CUACE模式产品在河南省空气质量预报中的检验  

Verification of CUACE Model Products in Air Quality Forecasts in Henan Province

作　　者：朱枫 田力 王新敏[1,3] 孔海江 李飞[4] Zhu Feng;Tian Li;Wang Xinmin;Kong Haijiang;Li Fei(CMA·Henan Key Laboratory of Agrometeorological Support and Applied Technique,Zhengzhou 450003,China;Xinxiang Meteorological Office of Henan Province,Xinxiang 453003,China;Henan Meteorological Observatory,Zhengzhou 450003,China;Henan Meteorological Service,Zhengzhou 450003,China)

机构地区：[1]中国气象局·河南省农业气象保障与应用技术重点实验室,郑州450003 [2]新乡市气象局,河南新乡453003 [3]河南省气象台,郑州450003 [4]河南省气象局,郑州450003

出　　处：《气象与环境科学》2025年第1期11-23,共13页Meteorological and Environmental Sciences

基　　金：中国气象局预报员专项项目(CMAYBY2020-075);中国气象局·河南省农业气象保障与应用技术重点开放实验室应用技术研究基金项目(KM202147)。

摘　　要：利用多维度分组检验的方式,对2017年4月至2021年3月CUACE模式产品在河南省空气质量预报的效果作了检验分析,结果表明:(1)CUACE的AQI预报整体上以高估为主;空气质量为良时的预报效果最好,严重污染时预报效果最差;在6、7、8、9月预报效果较好,3月预报效果最差;在南阳的整体预报效果最好。(2)CUACE对AQI预报有“中庸”倾向,即实况空气质量很好时倾向于报差一些,当空气质量很差时倾向于报好一些;空气质量为优时高估幅度最大,空气质量为严重污染时低估幅度最大。(3)高污染阶段,CUACE对PM_(2.5)、CO、SO_(2)的浓度预报存在系统性偏差;对PM_(10)、O_(3)的可预报性相对较差,订正难度较大。(4)PM_(2.5)、O_(3)和CO预报的相关系数随着预报时效的延长呈现递减性,各要素预报的各项检验指标随时效都存在以24 h为周期的峰值或谷值。周期性变化特征实际上是各污染物预报效果在一个自然日内变动的体现。(5)CUACE对AQI的预报效果很大程度上受到PM_(2.5)预报效果的影响。(6)CUACE对PM_(10)的预报效果最差,因为CUACE对沙尘天气预报性能较弱,对PM_(10)的预报需要结合专用的沙尘模式。对空气质量预报数值模式进一步检验,可以增加天气形势等维度、使用多维度同时检验的方法、采用多模式对比,从而为模式输出订正提供更好的参考。This article verifies and analyzes the air quality forecast performance of CUACE model in Henan Province from April,2017 to March,2021 by using the multi-dimensional grouping verification method.The results show that:(1)There exists overestimation in CUACE’s AQI forecasts generally.The model performs the best when the air quality is moderate,but the worst in the case of heavy pollution.The forecast performance of the model is better in June,July,August and September,while it is the worst in March.Its overall forecast performance in Nanyang is the best.(2)CUACE tends to follow“the Doctrine of the Mean”for AQI forecasts,that is,when the observed air quality is good,its forecast result tends to be poor,but when the air quality is poor,it tends to report better.The degree of overestimation is the greatest when air quality is good,while the degree of underestimation is the greatest when air is severely polluted.(3)During the intensive pollution stage,there is a systematic deviation in the CUACE forecasts of PM_(2.5),CO and SO_(2)concentrations.The predictability of PM_(10)and O_(3)is relatively poor,making their correction difficult.(4)The correlation coefficients of PM_(2.5),O_(3)and CO predictions decrease with the extension of the model forecast lead time,and each verification indicator of the forecast for pollutant concentration presents peak and trough values in the 24 h cycle.The periodic change feature is the manifestation of diurnal forecast effect variation for each pollutant.(5)The forecast performance of CUACE on AQI is largely affected by the forecast effect of PM_(2.5).(6)CUACE has the worst forecast performance on PM_(10)because of its weak forecast ability under the sand-dust weather condition.The forecast of PM_(10)may need to be combined with more specific dust models.Further verification of numerical models for air quality forecasting can provide even better reference for model output correction by adding dimensions such as weather patterns,using multi-dimensional verification methods simultane

关 键 词：环境气象 模式检验 空气质量预报 评估 

分 类 号：X513[环境科学与工程—环境工程] P456.8[天文地球—大气科学及气象学]