Calibrations of Surface Air Temperature Forecasts at Short- and Long-term Timescales Based on Statistical Pattern Projection Methods

So far, plenty of efforts have been pursued on the numerical weather prediction (NWP). However, systematic errors could never be ignored in the output applications. To supply the numerical forecasts with higher accuracies, statistical postprocessing is often expected to correct systemic biases and has been one of the key components of the forecasting suites. Based on the NWP models and taking advantages of the raw stepwise pattern projection method (SPPM), the neighborhood pattern projection method (NPPM) is newly proposed to postprocess the model outputs and to improve forecast skills of daily maximum and minimum temperatures (Tmax and Tmin) over East Asia for short-term timescales, as well as the Kalman filter based pattern projection method (KFPPM) for longer-term forecasts. For the short-term lead times of 1–7 days, the SPPM is slightly inferior to the benchmark of decaying averaging method, 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 ~0.7° and ~0.9°C, respectively. Advantages of the SPPM and NPPM are both mainly concentrated on the high-altitude areas such as the Tibetan Plateau, where the raw model outputs show the most conspicuous biases. As for longer-term forecasts at the subseasonal timescale, the NPPM effectively calibrates the temperature forecasts at the early stage. However, with the growing lead times, it shows speedily decreasing skills and can no longer produce positive adjustments over the areas outside the plateaus. By contrast, the KFPPM consistently outperforms the other calibrations and reduces the forecast errors by almost 1.0°C and 0.5°C for Tmax and Tmin, respectively, both retaining superiorities to the random climatology benchmark till the lead time of 24 days. The optimization of KFPPM maintains throughout the whole range of the subseasonal timescale, showing most conspicuous improvements distributed over the Tibetan Plateau and its surroundings. Case experiments further demonstrate the above-mentioned features and imply the potential capability of the NPPM and KFPPM in improving forecast skills and disaster preventions for extreme temperature events. Besides, compared with the initial SPPM, they not only produces more powerful forecast calibrations, but also provides more pragmatic calculations and greater potential economic benefits in practical applications.