山西省高速公路大雾数值预报研究
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摘要
为了实现山西省高速公路大雾的数值预报,本文利用MM5中尺度数值预报模式,研究辐射和边界层参数化方案、模式分辨率、资料同化等对大雾中尺度数值预报的影响,确定基于模式预报结果的山西省大雾判别指标,构建山西省高速公路大雾数值预报系统。模拟的结果表明：
(1)模式分辨率的高低对模式模拟结果有一定的影响,其中在水平分辨率设计的方案(45km×15km、27km×9km、15km×5km)中以27km×9km为最佳方案,垂直分辨率设计的方案(23层、30层、43层)中以43层为最佳方案。综合考虑不同方案模拟的结果、模式运行效率及预报的时效性,山西省雾数值预报系统采用的分辨率为30层、27km×9km的方案。
(2)根据相似预报法(结构相似、指标相似、相关相似等)的原理,综合考虑模式模拟的气象要素值与观测值之间的误差、偏离程度、相关性,雾落区预报的准确性及雾的生消、发展高度等,确定山西省大雾数值预报系统采用的参数化方案为：辐射方案选用云方案,边界层参数化方案选用高分辨率的Blackadar方案,积云对流参数化方案选用Grell方案,下垫面采用多层土壤温度模式,水汽方案为graupel(reisner2)方案。
(3)在以NCEP的FNL再分析资料为初始场和边界文件的个例模拟中,加入观测资料可以改进模式初始场和模式模拟的精度,特别是同化探空资料对模式模拟精度有较高的提高,而地面资料的加入对模式模拟的结果的影响不是很明显,因此,在山西省雾数值预报系统中同化探空资料以提高系统预报的准确性。
(4)基于MM5模式预报结果,统计大雾天气相关预报因子特征,确定山西省大雾预报指标为：20m高度的液态水含量(LWC)大于等于0.13g/kg而小于0.6g/kg,10m风速小于4m/s,在20到1500m高度大气层存在逆温层。
(5)利用粒子数浓度的观测资料拟合空气质量模式CAPSS的预报信息,建立空气粒子数浓度(质量)的订正模型：SO2:N=0.8265*NCAPPS+0.0043,NO2:N=0.8265*NCAPPs+0.0057,PM10:N=0.8265.NCAPPs+0.0015；利用地面观测的相对湿度拟合MM5模式中个例模拟的相对湿度,建立相对湿度的订正模型：RH=(RHmodel—28.773)/0.7853；根据指数模型及观测资料(RH相对湿度、N为根据不同粒子质量数浓度获得的空气污染指数)构建山西省太原能见度预测模型：Vis=13.297*exp(-0.0002*RH*N).
依据上述的结论,构建了以MM5中尺度预报模式为基础的山西省高速公路大雾数值预报系统,系统可以自动从NCEP网站上获取全球的区域预报形势,同化地面和高空资料,实现山西省高速公路大雾滚动预报。系统可以提供每小时高速公路雾数值预报图片产品。
The study uses all kinds of information to establish the numerical prediction system of the fog about a highway in Shanxi Province, such as the data in2009-2011about the FNL NCEP data during the typical fog examples happened in Shanxi Province, data about the109stood observations, the information from MICAPS about the simulation area, humidity and visibility from January1st to September26th in2009at TaiYuan, the monitoring of air pollution index (API) from Shanxi Environmental Protection Net, the number of particles concentration from January in2008to November in2010at TaiYuan. Through comprehensive processing and analyzing, we can follow the conclusion:
(1) The high or the low resolution of the mode has certain effect to the simulation results, the27km×9km scheme is the best one in a horizontal resolution design scheme (45km×15km,27km×9km,15km×5km), the43th layer is the best one in the vertical resolution design scheme (23th layer,30th layer,43th layer). Considering the result between the simulation result of the model operation efficiency and the efficiency of the forecast, the resolution in the numerical prediction system of the fog in Shanxi Province uses the30th layer of27km×9km scheme.
