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气象:2020,46(3):381-392
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全国网格化多模式集成空气质量预报的初步建立
张天航,迟茜元,张碧辉,张恒德,江琪,王继康,饶晓琴,谢超,吕梦瑶,安林昌,南洋
(国家气象中心,北京 100081)
Development of Gridding Multi-Model Ensemble Air Quality Forecast in China
ZHANG Tianhang,CHI Xiyuan,ZHANG Bihui,ZHANG Hengde,JIANG Qi,WANG Jikang,RAO Xiaoqin,XIE Chao,LYU Mengyao,AN Linchang,NAN Yang
(National Meteorological Centre, Beijing 100081)
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投稿时间:2019-04-29    修订日期:2020-01-12
中文摘要: 为降低单个模式预报的不确定性和提高多模式集成空气质量预报系统的精细化程度,利用Cressman插值初步建立了我国0.25°×0.25°网格化污染物实况。结合4套空气质量数值预报模式,通过均值集成、权重集成和多元线性回归集成分别逐格点建立了集成预报。在预报当天各单一模式和集成方法前50 d预报效果评估基础上,建立了最优集成预报。对2018年12月19—22日一次重污染过程中集成预报的PM2.5浓度评估结果显示:在污染较重时刻,最优集成预报与观测之间的归一化平均偏差(NMB)值在重污染地区保持在-20%~40%,对污染程度为良及以上区域的预报范围相较于单个模式更接近观测。整个过程中,最优集成在大部分污染区域与观测之间的NMB值为-20%~20%,均方根误差(RMSE)值为35~75 μg·m-3,相关系数(R)值大于0.4。相较于所有单一模式和其他集成方法,最优集成在全国最多的格点有着较高的总体评分。在污染最重区域的8个城市,最优集成预报的污染过程平均开始和结束时间分别比观测时间早1.8和6.9 h。未来需融合卫星反演和地表观测来提高网格化污染物实况的精细化程度,利用降尺度、主客观融合和滚动订正等方法进一步提高网格化多模式集成空气质量预报的准确率。
Abstract:To decrease the forecast uncertainties of single models and improve the refinement of multi-model ensemble air quality forecast system, the gridding observed pollutant concentration with resolution of 0.25°×0.25° was firstly established by using Cressman interpolation method. Then, combined with four numerical air quality forecast models, the mean, weighted and multiple linear regression ensembles were established in each grid, respectively. Finally, based on the evaluation results of single models and ensemble methods in previous 50 days, an optimal ensemble was established. The evaluation results of PM2.5 concentrations during a heavy pollution process in 19-22 December 2018 showed that in the case of heavy pollution, the NMB values between the optimal ensemble forecast and observations could also be maintained between -20% and 40%. And the forecast coverage area with good and above pollution by the optimal ensemble was closer to observation than those of single models. During the whole process, the NMB, root mean squared error (RMSE) and R values between forecasted PM2.5 concentrations by the optimal ensemble and observation were from -20% to 20%, from 35 to 75 μg·m-3 and higher than 0.4, respectively, in most polluted areas. Among all single models and ensemble methods, number of girds over China with high total scores was the largest in optimal ensemble. In the eight cities located in the most polluted region, the average onset and end times of the pollution process by optimal ensemble forecast was 1.8 and 6.9 h earlier than observation, respectively. Therefore, we propose that pollutant concentrations retrieval by satellite and surface observation should be fused to improve the refinement of gridding observed pollutant concentrations. And the methods of scale reduction, subjective and objective fusion and rolling correction should be used to further improve the forecast accuracy of gridding multi-model ensemble air quality forecast.
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基金项目:国家重点研发计划(2016YFC0203301)、中国气象局气象预报业务关键技术发展专项(YBGJXM2018-7A)和国家气象中心青年基金(Q201808)共同资助
引用文本:
张天航,迟茜元,张碧辉,张恒德,江琪,王继康,饶晓琴,谢超,吕梦瑶,安林昌,南洋,2020.全国网格化多模式集成空气质量预报的初步建立[J].气象,46(3):381-392.
ZHANG Tianhang,CHI Xiyuan,ZHANG Bihui,ZHANG Hengde,JIANG Qi,WANG Jikang,RAO Xiaoqin,XIE Chao,LYU Mengyao,AN Linchang,NAN Yang,2020.Development of Gridding Multi-Model Ensemble Air Quality Forecast in China[J].Meteor Mon,46(3):381-392.