic importance of weather forecasting.
In 2022, initial attempts to use artificial intelligence in weather forecasting led to the emergence of models like Huawei's Pangu-Weather, Google's GraphCast, WindBorne's WeatherMesh, Nvidia's FourCastNet, and the European Centre for Medium-Range Weather Forecasts' AIFS.
In 2023, AI weather models like Huawei's Pangu-Weather, Google's GraphCast, WindBorne's WeatherMesh, Nvidia's FourCastNet, and the European Centre for Medium-Range Weather Forecasts' AIFS continued to develop, pushing the boundaries of weather forecasting technology.
In 2024, Lang et al. presented a study using the AIFS to conduct 30-day ensemble simulations of the Madden-Julia Oscillation, providing insights into long-term weather patterns.
In 2024, researchers at Google's DeepMind AI research laboratories published a paper in Nature describing GenCast, their machine-learning model. GenCast is expected to produce more accurate forecasts compared to the best traditional weather forecasting systems.
In 2024, the European Centre for Medium-Range Weather Forecasts' AIFS began publishing real-time forecasts. While showing specific skill in predicting hurricane tracks, its performance on the intensity changes of storms was lower compared to physics-based models.
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