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In weather forecasting, model output statistics (MOS) is a multiple linear regression technique in which predictands, often near-surface quantities (such as two-meter-above-ground-level air temperature, horizontal visibility, and wind direction, speed and gusts), are related statistically to one or more predictors.
Top: Weather Research and Forecasting model simulation of Hurricane Rita tracks. Bottom: The spread of National Hurricane Center multi-model ensemble forecast. Ensemble forecasting is a method used in or within numerical weather prediction. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced.
The world's first televised weather forecasts, including the use of weather maps, were experimentally broadcast by the BBC in November 1936. [31] This was brought into practice in 1949, after World War II. [31] George Cowling gave the first weather forecast while being televised in front of the map in 1954.
Weather reconnaissance aircraft, such as this WP-3D Orion, provide data that is then used in numerical weather forecasts.. The atmosphere is a fluid.As such, the idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future.
Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the "lack of fit" in some other norm (as with least absolute deviations regression), or by minimizing a penalized version of the least squares cost function as in ridge regression (L 2-norm penalty) and ...
In linear regression, the model specification is that the dependent variable, is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n {\displaystyle n} data points there is one independent variable: x i {\displaystyle x_{i}} , and two parameters, β ...
The Weather Research and Forecasting (WRF) Model [1] (/ ˈ w ɔːr f /) is a numerical weather prediction (NWP) system designed to serve both atmospheric research and operational forecasting needs, developed in the United States. NWP refers to the simulation and prediction of the atmosphere with a computer model, and WRF is a set of software ...
The use of regression to calibrate weather forecasts in this way is an example of model output statistics. However, this simple linear regression model does not use the ensemble standard deviation S {\displaystyle S} , and hence misses any information that the ensemble standard deviation may contain about the forecast uncertainty.