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Brownie, C., King, L. D., and Dube, T. J. (2004Longitudinal and Spatial Analyses Applied to Corn Yield Data from a Long-Term Rotation Trial,"
Brownie, C., King, L. D., and Dube, T. J. (2004Longitudinal and Spatial Analyses Applied to Corn Yield Data from a Long-Term Rotation Trial,"



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History of numerical weather prediction



The history of numerical weather prediction considers how current weather conditions as input into mathematical models of the atmosphere and oceans to predict the weather and future sea state (the process of numerical weather prediction) has changed over the years. Though first attempted in the 1920s, it was not until the advent of the computer and computer simulation that computation time was reduced to less than the forecast period itself. ENIAC was used to create the first forecasts via computer in 1950, and over the years more powerful computers have been used to increase the size of initial datasets as well as include more complicated versions of the equations of motion. The development of global forecasting models led to the first climate models. The development of limited area (regional) models facilitated advances in forecasting the tracks of tropical cyclone as well as air quality in the 1970s and 1980s.Because the output of forecast models based on atmospheric dynamics requires corrections near ground level, model output statistics (MOS) were developed in the 1970s and 1980s for individual forecast points (locations). The MOS apply statistical techniques to post-process the output of dynamical models with the most recent surface observations and the forecast point's climatology. This technique can correct for model resolution as well as model biases. Even with the increasing power of supercomputers, the forecast skill of numerical weather models only extends to about two weeks into the future, since the density and quality of observations—together with the chaotic nature of the partial differential equations used to calculate the forecast—introduce errors which double every five days. The use of model ensemble forecasts since the 1990s helps to define the forecast uncertainty and extend weather forecasting farther into the future than otherwise possible.
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