ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 50 Issue 6 > 2024,50(6):649-660. DOI:10.7519/j.issn.1000-0526.2024.031402 Prev Next
Forecasting System for Short-Term Multi-Category Convective Phenomena Combining Physical Understanding and Fuzzy Logic Part Ⅱ: Performance Evaluation
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Abstract:
In this paper, the performance of the forecasting system of short-term multi-category convective phenomena in the event that happened on 13 June 2022 is analyzed first. Then, based on the objective probability forecasts of thunderstorm, short-time severe rainfall, thunderstorm gale and hail events in 2022 as well as available multi-category severe convective monitoring data, the performance of objective probability forecast products of the four types of severe convective weather provided by the short-term forecasting system is evaluated in detail by adopting the spatial test methods used in the severe convection forecast operations and the indices that indicate deterministic and probabilistic properties. The evaluated forecast period of the forecast products initiated at 08:00 BT from 1 April to 30 September 2022 is 96 h with interval of 12 h. Case studies show that the potential area of the four different convective phenomena could be well forecasted 24 h in advance. Statistical verification results show that the short-time severe rainfall forecast has the best performance among the four convective weather phenomena, followed by the forecast of thunderstorm. The forecast of the thunderstorm gale has certain applicability as well. There are obvious problems of overestimation in all the four convective weather phenomena compared to the observations. The diurnal variations of thunderstorm, short-time severe rainfall and thunderstorm gale forecasts are related to the forecast coverage time. These evaluation results are beneficial to subsequent improvement and development of forecast model and system, and could provide a useful reference for the operational application of multi-category severe convection forecast results based on the fusion of physical understanding and fuzzy logic artificial intelligence.
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