Dust Storm Ensemble Forecast Experiments in East Asia  

作　　者：朱江 林彩燕 王自发 

机构地区：[1]State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics, Chinese Academy of Sciences

出　　处：《Advances in Atmospheric Sciences》2009年第6期1053-1070,共18页大气科学进展（英文版）

基　　金：supported by the Chinese Academy of Sciences (Grant Nos.KZCX2-YW-202, KZCX2-YW-205);the National Basic Research Program of China (2006CB403600);the National Natural Science Foundation of China (Grant No.40221503)

摘　　要：The ensemble Kalman filter （EnKF）, as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23-30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error means representing the model bias are estimated as part of the data assimilation process. Observations from a LIDAR network are assimilated to generate the initial ensembles and correct the model biases. The ensemble forecast skills are evaluated against the observations and a benchmark/control forecast, which is a simple model run without assimilation of any observations. Another ensemble forecast experiment is also performed without the model bias correction in order to examine the impact of the bias correction. Results show that the ensemble-mean, as deterministic forecasts have substantial improvement over the control forecasts and correctly captures the major dust arrival and cessation timing at each observation site. However, the forecast skill decreases as the forecast lead time increases. Bias correction further improved the forecasts in down wind areas. The forecasts within 24 hours are most improved and better than those without the bias correction. The examination of the ensemble forecast skills using the Brier scores and the relative operating characteristic curves and areas indicates that the ensemble forecasting system has useful forecast skills.The ensemble Kalman filter （EnKF）, as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23-30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error means representing the model bias are estimated as part of the data assimilation process. Observations from a LIDAR network are assimilated to generate the initial ensembles and correct the model biases. The ensemble forecast skills are evaluated against the observations and a benchmark/control forecast, which is a simple model run without assimilation of any observations. Another ensemble forecast experiment is also performed without the model bias correction in order to examine the impact of the bias correction. Results show that the ensemble-mean, as deterministic forecasts have substantial improvement over the control forecasts and correctly captures the major dust arrival and cessation timing at each observation site. However, the forecast skill decreases as the forecast lead time increases. Bias correction further improved the forecasts in down wind areas. The forecasts within 24 hours are most improved and better than those without the bias correction. The examination of the ensemble forecast skills using the Brier scores and the relative operating characteristic curves and areas indicates that the ensemble forecasting system has useful forecast skills.

关 键 词：dust storm ensemble forecast data assimilation bias correction 

分 类 号：P425.55[天文地球—大气科学及气象学]