(2) According to use the similar forecast method (structure similar, index similar, relevant similar, etc), considerated some reasons such as the error, deviation and the correlation between the model simulation result and the elements of observation, the accuracy in the distribution of fog simulated, procedure of fog and the height of fog top,etc, The numerical prediction system of the fog in Shanxi Province dopts the parameterization scheme for:radiation schemes choosing cloud solutions, boundary layer parameterization scheme selecting the high resolution Blackadar scheme, the parameterization scheme of cumulus clouds billow convection selectiing Grell scheme, the underlying surface soil temperature using multi-level pattern, graupel (reisner2) scheme for the vapor scheme.
(3)Data assimilation can improve the simulation of the initial field and the precision of the model simulations, especially adding sounding material, but the results of the simulation of model after adding the ground material is not very obvious. therefore, in order to improve the accuracy of the prediction of the system we should assimilate sounding and observation data in the numerical prediction system of the fog in Shanxi Province.
(4) As the results of analyzing the tree simulation experiments, the indices for retrieving fog area from the simulation data were that the liquid water content was more than0.13g/kg and less than0.6g/kg, there were temperature inversion layers during20-1500m, and wind speed near earth surface was less than4m/s. Using of air pollution, relative humidity, we could conclude the visibility model of fog:Vis=13.297*exp (0.0002*RH*N).
(5) Use the number of particles of the concentration of the observation data fitting air quality model CAPSS forecast information, establish the number of particles air concentration (quality) correction model:SO2:N=0.8265*NCApps+0.0043, NO2:N=0.8265*NCApps+0.0057, PM10:N=0.8265*NCApps+0.0015; Make use of the ground of the observation of relative humidity fitting MM5model simulation examples of relative humidity, establish the relative humidity correction model:RH=(RHmodel-28.773)/0.7853; According to the index model and observation data (RH is relative humidity, N according to different particles for such a concentration of air pollution index for build) taiyuan Shanxi Province visibility prediction model:Vis=13.297*exp (0.0002*RH*N).
Based on the above conclusions, to construct the mesoscale prediction model MM5with the basis of Shanxi Province highway fog numerical prediction system, the system can automatically from NCEP sites global area forecast situation, assimilation ground and high material, realize the Shanxi Province highway fog rolling forecast. The system can provide each hour highway fog numerical prediction product pictures.
(1)模式分辨率的高低对模式模拟结果有一定的影响,其中在水平分辨率设计的方案(45km×15km、27km×9km、15km×5km)中以27km×9km为最佳方案,垂直分辨率设计的方案(23层、30层、43层)中以43层为最佳方案。综合考虑不同方案模拟的结果、模式运行效率及预报的时效性,山西省雾数值预报系统采用的分辨率为30层、27km×9km的方案。
(2)根据相似预报法(结构相似、指标相似、相关相似等)的原理,综合考虑模式模拟的气象要素值与观测值之间的误差、偏离程度、相关性,雾落区预报的准确性及雾的生消、发展高度等,确定山西省大雾数值预报系统采用的参数化方案为：辐射方案选用云方案,边界层参数化方案选用高分辨率的Blackadar方案,积云对流参数化方案选用Grell方案,下垫面采用多层土壤温度模式,水汽方案为graupel(reisner2)方案。
(3)在以NCEP的FNL再分析资料为初始场和边界文件的个例模拟中,加入观测资料可以改进模式初始场和模式模拟的精度,特别是同化探空资料对模式模拟精度有较高的提高,而地面资料的加入对模式模拟的结果的影响不是很明显,因此,在山西省雾数值预报系统中同化探空资料以提高系统预报的准确性。
(4)基于MM5模式预报结果,统计大雾天气相关预报因子特征,确定山西省大雾预报指标为：20m高度的液态水含量(LWC)大于等于0.13g/kg而小于0.6g/kg,10m风速小于4m/s,在20到1500m高度大气层存在逆温层。
(5)利用粒子数浓度的观测资料拟合空气质量模式CAPSS的预报信息,建立空气粒子数浓度(质量)的订正模型：SO2:N=0.8265*NCAPPS+0.0043,NO2:N=0.8265*NCAPPs+0.0057,PM10:N=0.8265.NCAPPs+0.0015；利用地面观测的相对湿度拟合MM5模式中个例模拟的相对湿度,建立相对湿度的订正模型：RH=(RHmodel—28.773)/0.7853；根据指数模型及观测资料(RH相对湿度、N为根据不同粒子质量数浓度获得的空气污染指数)构建山西省太原能见度预测模型：Vis=13.297*exp(-0.0002*RH*N).
