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Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions.

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dc.contributor.author Nipen, Thomas
dc.contributor.author Stull, Roland B.
dc.date.accessioned 2011-11-22T18:00:04Z
dc.date.available 2011-11-22T18:00:04Z
dc.date.issued 2011
dc.identifier.citation Delle Monache, Luca; Nipen, Thomas; Liu, Yubao; Roux, Gregory; Stull, Roland. 2011. Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions. Monthly Weather Review, 139 (11) 3554-3570, http://dx.doi.org/10.1175/2011MWR3653.1 en_US
dc.identifier.uri http://hdl.handle.net/2429/39210
dc.description.abstract Two new postprocessing methods are proposed to reduce numerical weather prediction’s systematic and random errors. The first method consists of running a postprocessing algorithm inspired by the Kalman filter (KF) through an ordered set of analog forecasts rather than a sequence of forecasts in time (ANKF). The analog of a forecast for a given location and time is defined as a past prediction that matches selected features of the current forecast. The second method is the weighted average of the observations that verified when the 10 best analogs were valid (AN). ANKF and AN are tested for 10-m wind speed predictions from the Weather Research and Forecasting (WRF) model, with observations from 400 surface stations over the western United States for a 6-month period. Both AN and ANKF predict drastic changes in forecast error (e.g., associated with rapid weather regime changes), a feature lacking in KF and a 7-day running-mean correction (7-Day). The AN almost eliminates the bias of the raw prediction (Raw), while ANKF drastically reduces it with values slightly worse than KF. Both analog-based methods are also able to reduce random errors, therefore improving the predictive skill of Raw. The AN is consistently the best, with average improvements of 10%, 20%, 25%, and 35% with respect to ANKF, KF, 7-Day, and Raw, as measured by centered root-mean-square error, and of 5%, 20%, 25%, and 40%, as measured by rank correlation. Moreover, being a prediction based solely on observations, AN results in an efficient downscaling procedure that eliminates representativeness discrepancies between observations and predictions. Copyright 2011 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyright@ametsoc.org. en_US
dc.language.iso eng en_US
dc.publisher American Meteorological Society en_US
dc.title Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions. en_US
dc.type text en_US
dc.type.text article en_US
dc.description.affiliation Earth and Ocean Sciences, Dept. of (EOS), Dept of en_US
dc.description.reviewstatus Reviewed en_US
dc.rights.copyright Stull, Roland B. en_US
dc.description.scholarlevel Faculty en_US

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