Radar reflectivity and radial velocity assimilation in a hybrid ETKF-3DVAR system for prediction of a heavy convective rainfall,Quarterly Journal of the Royal Meteorological Society

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Radar reflectivity and radial velocity assimilation in a hybrid ETKF-3DVAR system for prediction of a heavy convective rainfall
Quarterly Journal of the Royal Meteorological Society ( IF 3.0 ) Pub Date : 2021-03-20 , DOI: 10.1002/qj.4021
P Thiruvengadam ₁ , J. Indu _{1,

2} , Subimal Ghosh _{1,

2}

Affiliation

Reliable forecasting of heavy convective rainfall events continues to be one of the greatest challenges for numerical weather prediction models. Studies across the globe have indicated that Doppler weather radar (DWR) data assimilation through three-dimensional variational methods (3D-Var) has a considerable impact on improving the forecast skill of convective precipitation. However, the time-averaged background error statistics (BES) employed in 3D-Var fail to address the day-to-day variability of forecast errors in BES. To effectively represent the convective precipitation events through DWR assimilation, it is important to account for the day-to-day variability in background error's variance and correlations in the BES. In this study, the effect of assimilating DWR data using flow-dependent BES within the variational assimilation system is analysed via the hybrid Ensemble Transform Kalman Filter (ETKF)-3DVAR. For this, the functionality of ETKF in the hybrid ETKF-3DVAR has been extended to assimilate both DWR radial velocity and reflectivity information. The results suggest that assimilating DWR observations through the hybrid ETKF-3DVAR improves the forecast skills of convective precipitation as compared to the 3D-Var. It has also been observed that updating the ensemble states through assimilation of DWR observations in the ETKF system shows an increased wind and moisture convergence.

中文翻译：

用于预测强对流降雨的混合 ETKF-3DVAR 系统中的雷达反射率和径向速度同化

可靠地预测强对流降雨事件仍然是数值天气预报模型面临的最大挑战之一。全球研究表明，通过三维变分法（3D-Var）进行多普勒天气雷达（DWR）资料同化，对提高对流降水预报技术具有相当大的影响。然而，3D-Var 中采用的时间平均背景误差统计 (BES) 无法解决 BES 中预测误差的日常变化。为了通过 DWR 同化有效地表示对流降水事件，重要的是考虑 BES 中背景误差方差和相关性的日常变化。在这项研究中，通过混合集成变换卡尔曼滤波器 (ETKF)-3DVAR，分析了在变分同化系统中使用与流量相关的 BES 同化 DWR 数据的效果。为此，混合 ETKF-3DVAR 中 ETKF 的功能已扩展为同化 DWR 径向速度和反射率信息。结果表明，与 3D-Var 相比，通过混合 ETKF-3DVAR 同化 DWR 观测提高了对流降水的预测技能。还观察到，通过同化 ETKF 系统中的 DWR 观测来更新集合状态显示出风和水分会聚的增加。ETKF 在混合 ETKF-3DVAR 中的功能已扩展为同化 DWR 径向速度和反射率信息。结果表明，与 3D-Var 相比，通过混合 ETKF-3DVAR 同化 DWR 观测提高了对流降水的预测技能。还观察到，通过同化 ETKF 系统中的 DWR 观测来更新集合状态显示出风和水分会聚的增加。ETKF 在混合 ETKF-3DVAR 中的功能已扩展为同化 DWR 径向速度和反射率信息。结果表明，与 3D-Var 相比，通过混合 ETKF-3DVAR 同化 DWR 观测提高了对流降水的预测技能。还观察到，通过同化 ETKF 系统中的 DWR 观测来更新集合状态显示出风和水分会聚的增加。

更新日期：2021-03-20

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