Short-term forecast large-scale error characteristics and their relationship with precipitation forecast skill under two rainfall regimes

Abstract

Although precipitation forecast skill has improved significantly in recent decades, both location and intensity of precipitation forecast based on kilometric-scale numerical weather prediction (NWP) models still contain errors. The importance of large-scale error in NWP has been recognized, but characteristics of large-scale error under different rainfall regimes and the influence of large-scale error on precipitation forecast skill remain unclear. In this regard, we focused on the Yangtze-Huaihe River Basin (YHRB) and used an operational forecast dataset covering the summer seasons of 2018 and 2019 obtained from the Anhui Meteorology Observatory to investigate large-scale error characteristics and their relationship with precipitation errors under two rainfall regimes.Large-scale error characteristics were measured by root-mean-square errors (RMSE), relative RMSE (RRMSE), and large-scale spatial pattern correlation (LSCC). The large-scale RMSEs were highly correlated with the rainfall intensity and area, while the large-scale RRMSE and LSCC can be used to describe precipitation forecast skill for rainfall events under the two rainfall regimes. In general, the precipitation forecast for strongly forced rainfall events performed better than that for weakly forced rainfall events. Under strong forcing conditions, the majority of rainfall events were concentrated in the intervals of smaller precipitation errors with smaller RRMSEs and higher LSCCs, and the precipitation forecast skill was weakly related to the large-scale RRMSE, LSCC and the initial large-scale variance contribution (LVC). Under weak forcing conditions, most of the rainfall events had a larger precipitation error and the frequency of location error was higher than that of intensity error. The low precipitation forecast skill and large forecast uncertainties for weakly forced rainfall events were strongly related to the large RRMSEs and low LSCCs in the geopotential height in the middle-upper troposphere and meridional wind component (V) in the lower troposphere. In particular, a strong negative correlation was found between the initial LVC and the large-scale RRMSE under weak forcing. This suggests that large-scale information could play a more important role in precipitation forecast for weakly forced rainfall events, and could potentially be used to indicate the magnitude of large-scale forecast errors in short-term forecasts under weak forcing conditions.