霾天能见度参数化方案改进及预报效果评估
|
摘要
为了选择适合京津冀地区能见度预报的参数化方案,为霾的预报提供更准确的能见度预报产品.根据实际应用需求,改进了基于气溶胶体积浓度建立的Chen等能见度参数化方案(S1),并利用2017年2月快速更新多尺度分析和预报系统-化学子系统(RMAPS-CHEM v1. 0)的预报结果,对比评估了该方案与基于PM_(2.5)浓度建立的参数化方案(S2)和Mie散射计算方案(S3)在京津冀地区的预报效果.结果表明:①模式系统对京津冀地区的PM_(2.5)浓度预报总体较好,预报值与观测值非常接近,二者的相关系数在大部分地区可达0. 8以上,小时相对湿度的预报值与观测值相关在0. 78以上,平均误差低于3. 91%.②三套方案计算的能见度都能较好地预报出2017年2月京津冀地区能见度的时间演变趋势,且在大部分时间三者计算的能见度都非常接近.总体上S1计算的能见度最低,S3计算的能见度最高,S2居中.在京津冀大部分地区,S1的均方根误差和归一化平均绝对误差最低,S3的最高,S2居中且在北京地区表现最佳.③能见度大于10 km时三套方案计算结果都偏低,其中S3的平均误差和均方根误差最低,能见度低于10 km时,特别是对出现频率较高的1～5 km低能见度的预报,S1方案的平均误差、均方根误差和归一化平均绝对误差都是最低的,更适合京津冀地区霾天气能见度的预报应用.
In order to select a visibility parameterization scheme that can be applied well to the Beijing-Tianjin-Hebei( BTH) region and provide better forecasting,a modified parameterization of visibility based on the aerosol volume concentration and RH is developed in this study. This upgraded parameterization scheme( S1) and other schemes based on PM_(2.5) and RH( S2) and Mie theory( S3) are evaluated using forecast data from Rapid refresh Multi-scale Analysis & Prediction System-CHEM( RMAPS-CHEM v1. 0). A performance test using data from February 2017 showed that: ① The concentration of PM_(2.5) is forecast well in the BTH region. The correlation coefficients of the observed and forecast daily average PM_(2.5) in most areas are higher than 0. 8,and the forecasted values are close to those observed. The mean errors( ME) are-7. 54,-0. 46,and~(-1)1. 0 μg·m~(-3) for the forecast domain,south and north of Hebei province,and 12. 04,2. 02,and~(-1)3. 31 μg·m~(-3) for the cities of Beijing,Tianjin,and Shijiazhuang,respectively. The correlation coefficients for the forecast and observed hourly relative humidity in the three typical cities are above 0. 78,and the mean errors are lower than 3. 91%. ② All three parameterization schemes predict the time evaluation of visibility in the BTH region during February 2017 well. In general,the visibility predicted with S1 is the lowest,while that of S3 is the highest; the predictions of S2 are intermediate. In most areas of the BTH region,S1 has the minimum root mean squared error( RMSE) and normalized mean error( NME) between the observed and forecast visibility,while S3 has the maximum RMSE and NME. The error of S2 is between that of S1 and S3,but it shows the best performance in the Beijing area. ③ When the observed visibility is higher than 10 km,the predicted visibilities of the three schemes are all lower than the observed visibility,and S3 has the lowest mean error( ME) and RMSE. S1 has the lowest MB,RMSE,and NME when the visibility is lower than 10 km,especially for visibilities of 1 km to 5 km,which occurred more frequently during heavy haze episodes. The comparison of the results indicated that S1 is best for application to haze forecasting in the BTH region.
