Local temperature forecasts based on post-processing of Numerical Weather Prediction data

Six adaptive post-processing methods for correcting systematic biases in forecasts of near-surface air temperatures, using local meteorological observations, are assessed and compared. The methods tested are based on the simple moving average and the more advanced Kalman filter - constructed to remove the longer-term bias, the very short-term errors or a combination of the two. Forecasts from a coarser-resolution global model and a regional high-resolution model are post-processed and the results are evaluated for one hundred private weather stations in Denmark. Overall, the postprocessing method for which a moving average is combined with a Kalman filter, constructed to remove the very short-term errors, performs the best. The biases of both the global coarserresolution forecasts and the regional high-resolution forecasts are reduced close to zero for all forecast lead times. The standard deviation is reduced for all forecast lead times for the coarser resolution model, whereas for the high-resolution model the most significant reduction is seen for the first six forecast lead hours. This shows that the application of a relatively simple postprocessing method, based on a short training period, can give good results.