多元逐步回归与卡尔曼滤波法在霾预报中应用
|
摘要
针对目前霾预报的重要性和霾客观预报准确率较低,提出基于多元逐步回归算法和卡尔曼滤波算法的霾客观预报订正技术。利用多元逐步回归法控制因变量的物理因子,建立能见度预报方程,利用卡尔曼滤波法根据实况资料对多元逐步回归算法中回归系数进行订正,建立霾客观预报订正模型。以北京站、广州站、南京站、杭州站四个站为例,对站点进行预报实验和检验。实验结果表明,与业务上运行的雾-霾数值预报系统(CUACE)进行对比,提出的多元逐步回归与卡尔曼滤波法的预报准确率有所提高。
Considering the importance of objective haze forecast and the low accuracy rate of haze forecast,a new haze objective forecast correction method based on the multiple stepwise regression algorithms and the Kalman filtering algorithm is proposed. The multiple stepwise regression method is used to control the physical factor of the dependent variable, and the visibility forecast equation is established. The Kalman filtering method is adopted to correct the regression coefficient in multivariate stepwise regression algorithm according to the actual data, and the haze objective forecast correction model is established. The experiments are carried out in Beijing, Guangzhou, Nanjing and Hangzhou. The experimental results show that comparing with the operational running fog-haze numerical prediction system(CUACE), the prediction accuracy of the multiple stepwise regression and Kalman filtering method is improved.
Considering the importance of objective haze forecast and the low accuracy rate of haze forecast,a new haze objective forecast correction method based on the multiple stepwise regression algorithms and the Kalman filtering algorithm is proposed. The multiple stepwise regression method is used to control the physical factor of the dependent variable, and the visibility forecast equation is established. The Kalman filtering method is adopted to correct the regression coefficient in multivariate stepwise regression algorithm according to the actual data, and the haze objective forecast correction model is established. The experiments are carried out in Beijing, Guangzhou, Nanjing and Hangzhou. The experimental results show that comparing with the operational running fog-haze numerical prediction system(CUACE), the prediction accuracy of the multiple stepwise regression and Kalman filtering method is improved.
引文
[1]俞剑蔚,孙燕,张备,等.江苏沿江一次重霾天气成因分析[J].气象科学,2009,29(5):664-669.Jianwei Yu,Yan Sun,Bei Zhang,et al.Analysis on the cause of a heavy haze along the Yangtze River in Jiangsu[J].Meteorological Science,2009,29(5):664-669.
[2]穆泉,张世秋.2013年1月中国大面积雾霾事件直接社会经济损失评估[J].中国环境科学,2013,33(11):2087-2094.Quan Mu,Shiqiu Zhang.In January of 2003,Assessment of direct social and economic losses in China's large area haze events[J].Environmental Science in china,2013,33(11):2087-2094.
[3]吴兑,毕雪岩,邓雪娇,等.珠江三角洲大气灰霾导致能见度下降问题研究[J].气象学报,2006,64(4):510-516.Dui Wu,Xueyan Bi,Xuejiao Deng,et al.Study on the visibility reduction caused by atmospheric haze over the Pearl River Delta[J].Journal of Meteorology,2006,64(4):510-516.
[4]张运英,黄菲,杜鹃,等.广东雾霾天气能见度时空特征分析——年际年代际变化[J].热带地理,2009,29(4):324-327.Yunying Zhang,Fei Huang,Juan Du,et al.Spatial and temporal distribution of visibility under Fog and Haze conditions in Guangdong Province-Annual and Inter-Annual change[J].Tropical Geography,2009,29(4):324-327.
[5]姚剑,王广华,林俊,等.上海市大气颗粒物与能见度的关系[J].气象与环境学报,2010,26(4):17.Jian Yao,Guanghua Wang,Jun Lin,et al.The relationship between atmospheric particulates and visibility in Shanghai[J].Journal of Meteorology and Environment,2010,26(4):17.
[6]余锡刚,吴建,郦颖丽,等.灰霾天气与大气颗粒物的相关性研究综述[J].环境污染与防治,2009,20(2):252-256.Xigang Yu,Jian Wu,Yingli Li,et al.Review of researches on the correlation between atmospheric and particulate matter[J].Environmental pollution and Prevention,2009,20(2):252-256.
[7]刘红年,胡荣章,张美根.城市灰霾数值预报模式的建立与应用[J].环境科学研究,2009,22(6):631-636.Hongnian Liu,Rongzhang Hu,Meigen Zhang.Development and application of Urban Haze Numerical Forecast Model[J].Environmental Science Research,2009,22(6):631-636.