In order to select a visibility parameterization scheme that can be applied well to the Beijing-Tianjin-Hebei( BTH) region and provide better forecasting,a modified parameterization of visibility based on the aerosol volume concentration and RH is developed in this study. This upgraded parameterization scheme( S1) and other schemes based on PM_(2.5) and RH( S2) and Mie theory( S3) are evaluated using forecast data from Rapid refresh Multi-scale Analysis & Prediction System-CHEM( RMAPS-CHEM v1. 0). A performance test using data from February 2017 showed that: ① The concentration of PM_(2.5) is forecast well in the BTH region. The correlation coefficients of the observed and forecast daily average PM_(2.5) in most areas are higher than 0. 8,and the forecasted values are close to those observed. The mean errors( ME) are-7. 54,-0. 46,and~(-1)1. 0 μg·m~(-3) for the forecast domain,south and north of Hebei province,and 12. 04,2. 02,and~(-1)3. 31 μg·m~(-3) for the cities of Beijing,Tianjin,and Shijiazhuang,respectively. The correlation coefficients for the forecast and observed hourly relative humidity in the three typical cities are above 0. 78,and the mean errors are lower than 3. 91%. ② All three parameterization schemes predict the time evaluation of visibility in the BTH region during February 2017 well. In general,the visibility predicted with S1 is the lowest,while that of S3 is the highest; the predictions of S2 are intermediate. In most areas of the BTH region,S1 has the minimum root mean squared error( RMSE) and normalized mean error( NME) between the observed and forecast visibility,while S3 has the maximum RMSE and NME. The error of S2 is between that of S1 and S3,but it shows the best performance in the Beijing area. ③ When the observed visibility is higher than 10 km,the predicted visibilities of the three schemes are all lower than the observed visibility,and S3 has the lowest mean error( ME) and RMSE. S1 has the lowest MB,RMSE,and NME when the visibility is lower than 10 km,especially for visibilities of 1 km to 5 km,which occurred more frequently during heavy haze episodes. The comparison of the results indicated that S1 is best for application to haze forecasting in the BTH region.
引文
[1]吴兑.灰霾天气的形成与演化[J].环境科学与技术,2011,34(3):157-161.Wu D. Formation and evolution of haze weather[J].Environmental Science,2011,34(3):157-161.
[2] Zhao P S,Zhang X L,Xu X F,et al. Long-term visibility trends and characteristics in the region of Beijing,Tianjin,and Hebei,China[J]. Atmospheric Research,2011,101(3):711-718.
[3]符传博,唐家翔,丹利,等. 1960~2013年我国霾污染的时空变化[J].环境科学,2016,37(9):3237-3248.Fu C B,Tang J X,Dan L,et al. Temporal and spatial variation of haze pollution over China from 1960 to 2013[J].Environmental Science,2016,37(9):3237-3248.
[4] Quan J,Zhang Q,He H,et al. Analysis of the formation of fog and haze in North China Plain(NCP)[J]. Atmospheric Chemistry and Physics,2011,11(15):8205-8214.
[5]吴波,胡邦辉,王学忠,等.基于近似支持向量机的能见度释用预报研究[J].热带气象学报,2017,33(1):104-110.Wu B,Hu B H,Wang X Z,et al. Visibility forecast based on proximal support vector machine[J]. Journal of Tropical Meteorology,2017,33(1):104-110.
[6]张恒德,咸云浩,谢永华,等.基于时间序列分析和卡尔曼滤波的霾预报技术[J].计算机应用,2017,37(11):3311-3316.Zhang H D,Xian Y H,Xie Y H,et al. Haze forecast based on time series analysis and Kalman filtering[J]. Journal of Computer Applications,2017,37(11):3311-3316.
[7]李沛,王式功,尚可政,等.基于神经网络逐级分类建模的北京地区能见度预报[J].兰州大学学报(自然科学版),2012,48(3):52-57.Li P,Wang S G,Shang K Z,et al. Visibility forecast in Beijing through artificial neural network based on hierarchical classification method[J]. Journal of Lanzhou University(Natural Sciences),2012,48(3):52-57.
[8]张自银,赵秀娟,熊亚军,等.基于动态统计预报方法的京津冀雾霾中期预报试验[J].应用气象学报,2018,29(1):57-69.Zhang Z Y,Zhao X J,Xiong Y J,et al. The fog/haze mediumrange forecast experiments based on dynamic statistic method[J]. Journal of Applied Meteorological Science,2018,29(1):57-69.
[9]白永清,祁海霞,刘琳,等.武汉大气能见度与PM2. 5浓度及相对湿度关系的非线性分析及能见度预报[J].气象学报,2016,74(2):189-199.Bai Y Q, Qi H X, Liu L, et al. Study on the nonlinear relationship among the visibility,PM2. 5concentration and relative humidity in Wuhan and the visibility prediction[J]. Acta Meteorologica Sinica,2016,74(2):189-199.
