Model output statistics

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Model Output Statistics (MOS) is an omnipresent statistical technique that forms the backbone of modern weather forecasting. The technique pioneered in the 1960s and early 1970s is used to post-process output from numerical weather forecast models. Generally speaking, numerical weather forecast models do an excellent job of forecasting upper air patterns but are too crude to account for local variations in surface weather. Pure statistical models, on the other hand, are excellent at forecasting idiosyncrasies in local weather but are usually worthless beyond about six hours. The MOS technique combines the two, using complex numerical forecasts based on the physics of the atmosphere to forecast the large-scale weather patterns and then using regression equations in statistical post-processing to clarify surface weather details. The accuracy is generally far better than either a pure statistical model or a pure numerical model (NWP).

Definition

Because forecast models on their own do not reliably determine weather conditions near ground level, statistical models based upon the three dimensional fields output from numerical weather models, surface observations, as well as the climatology for specific locations were developed. These statistical models are called Model Output Statistics (MOS).^[1]

History

MOS was developed by the National Weather Service for their suite of weather forecasting models by 1976.^[2] The United States Air Force developed their own set of MOS based upon their dynamical weather model, the NOGAPS, by 1983.^[3] This differs from the perfect prog technique, which assumes that the output of numerical weather prediction guidance is perfect.^[4]

Leading MOS scientists

MOS has a very small field of practitioners. The most often cited pioneers are Glahn, Lowry, and Klein. Currently, most work in the field is done from the Meteorological Development Lab (MDL) . Significant work has been contributed to the field by Murphy, Dallavalle, Mass, Vislocky, Fritsch, Allen, Hughes, and Antolik.

Current operational forecasts

MOS is run operationally using millions of equations from the MDL. As of 2010, the NAM, GFS (short and long range) and 22-member GFS ensemble are used to create MOS products; the NGM-derived MOS products were discontinued in March 2009. (List of MOS products from the MDL)

References

↑ Baum, Marsha L. (2007). When nature strikes: weather disasters and the law. Greenwood Publishing Group. p. 189. ISBN 978-0-275-22129-4. Retrieved 2011-02-11.
↑ Harry Hughes (1976). Model output statistics forecast guidance. United States Air Force Environmental Technical Applications Center. pp. 1–16.
↑ L. Best, D. L. and S. P. Pryor (1983). Air Weather Service Model Output Statistics Systems. Air Force Global Weather Central. pp. 1–90.
↑ Gultepe, Ismail (2007). Fog and boundary layer clouds: fog visibility and forecasting. Springer. p. 1144. ISBN 978-3-7643-8418-0. Retrieved 2011-02-11.

External links

Atmospheric, oceanographic and climate models

Model types

Atmospheric model Climate model Numerical weather prediction Tropical cyclone forecast model Atmospheric dispersion modeling Chemical transport model Ensemble forecasting Model output statistics

Specific models

Climate	IGCM HadCM3 HadGEM1 GFDL CM2.X CGCM CCSM CFSv2 ECHAM

Global weather	IFS (ECMWF) FIM GEM GFS NAVGEM UM JMA GME ARPEGE

Regional and mesoscale atmospheric	NAM RR / RAP RAMS WRF RAQMS HIRLAM LAPS RPM

Regional and mesoscale oceanographic	HyCOM ROMS POM MOM

Atmospheric dispersion	ADMS AERMOD ATSTEP AUSTAL2000 CALPUFF DISPERSION21 ISC3 MEMO MERCURE NAME OSPM PUFF-PLUME RIMPUFF SAFE AIR

Chemical transport	CLaMS MOZART

Land surface parameterisation	JULES CLASS ISBA

Discontinued	AVN / MRF / GSM Eta LFM MM5 NGM NOGAPS RUC

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