Kalman filter and analog schemes to postprocess numerical weather predictions
ANKF and AN are tested with 10-m wind speed forecasts from the Weather Research and Forecasting (WRF) modeling system with a 24-hour lead time and initialized at 0200 UTC. Hourly predictions are compared to 500 surface wind observations for a 6-month period (May-October, 2009) and over a domain centered on western Colorado, USA. The analog-based methods (i.e., ANKF and AN) performance is compared to the skill of the raw forecast (Raw), a 7-day running mean bias correction (7-Day), and KF. Both AN and ANKF are able to predict drastic changes in forecast error (e.g., associated with rapid weather regime changes), a feature lacking in KF, 7-Day, and standard model output statistics algorithms. AN almost eliminates the bias of the raw prediction, 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. 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.