摘要
为了提高区域降水量观测的空间分辨率,提出了雷达-雨量计-粒子激光探测仪联合校准法。首先,利用粒子激光探测仪观测到的滴谱资料建立实时的Z-I关系,然后,利用变分法对同时有雷达回波和雨量计资料的点的实测校准因子进行校准,获得最优校准因子分析场,最后,对有雨量计的点取雨量计实际观测值,没有雨量计的点利用最优校准因子分析场估算降水。利用此方法对辽宁省2007年5月15日一次天气过程进行降水量估算,结果表明:雷达-雨量计-粒子激光探测仪联合校准法结合了雨量站观测资料单点精度高和雷达资料时空分辨率高的优点,提高了降水量的估算精度,更好地反映了降水的空间分布。
The purpose of this paper is to explore the performances of different model error scheme in soil moisture data assimilation.Based on the ensemble Kalman filter（EnKF） and the atmosphere-vegetation interaction model（AVIM）,point-scale analysis results for three schemes,1） covariance inflation（CI）,2） direct random disturbance（DRD）,and 3） source random disturbance（SRD）,are combined under conditions of different observational error estimations,different observation layers,and different observation intervals using a series of idealized experiments.The results shows that all these schemes obtain good assimilation results when the assumed observational error is an accurate statistical representation of the actual error used to perturb the original truth value,and the SRD scheme has the least root mean square error（RMSE）.Overestimation or underestimation of the observational errors can affect the assimilation results of CI and DRD schemes sensitively.The performances of these two schemes deteriorate obviously while the SRD scheme keeps its capability well.When the observation layers or observation interval increase,the performances of both CI and DRD schemes decline evidently.But for the SRD scheme,as it can assimilate multi-layer observations coordinately,the increased observations improve the assimilation results further.Moreover,as the SRD scheme contains a certain amount of model error estimation functions in its assimilation process,it also has a good performance in assimilating sparse-time observations.
出处
《大气科学》
CSCD
北大核心
2010年第3期513-519,共7页
Chinese Journal of Atmospheric Sciences
基金
国家自然科学基金资助项目40875080
国家自然科学基金资助项目40333033
国家科技支撑计划项目2006BAC12B00-1
辽宁省科技厅"十一五"重点攻关项目2006210001
关键词
激光探测仪
Z-I关系
降水量估算
雷达回波
ensemble Kalman filter
soil moisture
model error
error source
data assimilation
