0−12 Hour QPFs of HRRR-TLE Using Optimized Probability-Matching Method: Taking Hunan Province as an Example

In real-time operations, the minutely/hourly updated high-resolution rapid refresh (HRRR) system is one of the most expensive numerical weather prediction (NWP) models. Based on a twenty-member HRRR-time-lagged-ensemble (HRRR-TLE) system developed from two real-time convection-permitting HRRR models, CMA-GD(R3) and CMA-SH3, from the China Meteorological Administration (CMA), this study proposes an optimized probability-matching (OPM) technique to improve 0−12 h quantitative precipitation forecasts (QPFs) based on the correlation and error relationships between ensemble forecasts and observations during the training window. Then, a series of sensitivity experiments using different cost functions and optimized ratios was conducted to further improve OPM predictions. The results indicate that: (1) In the HRRR-TLE system, there is no always optimal member in both weak rain and severe rain forecasts, as measured by the equitable threat score (ETS) and bias extent (BE) at four thresholds (1+, 5+, 10+, and 20+ mm h^–1; e.g., "1+" means ≥ 1). (2) Compared with the HRRR-TLE system, the QPFs generated by the traditional PM technique showed a notable increase in ETS and a decrease in BE at all of the above thresholds. Compared with the traditional probability-matching method (PM), OPM can generate more skillful forecasts on both spatial representations and rain rates by using the sliding-weight method and optimized ensembles, respectively. (3) In particular, in the 20+ mm h^–1 forecasts, which are often difficult to predict, the ETS of the optimal OPM test, with a 20% optimization ratio and symmetric mean absolute percentage error cost function, increased by 64.6%, and the BE decreased by 5.7%, relative to PM. Moreover, OPM shows good stability in both daytime and nighttime periods.