| 基于气候系统指数的月尺度霾日定量预测方法研究——以芜湖市为例 |
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| 引用本文： | 付伟,司红君,何冬燕,卢尧,刘蕾,杨琼琼,邹莹瑾.基于气候系统指数的月尺度霾日定量预测方法研究——以芜湖市为例[J].大气科学学报,2021,44(2):270-278. |
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| 作者姓名： | 付伟 司红君 何冬燕 卢尧 刘蕾 杨琼琼 邹莹瑾 |
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| 作者单位： | 安徽省芜湖市气象局, 安徽 芜湖 241000;安徽省无为市气象局, 安徽 无为 238300;安徽省气候中心, 安徽 合肥 230061 |
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| 基金项目： | 安徽省自然科学基金资助项目（1308085MD55）；安徽省公益性研究联动计划项目（1604f0804003）；安徽省气象局预报员专项项目（kY202005） |
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| 摘 要： | 利用1955—2018年芜湖市国家气象观测站资料,1980—2018年国家气候中心气候系统指数资料,对芜湖市近64 a的霾日气候序列进行重建,在此基础上使用线性趋势和Mann-Kendall方法,系统分析了芜湖市霾日的气候特征。以芜湖市为例,借助多元逐步回归方法,尝试研究了一种以气候系统指数为自变量,霾日为因变量,建立霾日预测方程并对月尺度霾日进行定量预测的方法。结果表明:1)重建的霾日时间序列能够更客观的反映芜湖市霾日实际的长期变化特征。近64 a霾的气候特征:年日数显著增加,并在1980年左右出现突变;季日数在冬、秋季较多,夏季最少,四季均呈显著增多趋势,增速秋季最快,夏季最慢;月日数在1、12、11月较多,7、8月较少。中度以上霾的气候特征:年日数显著增加,表现出渐变特征;季日数冬季最多,夏季最少,除夏季外均显著增多趋势,增速冬季最快,秋、春季次之;月日数在1、12、11月较多,5、6月较少,8月则从未出现过。2)各月霾日、中度以上霾日与多项月气候系统指数表现出显著的相关性,使用这些指数,计算出各月霾日、中度以上霾日的预测方程,最后对方程效果进行检验。
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| 关 键 词： | 霾日 重建 气候系统指数 多元逐步回归 月尺度定量预测 |
| 收稿时间： | 2020/7/25 0:00:00 |
| 修稿时间： | 2020/12/3 0:00:00 |
The research on quantitative prediction method of monthly scale haze days based on climate system index-take Wuhu for example |
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| FU Wei,SI Hongjun,HE Dongyan,LU Yao,LIU Lei,YANG Qiongqiong,ZOU Yingjin.The research on quantitative prediction method of monthly scale haze days based on climate system index-take Wuhu for example[J].大气科学学报,2021,44(2):270-278. |
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| Authors: | FU Wei SI Hongjun HE Dongyan LU Yao LIU Lei YANG Qiongqiong ZOU Yingjin |
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| Institution: | Wuhu Meteorological Bureau, Wuhu 241000, China;Wuwei Meteorological Bureau, Wuwei 238300, China;Anhui Climate Centre, Hefei 230061, China |
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| Abstract: | In this article,the time series of haze days were constructed on the basis of past 64 years data which was collected from the national meteorological observatory of Wuhan and 130 climate system indexes of the National Climate Center during the time spread from 1955 to 2018.The linear trend analysis and Mann-Kendall were validated to analyze the climatic characteristics of haze days in Wuhan during this specific period.In the next stage of this research method,the stepwise multiple regression analysis was adopted in order to develop the prediction equations and quantitative monthly forecast scale of haze days.While in this piece of research,climate system index and haze days were considered as independent and dependent variables accordingly.The consequences of this research indicated that the reconstructed time series of haze days contemplated the actual long-term variations in haze days characteristics of Wuhan.During the selected span of time,the climatic characteristics in terms of annual haze days expanded significantly while the unanticipated variations were observed around the 1980s.It can also be observed that the span of seasonal haze days in winter and autumn comparatively more expanded than the summer.The number of seasonal haze days significantly increased in winter and autumn and in contrast decreased spring and summer seasons.The aggregate of monthly haze days in November,December,and January significantly increased while on other side declined in July and August.In the consideration of serious climatic conditions of haze days significantly increased and owned the gradient feature in winter and less effective in summer.The aggregate of moderate or more serious monthly haze days in November,December,and January significantly increased while on the other side declined in May,June,and August.Based on the outcome it can be concluded that discrete monthly haze days and harsh haze days owned a significant correlation with many monthly climate system indexes.The results showed that this prediction method had a great effect in Wuhu and wealth simplifying broadly in similar areas. |
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| Keywords: | haze days reconstruction climate system index stepwise multiple regression monthly scale quantitative prediction |
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