How does Model Output Statistics improve local weather forecasts?

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Weather forecasting Doppler radars, such as this one operated by the U.S. National Weather Service at New Underwood, South Dakota, extract the Doppler frequency shift of the echo produced by a moving target by noting how much the frequency of the received signal differs from the frequency of the signal that was transmitted.

Model Output Statistics (MOS) improves local weather forecasts by bridging the gap between the outputs of numerical weather prediction models and actual observed conditions at specific locations.1

Numerical models are often imperfect and may struggle to predict certain surface-level phenomena or may consistently produce biased results, such as overestimating wind speeds. MOS addresses these limitations by establishing statistical relationships between a model's historical forecasts and the weather that actually occurred.1

By applying these relationships, meteorologists can translate raw model data into more precise, localized predictions. For example, if a model consistently predicts wind speeds that are too high, MOS can automatically adjust those values to produce an accurate forecast for a specific point, such as Heathrow Airport. As long as numerical models remain imperfect, MOS serves as a vital tool for refining objective weather forecasting to provide consistent and reliable results.1