Evaluation of TIGGE Precipitation Forecast and Its Applicability in Streamflow Predictions over a Mountain River Basin, China

Abstract

The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in EPF and ESP over a mountain river basin in China. This evaluation was carried out based on both deterministic and probabilistic metrics at a daily temporal scale. The effectiveness of two postprocessing methods, the Generator-based Postprocessing (GPP) method, and the Bayesian Model Averaging (BMA) method were also investigated for EPF and ESP. Results showed that: (1) The ECMWF shows better performances than NCEP in both EPF and ESP in terms of evaluation indexes and representation of the hydrograph. (2) The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances. (3) Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, there are desirable EPF performances for both ECMWF and NCEP at all 7 lead days, as well as highly skillful ECMWF ESP for 1~5 lead days and average moderate skillful NCEP ESP for all 7 lead days. The results of this study can provide a reference for the applications of TIGGE over mountain river basins.

Was the extreme rainfall that caused the August 2022 flood in Pakistan predictable?

“…We also investigated the deviation among ensemble members of each forecast model in predicting two days of accumulated rainfall at various leads (figure 5). We observe that all the models exhibit less deviation among members at a shorter lead than a longer lead indicating that uncertainty increases with the lead time (Yang et al 2021, Xiang et al 2022. UKMO showed the highest uncertainty regarding standard deviation among all its ensemble members.…”

Post-Processing Ensemble Precipitation Forecasts and Their Applications in Summer Streamflow Prediction over a Mountain River Basin

“…In this study, the Xin'anjiang (XAJ) model [28] was utilized for hydrological modelling because of its effective applications across the semi-humid and humid zones in China [21,[29][30][31]. Within the framework of the lumped XAJ model, the basin is generalized into a soil box, encompassing an upper, lower, and deep soil layer.…”