[10] Stoelinga M T,Warner T T. Nonhydrostatic,Mesobeta-scale model simulations of cloud ceiling and visibility for an east coast winter precipitation event[J]. Journal of Applied Meteorology,1999,38(4):385-404.
[11]邓涛,邓雪娇,吴兑,等.珠三角灰霾数值预报模式与业务运行评估[J].气象科技进展,2012,2(6):38-44.Deng T,Deng X J,Wu D,et al. Study on Numerical forecast model of haze over Pearl River Delta region and routine business assessment[J]. Advances in Meteorological Science and Technology,2012,2(6):38-44.
[12]侯梦玲,王宏,赵天良,等.京津冀一次重度雾霾天气能见度及边界层关键气象要素的模拟研究[J].大气科学,2017,41(6):1177-1190.Hou M L,Wang H,Zhao T L,et al. A modeling study of the visibility and PBL key meteorological elements during a heavy foghaze episode in Beijing-Tianjin-Hebei of China[J]. Chinese Journal of Atmospheric Sciences,2017,41(6):1177-1190.
[13] Wang T J,Jiang F,Deng J J,et al. Urban air quality and regional haze weather forecast for Yangtze River Delta region[J].Atmospheric Environment,2012,58:70-83.
[14]周斌斌,蒋乐,杜钧.航空气象要素以及基于数值模式的低能见度和雾的预报[J].气象科技进展,2016,6(2):29-41.Zhou B B,Jiang L,Du J. Aviation weather and model-based operational forecasts of low visibility and fog[J]. Advances in Meteorological Science and Technology,2016,6(2):29-41.
[15] Malm W C,Day D E,Carrico C,et al. Intercomparison and closure calculations using measurements of aerosol species and optical properties during the Yosemite aerosol characterization study[J]. Journal of Geophysical Research,2005,110(D14):D14302,doi:10. 1029/2004JD005494.
[16] Malm W C,Hand J L. An examination of the physical and optical properties of aerosols collected in the IMPROVE program[J]. Atmospheric Environment,2007,41(16):3407-3427.
[17] Pitchford M,Malm W,Schichtel B,et al. Revised algorithm for estimating light extinction from IMPROVE particle speciation data[J]. Journal of the Air&Waste Management Association,2007,57(11):1326-1336.
[18] Chen J,Zhao C S,Ma N,et al. A parameterization of low visibilities for hazy days in the North China Plain[J].Atmospheric Chemistry and Physics,2012,12(11):4935-4950.
[19]胡俊,赵天良,张泽锋,等.霾污染环境大气能见度参数化方案的改进[J].环境科学研究,2017,30(11):1680-1688.Hu J,Zhao T L,Zhang Z F,et al. Upgradeding atmospheric visibility parameterization scheme for haze pollution environment[J]. Research of Environmental Sciences,2017,30(11):1680-1688.
[20]陈一娜,赵普生,何迪,等.北京地区大气消光特征及参数化研究[J].环境科学,2015,36(10):3582-3589.Chen Y N, Zhao P S, He D, et al. Characteristics and parameterization for atmospheric extinction coefficient in Beijing[J]. Environmental Science,2015,36(10):3582-3589.
[21] Deng H,Tan H B,Li F,et al. Impact of relative humidity on visibility degradation during a haze event:a case study[J].Science of the Total Environment,2016,569-570:1149-1158.
[22] Wang Y, Wu Z J, Ma N, et al. Statistical analysis and parameterization of the hygroscopic growth of the sub-micrometer urban background aerosol in Beijing[J]. Atmospheric Environment,2018,175:184-191.
[23]赵秀娟,徐敬,张自银,等.北京区域环境气象数值预报系统及PM2. 5预报检验[J].应用气象学报,2016,27(2):160-172.Zhao X J, Xu J, Zhang Z Y, et al. Beijing regional environmental meteorology prediction system and its performance test of PM2. 5concentration[J]. Journal of Applied Meteorological Science,2016,27(2):160-172.
[24] Grell G A,Peckham S E,Schmitz R,et al. Fully coupled"online"chemistry within the WRF model[J]. Atmospheric Environment,2005,39(37):6957-6975.
