卫星气溶胶光学厚度资料同化对灰霾预报改进个例研究

A study on effects of satellite based AOD data assimilation on numerical forecast of haze

面对日益严峻的大气污染形势,针对卫星气溶胶光学厚度(AOD)资料在灰霾数值预报领域的合理有效利用问题,使用WRF-Chem(WRF coupled with Chemistry)大气化学模式以及GSI(Gridpoint Statistical Interpolation)三维变分同化系统,利用MODIS和FY-3A/MERSI AOD资料,对一次灰霾天气过程进行了同化预报试验。试验结果显示,同化卫星AOD资料有效改善了模式初始场,MODIS和MERSI同化试验分别在AOD分析场的中心强度和空间分布各具优势,且对PM_(2.5)和PM_(10)的后续预报改进明显;从统计分析上看,同化试验的预报效果整体上好于控制试验,同化试验中PM_(2.5)和PM_(10)预报值的平均值、中值、平均偏差、均方根误差等指标均明显优于控制试验,且MODIS和MERSI同化试验分别在PM_(2.5)和PM_(10)预报统计结果中体现出了优势;卫星AOD资料同化能明显降低污染事件的空报率和漏报率,提高预报的TS评分和ETS评分。不同卫星AOD资料的差异对分析场中AOD的分布和强度产生了相应影响,进而影响了模式的灰霾预报效果;本次试验中,MODIS和MERSI AOD同化试验分别在PM_(2.5)和PM_(10)预报的评分上表现更佳。结果表明,卫星AOD资料同化对数值预报产生了积极的效果。

Facing the increasingly severe condition of air pollution,it is important to make rational and effective use of satellite data to improve the quality of numerical forecast of haze.In this study,the WRF-Chem（WRF coupled with Chemistry）model and GSI（Gridpoint Statistical Interpolation）3 D-VAR assimilation system are applied to assimilate MODIS and FY-3 A/MERSI AOD（Aerosol optical depth）to simulate a haze case over China.Results indicate that assimilating AOD data can significantly improve the initial field,and MODIS and MERSI assimilation experiments have advantages in simulations of the center intensity and spatial distribution of AOD,respectively.AOD data assimilation has a positive effect on the subsequent forecast of PM_（2.5） and PM_（10）,while the strength and distribution simulation of each individual variable is improved reasonably.From the perspective of statistical analysis,mean biases and root-mean-square errors of PM_（2.5） and PM_（10） both reduce dramatically,and the simulated mean values and median values of the two pollutants are closer to observations.Satellite AOD data assimilation has obvious regional and individual characteristics for the improvement of particulate matter prediction.MODIS and MERSI assimilation experiment showed advantages in the prediction of PM_（2.5） and PM_（10）,respectively.In addition,the experiment results suggest that AOD data assimilation can significantly improve the forecast accuracy of haze weather.MODIS and MERSI assimilation experiments show some differences in the accuracy of PM_（2.5） and PM_（10） prediction.The assimilation of AOD data reduces the nohitting rate and false-hitting rate of PM_（2.5） and PM_（10） forecast,and improves the TS score and ETS score.For distinctive satellite AOD data,the assimilation of AOD data has different effects on the intensity and distribution of AOD in the initial field,which also leads to different results in the numerical forecast.In this case,The MODIS AOD DA experiment proves to be advantageous in PM_（2.5） forecast while MERSI is advantageous in PM_（10） simulation.The results reveal that AOD satellite data assimilation has a positive effect on numerical prediction,which implies the broad prospect of AOD data assimilation in the application of air quality forecast.