基于IBAM指數的重慶地區空氣污染氣象條件預報方法
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國家自然科學基金重大研究計劃重點支持項目“冬春季四川盆地西南渦活動對大氣復合污染影響與機制研究”(91644226)資助


Forecast Method of Meteorological Conditions of Air Pollution in Chongqing Based on IBAM Index
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    摘要:

    通過重慶城區2013—2016年空氣質量指數AQI與氣象要素的相關分析,引入表征大氣溫濕狀態的物理量總溫度、比濕、近地層風速、24 h變壓及大氣低層總溫度差,構建新的空氣污染氣象條件指數IBAM(Index Between Air pollution and Meteorology)。應用2013年4月1日至2016年12月31日歐洲中心預報產品計算重慶地區歷史IBAM指數,通過K均值聚類分析,引入極端天氣事件概念確定空氣污染氣象條件閾值,建立預報模型。利用IBAM指數與滯后1天AQI建立擬合曲線方程,計算出AQI預報值,計算預報準確率,經過2017年1月1日至2018年9月1日樣本檢驗,72 h內預報準確率在70%左右。通過誤差分析發現:當氣象條件為大氣污染物濃度主要影響因素且在大氣污染源變化不明顯時,預報誤差較小;而當大氣污染源變化明顯時,預報誤差較大。該預報方法已在重慶市氣象臺業務應用,對預防和處理重污染事件,改善重慶地區空氣質量有較好參考價值。

    Abstract:

    A new technique for forecasting meteorological conditions of air pollution is developed by the analysis based on Air Quality Index (AQI) of PM2.5 from 2013 to 2016. According to the correlation analysis between AQI and meteorological factors, the IBAM (Index Between Air pollution and Meteorology) is built by four factors (specific humidity, surface wind velocity, surface pressure change in 24 hours, and the difference of total temperature between two levels in the lower troposphere) from 2013 to 2016 in Chongqing. Then the past IBAM is attained by use of the EC numerical forecast products from 1 April 2013 to 31 〖JP2〗December 2016, and the prediction model of meteorological conditions is established by introducing the concepts of extreme weather events and Kmeans cluster analysis. The fitting curve equation between IBAM and AQI of 1day lagging can be used to calculate the forecast value of AQI; then according to the ranking standards of air quality, the forecasting accuracy of air pollution grades in 72h prediction reaches about 70% by a test with the samples of nearly two years (from 1 January 2017 to 1 September 2018). Through the error analysis of two air pollution events, the results show that the prediction of air pollution grades is relatively well when the meteorological condition is the major factor impacting the spread of the pollutants in atmosphere or the change of air pollution sources is not obvious (local accumulation as main air pollution mode). However, the prediction errors increase evidently when the change of air pollution sources is relatively remarkable because of upstream transportation. This forecast technique is applied in the realtime prediction services in the Chongqing Meteorological Observatory, which has important reference value for preventing heavy air pollution events and improving air quality in Chongqing.〖JP〗

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胡春梅,陳道勁,周國兵,王式功.基于IBAM指數的重慶地區空氣污染氣象條件預報方法[J].氣象科技,2020,48(5):741~751

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  • 收稿日期:2019-10-30
  • 定稿日期:2019-12-18
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  • 在線發布日期: 2020-10-26
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