首页|融合物理理解与模糊逻辑的分类强对流客观短期预报系统:(1)系统构成

融合物理理解与模糊逻辑的分类强对流客观短期预报系统:(1)系统构成

扫码查看
提供准确的雷暴、短时强降水、雷暴大风和冰雹客观短期预报产品,对提高预报预警的预见期,及早采取有针对性的预防措施有重要意义。基于对四类强对流天气现象物理成因理解,给出了由国家气象中心牵头研发,融合模糊逻辑人工智能方法的分类强对流客观短期概率预报系统的流程框架和实现方法,详细介绍了该系统的结构特征,以及系统中用于雷暴、短时强降水、雷暴大风和冰雹四类强对流天气预报模型构建的关键预报因子、隶属度函数获取方法和权重因子配置等信息,并在此基础上探讨了物理理解与模糊逻辑人工智能相融合方法具有广泛适用性的本质,可以表征产生特定强对流天气现象的环境配置的多样性和复杂性。
Forecasting System for Short-Term Multi-Category Convective Phenomena Combining Physical Understanding and Fuzzy Logic Part Ⅰ:System Construction
Accurate and objective forecasts of thunderstorm,short-time severe rainfall,thunderstorm gale and hail are meaningful for extending the validation of warnings and taking targeted preventive measures.This paper introduces the framework and implementation ways of the objective forecasting system com-bining physical understanding and fuzzy logic artificial intelligence.This system,developed by the National Meteorological Centre(NMC),can provide short-term probability forecasts of thunderstorm,short-time severe rainfall,thunderstorm gale,and hail.The key predictors used for the four different convective weather phenomena,the methods for obtaining the membership functions,and the weighting sets of pre-dictors are discussed.The property for the wide applicability of the combination method of physical under-standing and fuzzy logic artificial intelligence is further investigated.It is concluded that the combination of the two can cover and reveal the key characteristics of the ever-changing environmental features favorable for a specific convective weather phenomenon.

physical understandingfuzzy logic artificial intelligencemulti-category convective weather phenomenonshort-term forecasting systemsystem construction

田付友、郑永光、孙建华、夏坤、杨波、坚参扎西、赤曲

展开 >

国家气象中心,北京 100081

中国科学院大气物理研究所云降水物理与强风暴重点实验室,北京 100029

中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京 100029

西藏自治区气象台,拉萨 850000

展开 >

物理理解 模糊逻辑人工智能 分类强对流 短期预报系统 系统构成

国家自然科学基金联合基金西藏自治区科技计划国家重点研发计划中国气象局重点创新团队项目

U2142202XZ202101ZY0004G2022YFC3004104CMA2022ZD07

2024

气象
国家气象中心

气象

CSTPCD北大核心
影响因子:2.337
ISSN:1000-0526
年,卷(期):2024.50(5)