Improvements on short-term precipitation forecast in Northwest China based on regionally optimized moisture adjustment scheme for convective-scale NWP

Abstract

Initial moisture information is of significance in high-resolution numerical weather prediction (NWP). To enhance the forecasting ability of kilometric-scale NWP systems, the performance of two moisture adjustment schemes (MASs) in a cloud analysis system were first investigated in the complex terrain. Subsequently, a three-step approach to reconstruct a suitable MAS for the local NWP was proposed. The effectiveness and ability of the reconstructed scheme were examined with a series of forecast experiments involving eight heavy rain cases and a one-month period in summer 2018. All experiments, with or without cloud analysis, were conducted using the GRAPES-Meso model with a resolution of 3 km and the operational setting pertaining to Northwest China. The eight heavy rain cases and the one-month period experiments demonstrated that the reconstructed MAS outperforms the MAS currently used in operational NWP, even in the complex terrain. The reconstructed MAS can achieve superior precipitation forecasts, with a high forecast accuracy for 2 m temperature and 10 m wind, especially for eight heavy rain cases. Notably, the precipitation forecast performance is sensitive to the parameters in the moisture adjustment scheme. The performance of the reconstructed MAS for eight heavy rain cases is better than that for the one-month period experiments, likely because the data samples used to reconstruct MAS are extracted from the forecasts of one heavy rain case. The three-step reconstructed approach for the moisture adjustment scheme can be extended into other regional cloud analysis systems.