Kalman filtered analogs to improve Numerical Weather Predictions

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Tuesday, 19 January 2010: 1:30 PM
B305 (GWCC)
Thomas Nipen, University of British Columbia, Vancouver, BC, Canada; and L. Delle Monache, J. P. Hacker, Y. Liu, R. B. Stull, and T. Warner

A new approach combining the Kalman filter (KF) bias-corrector and an analog (AN) procedure is presented. The method (KFAN) can be used to improve predictions skill and reduce bias of Numerical Weather Predictions (NWP) and is applied here for surface wind forecasts.

KF is a linear, adaptive, recursive and optimal (in a least-square sense) method to estimate NWP biases. After a weather regime change, when the distribution of bias often changes, the KF requires few adjustment cycles before the new bias is accurately estimated. This drawback can be largely mitigated by rearranging past predictions based on their similarity to the current prediction (i.e., by searching for "analogs"). Least similar forecasts are placed first, and most similar are placed last in the KF sequence of cycles. The resulting series is then Kalman filtered. By Kalman filtering in analog space instead of time space the adjustment period present in the original KF method can be bypassed.

KFAN is tested on wind speed predictions of the fifth-generation Pennsylvania Sate University-NCAR Mesoscale Model (MM5), with data from 22 stations over New Mexico and a period of a year (starting 1 January 2005). KFAN largely improves the raw forecast skill, significantly outperforming also KF, persistence, and a simple 7-day bias correction.