Calibrating Multi-Scale Deterministic and Probabilistic Forecasts

Over recent decades the deterministic and probabilistic NWPs have been improved significantly. It becomes the essential tool for the meteorological operation and applications. It is very often that there are several deterministic NWPs and EPSs with different resolution available for meteorological operation and applications. Those forecasts are with different characteristics of systematic bias and dispersion errors. Many statistical calibration methods have been proposed and implemented in the operation, for example, ensemble model output statistics (EMOS) and standardized anomaly model output statistics (SAMOS). In the recent years, Artificial intelligence (AI) based method has been used in different way for calibration. In this study we have applied AI based methods for selecting the important variables and building the non-linearity for calibration in frame of EMOS and SAMOS to calibrate multi-scale deterministic and probabilistic forecasts. The CMA（China meteorological Administration) NWP model chain, a convection permitting NWP (3km resolution), a regional NWP (9km) and a global NWP (25km), a regional EPS (10km) and a global EPS (50km) have been used for the calibration. Two years observation and NWP data over Beijing region was selected for training the EMOS/SAMOS method. EMOS and SAMOS, AI based variable selection and Boosting method etc. have been compared. 2m temperature, 10m Wind and precipitation have been calibrated and verified with statistical scores such as, root mean square error of ensemble mean, continuous ranked probability score（CRPS）and so on. The results of calibrated ensemble mean and ensemble spread are quite encouraging, which will be presented at the conference.