基于气象条件的昭苏地区彩虹概率预报模型研究

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Meteorological Applications Pub Date : 2023-06-29 DOI:10.1002/met.2131
Jing Liu, Jin Yu, Shen Lin, Guodong Zhang, Shuo Zhang, Min Li, Xiaoyue Lin
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引用次数: 0

摘要

对2017年至2019年昭苏地区人工彩虹观测结果的分析表明,彩虹主要发生在4月至9月的16:00至22:00(LST)之间。根据同一时期的气象观测分析表明,降水、温度、风力和云的变化对彩虹的形成有贡献。大约90%的彩虹在降雨后一小时出现,100%的彩虹出现在博福特风力等级低于8级时(低于20.9级 m/s),温度大于8°C,云量分别大于40%。基于五个气象因子建立了彩虹概率预报模型。该模型的预测能力是通过比较2020年彩虹预报及其观测结果来独立评估的。Brier评分为0.20,表明该客观模型对彩虹预报有效。
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Research on rainbow probabilistic forecast model based on meteorological conditions in ZhaoSu region

An analysis of artificial rainbow observations in ZhaoSu region from 2017 to 2019 shows that rainbows mainly occur between 16:00 and 22:00 (LST) from April to September. The analysis based on the meteorological observation in the same period shows that precipitation, temperature, wind force and cloud variety contribute to rainbow formation. Approximately 90% of rainbows appear one hour after rainfall, and 100% of rainbows occur when the Beaufort wind scale is less than level 8 following Beaufort wind scale (less than 20.9 m/s), the temperature is greater than 8°C, and the cloud amount is greater than 40%, respectively. A rainbow probabilistic forecast model is constructed based on five meteorological factors. The forecast ability of the model is independently assessed by comparing rainbow forecasts and its observation in 2020. The Brier score is 0.20, indicating that the objective model is effective for rainbow forecasts.

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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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