[25]程兴宏,刁志刚,胡江凯,等.基于CMAQ模式和自适应偏最小二乘回归法的中国地区PM2. 5浓度动力-统计预报方法研究[J].环境科学学报,2016,36(8):2771-2782.Cheng X H,Diao Z G,Hu J K,et al. Dynamical-statistical forecasting of PM2. 5concentration based on CMAQ model and adapting partial least square regression method in China[J].Acta Scientiae Circumstantiae,2016,36(8):2771-2782.
[26]王哲,王自发,李杰,等.气象—化学双向耦合模式(WRFNAQPMS)研制及其在京津冀秋季重霾模拟中的应用[J].气候与环境研究,2014,19(2):153-163.Wang Z,Wang Z F,Li J,et al. Development of a meteorologychemistry two-way coupled numerical model(WRF-NAQPMS)and its application in a severe autumn haze simulation over the Beijing-Tianjin-Hebei area, China[J]. Climatic and Environmental Research,2014,19(2):153-163.
[2] Zhao P S,Zhang X L,Xu X F,et al. Long-term visibility trends and characteristics in the region of Beijing,Tianjin,and Hebei,China[J]. Atmospheric Research,2011,101(3):711-718.
[3]符传博,唐家翔,丹利,等. 1960~2013年我国霾污染的时空变化[J].环境科学,2016,37(9):3237-3248.Fu C B,Tang J X,Dan L,et al. Temporal and spatial variation of haze pollution over China from 1960 to 2013[J].Environmental Science,2016,37(9):3237-3248.
[4] Quan J,Zhang Q,He H,et al. Analysis of the formation of fog and haze in North China Plain(NCP)[J]. Atmospheric Chemistry and Physics,2011,11(15):8205-8214.
[5]吴波,胡邦辉,王学忠,等.基于近似支持向量机的能见度释用预报研究[J].热带气象学报,2017,33(1):104-110.Wu B,Hu B H,Wang X Z,et al. Visibility forecast based on proximal support vector machine[J]. Journal of Tropical Meteorology,2017,33(1):104-110.
[6]张恒德,咸云浩,谢永华,等.基于时间序列分析和卡尔曼滤波的霾预报技术[J].计算机应用,2017,37(11):3311-3316.Zhang H D,Xian Y H,Xie Y H,et al. Haze forecast based on time series analysis and Kalman filtering[J]. Journal of Computer Applications,2017,37(11):3311-3316.
[7]李沛,王式功,尚可政,等.基于神经网络逐级分类建模的北京地区能见度预报[J].兰州大学学报(自然科学版),2012,48(3):52-57.Li P,Wang S G,Shang K Z,et al. Visibility forecast in Beijing through artificial neural network based on hierarchical classification method[J]. Journal of Lanzhou University(Natural Sciences),2012,48(3):52-57.
[8]张自银,赵秀娟,熊亚军,等.基于动态统计预报方法的京津冀雾霾中期预报试验[J].应用气象学报,2018,29(1):57-69.Zhang Z Y,Zhao X J,Xiong Y J,et al. The fog/haze mediumrange forecast experiments based on dynamic statistic method[J]. Journal of Applied Meteorological Science,2018,29(1):57-69.
[9]白永清,祁海霞,刘琳,等.武汉大气能见度与PM2. 5浓度及相对湿度关系的非线性分析及能见度预报[J].气象学报,2016,74(2):189-199.Bai Y Q, Qi H X, Liu L, et al. Study on the nonlinear relationship among the visibility,PM2. 5concentration and relative humidity in Wuhan and the visibility prediction[J]. Acta Meteorologica Sinica,2016,74(2):189-199.
[10] Stoelinga M T,Warner T T. Nonhydrostatic,Mesobeta-scale model simulations of cloud ceiling and visibility for an east coast winter precipitation event[J]. Journal of Applied Meteorology,1999,38(4):385-404.
[11]邓涛,邓雪娇,吴兑,等.珠三角灰霾数值预报模式与业务运行评估[J].气象科技进展,2012,2(6):38-44.Deng T,Deng X J,Wu D,et al. Study on Numerical forecast model of haze over Pearl River Delta region and routine business assessment[J]. Advances in Meteorological Science and Technology,2012,2(6):38-44.
[12]侯梦玲,王宏,赵天良,等.京津冀一次重度雾霾天气能见度及边界层关键气象要素的模拟研究[J].大气科学,2017,41(6):1177-1190.Hou M L,Wang H,Zhao T L,et al. A modeling study of the visibility and PBL key meteorological elements during a heavy foghaze episode in Beijing-Tianjin-Hebei of China[J]. Chinese Journal of Atmospheric Sciences,2017,41(6):1177-1190.
