Abstract
Subseasonal forecasts have recently attracted widespread interest yet remain a challenging issue. A statistical Kalman filter pattern projection method (KFPPM), which absorbs the projection conception of the raw covariance pattern projection (COVPPM) and the adaptive adjustments of the Kalman filter, is proposed to calibrate the single-model forecasts of the daily maximum and minimum temperatures (Tmax and Tmin) for lead times of 8–42 days over East Asia in 2018 derived from the UKMO control (CTL) forecast. The Kalman filter–based gridly calibration (KFGC) is carried out in parallel as a benchmark, which could improve the forecast skills to a certain extent. The COVPPM effectively calibrates the temperature forecasts at the early stage and displays better performances than the CTL and KFGC. 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° 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 time scale, showing the most conspicuous improvements distributed over the Tibetan Plateau and its surroundings. Though the postprocessing procedures are more skillful in calibrating Tmax forecasts than Tmin forecasts, the Tmax forecasts are still characterized by lower skills than the latter. Case experiments further demonstrate the abovementioned features and imply the potential capability of KFPPM in improving forecast skills and disaster preventions for extreme temperature events.
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