# Numerical weather prediction

**Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. The output of a numerical weather model and the ensuing conditions at the ground was developed in the 1970s and 1980s, known as model statistics. Starting in 1990s models began to include the interactions of soil and vegetation with the atmosphere, which led to more realistic forecasts on weather near the Earth’s surface.**

### About Numerical weather prediction in brief

Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs. Even with the increasing power of supercomputers, the forecast skill of numerical weather models extends to only about six days even with accurate input data and a flawless model. In an effort to quantify the large amount of inherent uncertainty remaining in numerical predictions, ensemble forecasts have been used since the 1990s to help gauge the confidence in the forecast, and to obtain useful results farther into the future than otherwise possible. The idea of the numerical weather prediction is to use the state of the fluid dynamics at a given time and use the equations of fluid dynamics to predict weather at a specific time in the future. The output of a numerical weather model and the ensuing conditions at the ground was developed in the 1970s and 1980s, known as model statistics. Starting in 1990s, models began to include the interactions of soil and vegetation with the atmosphere, which led to more realistic forecasts on weather near the Earth’s surface. As such, a statistical relationship between the output of the model and weather at the surface is a viable idea to use to the prediction of the weather in the near future. In 1966, West Germany and the United States began producing operational forecasts based on primitive-equation models, followed by the United Kingdom in 1972 and Australia in 1977.

As computers have become more powerful, the size of the initial data sets has increased and newer atmospheric models have been developed to take advantage of the added available computing power. The development of limited area models facilitated advances in forecasting the tracks of tropical cyclones as well as air quality forecasts in the 70s and 80s. In the early 1980s models began the inclusion of soil, vegetation and vegetation interactions to make forecasts on the ground more realistic. This led to the creation of the output statistics, which are used to help define the forecast and extend the forecast window in which it is possible to make a good forecast. The latest models include more physical processes in the simplifications of the equation of motion in numerical simulations of the Atmosphere. The latter are widely applied for understanding and projecting climate change. The improvements made to regional models have allowed for significant improvements in tropical cyclone track and airquality forecasts; however, atmospheric models perform poorly at handling processes that occur in a relatively constricted area, such as wildfires. In addition, the partial differential equations used in the model need to be supplemented with parameterizations for solar radiation, moist processes, heat exchange, soil, and vegetation, surface water, and the effects of terrain. The chaotic nature of thepartial differential equations that govern the atmosphere is impossible to solve exactly, and small errors grow with time.

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This page is based on the article Numerical weather prediction published in Wikipedia (as of Nov. 06, 2020) and was automatically summarized using artificial intelligence.