[13] Wang T J,Jiang F,Deng J J,et al. Urban air quality and regional haze weather forecast for Yangtze River Delta region[J].Atmospheric Environment,2012,58:70-83.
[14]周斌斌,蒋乐,杜钧.航空气象要素以及基于数值模式的低能见度和雾的预报[J].气象科技进展,2016,6(2):29-41.Zhou B B,Jiang L,Du J. Aviation weather and model-based operational forecasts of low visibility and fog[J]. Advances in Meteorological Science and Technology,2016,6(2):29-41.
[15] Malm W C,Day D E,Carrico C,et al. Intercomparison and closure calculations using measurements of aerosol species and optical properties during the Yosemite aerosol characterization study[J]. Journal of Geophysical Research,2005,110(D14):D14302,doi:10. 1029/2004JD005494.
[16] Malm W C,Hand J L. An examination of the physical and optical properties of aerosols collected in the IMPROVE program[J]. Atmospheric Environment,2007,41(16):3407-3427.
[17] Pitchford M,Malm W,Schichtel B,et al. Revised algorithm for estimating light extinction from IMPROVE particle speciation data[J]. Journal of the Air&Waste Management Association,2007,57(11):1326-1336.
[18] Chen J,Zhao C S,Ma N,et al. A parameterization of low visibilities for hazy days in the North China Plain[J].Atmospheric Chemistry and Physics,2012,12(11):4935-4950.
[19]胡俊,赵天良,张泽锋,等.霾污染环境大气能见度参数化方案的改进[J].环境科学研究,2017,30(11):1680-1688.Hu J,Zhao T L,Zhang Z F,et al. Upgradeding atmospheric visibility parameterization scheme for haze pollution environment[J]. Research of Environmental Sciences,2017,30(11):1680-1688.
[20]陈一娜,赵普生,何迪,等.北京地区大气消光特征及参数化研究[J].环境科学,2015,36(10):3582-3589.Chen Y N, Zhao P S, He D, et al. Characteristics and parameterization for atmospheric extinction coefficient in Beijing[J]. Environmental Science,2015,36(10):3582-3589.
[21] Deng H,Tan H B,Li F,et al. Impact of relative humidity on visibility degradation during a haze event:a case study[J].Science of the Total Environment,2016,569-570:1149-1158.
[22] Wang Y, Wu Z J, Ma N, et al. Statistical analysis and parameterization of the hygroscopic growth of the sub-micrometer urban background aerosol in Beijing[J]. Atmospheric Environment,2018,175:184-191.
[23]赵秀娟,徐敬,张自银,等.北京区域环境气象数值预报系统及PM2. 5预报检验[J].应用气象学报,2016,27(2):160-172.Zhao X J, Xu J, Zhang Z Y, et al. Beijing regional environmental meteorology prediction system and its performance test of PM2. 5concentration[J]. Journal of Applied Meteorological Science,2016,27(2):160-172.
[24] Grell G A,Peckham S E,Schmitz R,et al. Fully coupled"online"chemistry within the WRF model[J]. Atmospheric Environment,2005,39(37):6957-6975.
[25]程兴宏,刁志刚,胡江凯,等.基于CMAQ模式和自适应偏最小二乘回归法的中国地区PM2. 5浓度动力-统计预报方法研究[J].环境科学学报,2016,36(8):2771-2782.Cheng X H,Diao Z G,Hu J K,et al. Dynamical-statistical forecasting of PM2. 5concentration based on CMAQ model and adapting partial least square regression method in China[J].Acta Scientiae Circumstantiae,2016,36(8):2771-2782.
[26]王哲,王自发,李杰,等.气象—化学双向耦合模式(WRFNAQPMS)研制及其在京津冀秋季重霾模拟中的应用[J].气候与环境研究,2014,19(2):153-163.Wang Z,Wang Z F,Li J,et al. Development of a meteorologychemistry two-way coupled numerical model(WRF-NAQPMS)and its application in a severe autumn haze simulation over the Beijing-Tianjin-Hebei area, China[J]. Climatic and Environmental Research,2014,19(2):153-163.