Abstract
This present study aims to explore how forecasters can quickly make accurate predictions by using various high-resolution model forecasts. Based on three high temporal-spatial resolution (3 km, hourly) numerical weather prediction models (CMA-MESO, CMA-GD, CMA-SH3) from the China Meteorological Administration (CMA), the hourly precipitation characteristics of three model within 24 h from March to September 2020 are discussed and integrated into a single, hourly, deterministic quantitative precipitation forecast (QPF) by making use of an improved weighted moving average probability-matching method (WPM). The results are as follows: (1) In non-rainstorm forecasts, CMA-MESO and CMA-GD have similar forecast abilities. However, in rainstorm forecasts, CMA-MESO has a notable advantage over the other two models. Thus, CMA-MESO is selected as a critical factor when participating in sensitivity experiments. (2) Compared with the traditional equal-weight probability-matching method (PM), the WPM improves the different grade QPF because it can effectively reduce rainfall pattern bias by making use of the weighted moving average (WMA). Additionally, the WPM threat score in rainstorm forecast similarly improved from 0.051 to 0.056, with a 9.8% increase relative to the PM. (3) The sensitivity experiments show that an optimal rainfall intensity score (WPM-best) can further improve the QPF and overcome all single models in both rainstorm and non-rainstorm forecasts, and the WPM-best has a rainstorm threat score skill of 0.062, with an increase of 21.6% compared with the PM. The performance of the WPM-best will be better if the precipitation intensity is stronger and the valid forecast periods is longer. It should be noted that there is no need to select models before using the WPM-best method, because WPM-best can give a very low weight to the less-skillful model in a more objective way. (4) The improved WPM method is also applied to investigate the heavy-rainfall case induced by typhoon Mekkhala (2020), where the improved WPM technique significantly improves rainstorm forecasting ability compared with a single model.
Highlights
High-resolution numerical weather prediction (NWP) models have been continuously improved with the rapid development of computers, and the models’ forecasting performance have steadily increased
In rainstorm forecasts, the China Meteorological Administration (CMA)-MESO model shows a notable advantage with its threat score (TS) reaching 0.058, followed by the CMA-GD model (0.052)
Based on the hourly quantitative precipitation forecast (QPF) of three high-resolution NWP models spanning from March to September 2020, frequency-matching method (FM) and optimal threat score-based correction algorithm (OTS) calibration methods were used to construct a multi-model ensemble correction forecast and a group of comparative experiments were designed based on the multi-model ensemble method
Summary
High-resolution numerical weather prediction (NWP) models have been continuously improved with the rapid development of computers, and the models’ forecasting performance have steadily increased. By almost all NWP models have considerable forecast errors and have trouble to accurately forecast the precipitation intensity [1]. How to eliminate these biases has yet to be addressed in the improvement of NWP skills. The model output statistics (MOS) technique can enhance quantitative precipitation forecast (QPF) skills [4,5], and quantile mapping algorithms are effective in removing historical biases relative to observations [6]. Zhu and Luo [7] employed a frequency-matching method (FM) to produce a more realistic rainfall forecast based on frequency distributions of forecast and observations. Wu et al [8] showed that the optimal threat score-based correction algorithm (OTS) is superior to all lead times, single models, and multi-model means
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
