The Numerical Prediction System Of The Fog In Shanxi Province

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.