近几年我国霾污染实时季节预测概要  

Outline of the real-time seasonal haze pollution prediction in China in recent years

作　　者：尹志聪[1,2] 王会军[1,2] 段明铿[1] YIN Zhicong;WANG Huijun;DUAN Mingkeng(Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)/Key Laboratory of Meteorological Disaster,Ministry of Education(KLME),Nanjing University of Information Science & Technology,Nanjing 210044,China;Nansen-Zhu International Research Centre,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China)

机构地区：[1]南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室/大气科学学院,江苏南京210044 [2]中国科学院大气物理研究所竺可桢-南森国际研究中心,北京100029

出　　处：《大气科学学报》2019年第1期2-13,共12页Transactions of Atmospheric Sciences

基　　金：国家自然科学基金资助项目(91744311;41705058)

摘　　要：近些年,中国东部经历了严重的霾污染,对人体健康、交通安全、生态系统以及社会经济有巨大的危害。在1周以内的霾污染预报之外,季节尺度的霾污染预测可以给减排治污措施的制定提供更长时间尺度的科学支撑。本文以年际增量为预测对象,选取前期外强迫因子为自变量,分别针对京津冀和长三角区域建立逐月的冬季霾日数季节尺度预测模型,并开展了实时的季节预测。总体来看,京津冀和长三角区域预测模型的性能大体处于相似的水平,均方根误差在2 d左右,对距平符号的捕捉率在80%以上,对霾日数变化的长期趋势具有很好的再现能力。在2016/2017年冬季京津冀霾日数实时预测中,模型预测的结果相对于常年值的定性结论全部准确,相对于前一年污染状况的结论大多数准确。在2017/2018年冬季长三角霾日数实时预测中,12月和1月的预测误差较小,2月的预测误差在2 d左右。In recent years,severe haze pollution has been damaging human health,traffic security,the ecosystem and social economy in eastern China.In addition to the haze forecast within 1 week,seasonal haze prediction provides scientific support for longer periods to the decisions of emission reduction.In this study,taking the annual increment as the predictand,monthly prediction models were trained for the Beijing-Tianjin-Hebei and Yangtze Delta regions.The performances of the built models were similar,with 2 days of root-mean-square error and a >80% simulation rate of the anomalies mathematical sign.In the real-time seasonal prediction for Beijing-Tianjin-Hebei haze days in the winter of 2016,the results with respect to the climate mean(the previous year) were completely (mostly) accurate.During the winter of 2017,the predicted biases for the December and January haze days in the Yangtze River Delta were very small,and the bias of February was nearly 2 days.

关 键 词：霾 污染 气候预测 减排 

分 类 号：X513[环境科学与工程—环境工程]