[8]尤佳红.上海地区霾集合预报研究[D].上海:华东师范大学,2014.Jiahong You.Study on haze ensemble forecast in Shanghai area[J].Shanghai:East China Normal University,2014.
[9]任万辉,苏枞枞,赵宏德.城市环境空气污染预报研究进展[J].环境保护科学,2010,36(3):9-11.Wanhui Ren,Congcong Su,Hongde Zhao.Research progress on air pollution forecast in urban environment[J].Environmental Protection Science,2010,36(3):9-11.
[10]任建伟,李永峰,韩生梅.基于灰色线性回归模型的预测控制[J].系统仿真技术,2012,8(2):144-148.Jianwei Ren,Yongfeng Li,Shengmei Han.Predictive control based on grey linear regression model[J].System Simulation Technology,2012,8(2):144-148.
[11]黄凤新.基于卡尔曼滤波的复杂地形WRF模式预报风速订正[D].南京:南京信息工程大学,2013.Fengxin Huang.Prediction of wind speed correction for complex terrain WRF model based on Kalman filtering[D].Nanjing:Nanjing University of Information Science and Technology,2013.
[2]穆泉,张世秋.2013年1月中国大面积雾霾事件直接社会经济损失评估[J].中国环境科学,2013,33(11):2087-2094.Quan Mu,Shiqiu Zhang.In January of 2003,Assessment of direct social and economic losses in China's large area haze events[J].Environmental Science in china,2013,33(11):2087-2094.
[3]吴兑,毕雪岩,邓雪娇,等.珠江三角洲大气灰霾导致能见度下降问题研究[J].气象学报,2006,64(4):510-516.Dui Wu,Xueyan Bi,Xuejiao Deng,et al.Study on the visibility reduction caused by atmospheric haze over the Pearl River Delta[J].Journal of Meteorology,2006,64(4):510-516.
[4]张运英,黄菲,杜鹃,等.广东雾霾天气能见度时空特征分析——年际年代际变化[J].热带地理,2009,29(4):324-327.Yunying Zhang,Fei Huang,Juan Du,et al.Spatial and temporal distribution of visibility under Fog and Haze conditions in Guangdong Province-Annual and Inter-Annual change[J].Tropical Geography,2009,29(4):324-327.
[5]姚剑,王广华,林俊,等.上海市大气颗粒物与能见度的关系[J].气象与环境学报,2010,26(4):17.Jian Yao,Guanghua Wang,Jun Lin,et al.The relationship between atmospheric particulates and visibility in Shanghai[J].Journal of Meteorology and Environment,2010,26(4):17.
[6]余锡刚,吴建,郦颖丽,等.灰霾天气与大气颗粒物的相关性研究综述[J].环境污染与防治,2009,20(2):252-256.Xigang Yu,Jian Wu,Yingli Li,et al.Review of researches on the correlation between atmospheric and particulate matter[J].Environmental pollution and Prevention,2009,20(2):252-256.
[7]刘红年,胡荣章,张美根.城市灰霾数值预报模式的建立与应用[J].环境科学研究,2009,22(6):631-636.Hongnian Liu,Rongzhang Hu,Meigen Zhang.Development and application of Urban Haze Numerical Forecast Model[J].Environmental Science Research,2009,22(6):631-636.
[8]尤佳红.上海地区霾集合预报研究[D].上海:华东师范大学,2014.Jiahong You.Study on haze ensemble forecast in Shanghai area[J].Shanghai:East China Normal University,2014.
[9]任万辉,苏枞枞,赵宏德.城市环境空气污染预报研究进展[J].环境保护科学,2010,36(3):9-11.Wanhui Ren,Congcong Su,Hongde Zhao.Research progress on air pollution forecast in urban environment[J].Environmental Protection Science,2010,36(3):9-11.
[10]任建伟,李永峰,韩生梅.基于灰色线性回归模型的预测控制[J].系统仿真技术,2012,8(2):144-148.Jianwei Ren,Yongfeng Li,Shengmei Han.Predictive control based on grey linear regression model[J].System Simulation Technology,2012,8(2):144-148.
[11]黄凤新.基于卡尔曼滤波的复杂地形WRF模式预报风速订正[D].南京:南京信息工程大学,2013.Fengxin Huang.Prediction of wind speed correction for complex terrain WRF model based on Kalman filtering[D].Nanjing:Nanjing University of Information Science and Technology,2013.