LAUSR

A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/∼13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.摘要本文介绍了一套为业务快速更新预报系统建立的区域集合卡尔曼滤波和3D集合变分耦合的双分辨率混合同化系统. 双分辨率混合同化系统使用了确定性高分辨率背景场和低分辨率的集合卡尔曼滤波的集合扰动, 其中后者为高分辨率分析场的获得提供了流依赖背景误差协方差. 低分辨率的集合卡尔曼滤波分析和集合预报减小了计算成本. 基于9天每3小时循环, 和40/~13km分辨率的配置对双分辨率的混合同化系统进行了测试. 通过与GSI三维变分同化系统、和基于粗分辨率混合同化系统分析场插值场的高分辨率预报结果进行的对比和分析显示: 使用90%的集合流依赖背景误差协方差, 双分辨率混合同化系统能够获得最小预报误差, 并显著优于高分辨率GSI三维变分系统. 此外, 湿度和风场的预报明显优于粗分辨率混合同化系统分析场插值预报的结果, 但温度场不然. 对各变量的评分显示湿度预报提高最为显著. 并与之一致, 双分辨率混合同化系统相对于GSI三维变分系统, 得到了更为精确的降水预报; 并且, 除低阈值降水和初始预报阶段以外, 双分辨率混合同化系统优于粗分辨率混合同化系统分析场的插值场预报.