Assimilation of multi-time radar observations with WRF-based Ensemble Kalman Filter

Yi Yang, Xiaobin Qiu, Zhenghu Wang, D. Wen, A. Shao
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Abstract

Weather Doppler radar can provide high spatial and temporal resolution observations, but most observations are discarded during data assimilation. Only the radar data observed at assimilation time are used in operational prediction. In this paper, the radial velocity observation that not only at the assimilation time but also at some earlier and later time are all assimilated with Ensemble Square Root Kalman Filter (EnSRF) based on WRF model. Result shows that the new scheme produces better analysis than the traditional scheme which only uses radar observations at the assimilation time. The improvement is especially clear in the first several assimilation cycles and then decreases with following assimilation cycles generally.
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基于wrf的集合卡尔曼滤波同化多时间雷达观测
天气多普勒雷达可以提供高时空分辨率的观测资料,但在同化过程中大部分观测资料被丢弃。只有同化时观测到的雷达资料才用于作战预报。本文采用基于WRF模型的合集平方根卡尔曼滤波(EnSRF)对同化时间的径向速度观测进行同化,不仅同化了同化时间,而且同化了同化前和同化后的径向速度观测。结果表明,新方案比仅利用同化时段雷达观测的传统方案具有更好的分析效果。这种改善在前几个同化周期中尤其明显,然后在随后的同化周期中普遍下降。
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