依据上述的结论,构建了以MM5中尺度预报模式为基础的山西省高速公路大雾数值预报系统,系统可以自动从NCEP网站上获取全球的区域预报形势,同化地面和高空资料,实现山西省高速公路大雾滚动预报。系统可以提供每小时高速公路雾数值预报图片产品。
The study uses all kinds of information to establish the numerical prediction system of the fog about a highway in Shanxi Province, such as the data in2009-2011about the FNL NCEP data during the typical fog examples happened in Shanxi Province, data about the109stood observations, the information from MICAPS about the simulation area, humidity and visibility from January1st to September26th in2009at TaiYuan, the monitoring of air pollution index (API) from Shanxi Environmental Protection Net, the number of particles concentration from January in2008to November in2010at TaiYuan. Through comprehensive processing and analyzing, we can follow the conclusion:
(1) The high or the low resolution of the mode has certain effect to the simulation results, the27km×9km scheme is the best one in a horizontal resolution design scheme (45km×15km,27km×9km,15km×5km), the43th layer is the best one in the vertical resolution design scheme (23th layer,30th layer,43th layer). Considering the result between the simulation result of the model operation efficiency and the efficiency of the forecast, the resolution in the numerical prediction system of the fog in Shanxi Province uses the30th layer of27km×9km scheme.
(2) According to use the similar forecast method (structure similar, index similar, relevant similar, etc), considerated some reasons such as the error, deviation and the correlation between the model simulation result and the elements of observation, the accuracy in the distribution of fog simulated, procedure of fog and the height of fog top,etc, The numerical prediction system of the fog in Shanxi Province dopts the parameterization scheme for:radiation schemes choosing cloud solutions, boundary layer parameterization scheme selecting the high resolution Blackadar scheme, the parameterization scheme of cumulus clouds billow convection selectiing Grell scheme, the underlying surface soil temperature using multi-level pattern, graupel (reisner2) scheme for the vapor scheme.
(3)Data assimilation can improve the simulation of the initial field and the precision of the model simulations, especially adding sounding material, but the results of the simulation of model after adding the ground material is not very obvious. therefore, in order to improve the accuracy of the prediction of the system we should assimilate sounding and observation data in the numerical prediction system of the fog in Shanxi Province.
(4) As the results of analyzing the tree simulation experiments, the indices for retrieving fog area from the simulation data were that the liquid water content was more than0.13g/kg and less than0.6g/kg, there were temperature inversion layers during20-1500m, and wind speed near earth surface was less than4m/s. Using of air pollution, relative humidity, we could conclude the visibility model of fog:Vis=13.297*exp (0.0002*RH*N).
(5) Use the number of particles of the concentration of the observation data fitting air quality model CAPSS forecast information, establish the number of particles air concentration (quality) correction model:SO2:N=0.8265*NCApps+0.0043, NO2:N=0.8265*NCApps+0.0057, PM10:N=0.8265*NCApps+0.0015; Make use of the ground of the observation of relative humidity fitting MM5model simulation examples of relative humidity, establish the relative humidity correction model:RH=(RHmodel-28.773)/0.7853; According to the index model and observation data (RH is relative humidity, N according to different particles for such a concentration of air pollution index for build) taiyuan Shanxi Province visibility prediction model:Vis=13.297*exp (0.0002*RH*N).
Based on the above conclusions, to construct the mesoscale prediction model MM5with the basis of Shanxi Province highway fog numerical prediction system, the system can automatically from NCEP sites global area forecast situation, assimilation ground and high material, realize the Shanxi Province highway fog rolling forecast. The system can provide each hour highway fog numerical prediction product pictures.
引文
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