Statistical Calibrations of Surface Air Temperature Forecasts over East Asia Using Pattern Projection Methods

In this study, two pattern projection methods, i.e., the stepwise pattern projection method (SPPM) and the newly proposed neighborhood pattern projection method (NPPM), are investigated to improve forecast skills of daily maximum and minimum temperatures (Tmax and Tmin) over East Asia with lead times of 1-7 days. Meanwhile, the decaying averaging method (DAM) is conducted in parallel for comparison. These postprocessing methods are found to effectively calibrate the temperature forecasts on the basis of the raw ECMWF output. Generally, the SPPM is slightly inferior to the DAM, while its insufficiency decreases with increasing lead times. The NPPM shows manifest superiority for all lead times, with the mean absolute errors of Tmax and Tmin decreased by similar to 0.7 degrees and similar to 0.9 degrees C, respectively. Advantages of the two pattern projection methods are both mainly concentrated on the high-altitude areas such as the Tibetan Plateau, where the raw ECMWF forecasts show the most conspicuous biases. In addition, aiming at further assessments of these methods on extreme event forecasts, two case experiments are carried out toward a heat wave and a cold surge, respectively. The NPPM is retained as the optimal with the highest forecast skills, which reduces most of the biases to <2 degrees C for both Tmax and Tmin over all the lead days. In general, the statistical pattern projection methods are capable of effectively eliminating spatial biases in forecasts of surface air temperature. Compared with the initial SPPM, the NPPM not only produces more powerful forecast calibrations, but also provides more pragmatic calculations and greater potential economic benefits in practical applications.