The impact of deploying a flow-dependent ensemble error covariance in Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation (DA) system is examined for short-range rainfall forecasts during an Indian summer monsoon season. The flow-dependent background error covariance (BEC) is generated using a 50-member ensemble, which is further updated using the ensemble transform Kalman filter (ETKF). Assimilation is performed using a Hybrid variational-ensemble (“Hybrid”) and traditional 3DVAR DA system during the 4 weeks of July 2013. The forecasted wind, temperature, and rainfall from the assimilation experiments are verified against corresponding observations. The results indicate that the flow-dependent ensemble background error covariance in 3DVAR has systematically improved the forecasted wind and temperature when compared to the traditional 3DVAR. Similarly, rainfall forecast skill is superior in the Hybrid experiments relative to that of 3DVAR. Convection-permitting resolution rainfall forecast is validated against 746 telemetric rain gauge observations over the state of Karnataka. The Hybrid experiments show higher quantitative precipitation forecast skill than 3DVAR, particularly towards the later stages of data assimilation cycling. Spatially, the 3DVAR experiment shows a dry bias over the upper peninsular regions and a slight wet bias over the central and the northern Indian regions, while the magnitude of such wet and dry biases is smaller in forecasts from Hybrid analysis. Additionally, the westerly wind over the peninsular Indian landmass analyzed by 3DVAR is considerably weaker than that analyzed by the Hybrid experiments. This is proposed as a possible reason for the reduced dry bias in rainfall forecasts over the Indian landmass in Hybrid versus 3DVAR experiments.

中文翻译：

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印度夏季风季有限区域模型中混合 ETKF-3DVAR 和 3DVAR 资料同化系统对短期预报的性能比较

在天气研究和预报 (WRF) 三维变分 (3DVAR) 数据同化 (DA) 系统中部署与流量相关的集合误差协方差对印度夏季季风季节的短程降雨预报的影响进行了检查。流相关背景误差协方差 (BEC) 是使用 50 成员集成生成的，使用集成变换卡尔曼滤波器 (ETKF) 进一步更新。同化是在 2013 年 7 月的 4 周期间使用混合变分集合（“混合”）和传统 3DVAR DA 系统进行的。同化实验中预测的风、温度和降雨量将根据相应的观测结果进行验证。结果表明，与传统 3DVAR 相比，3DVAR 中与流相关的集合背景误差协方差系统地改进了预测的风和温度。同样，混合实验中的降雨预报技巧相对于 3DVAR 而言更为优越。允许对流的分辨率降雨预测根据卡纳塔克邦的 746 个遥测雨量计观测得到验证。Hybrid 实验显示出比 3DVAR 更高的定量降水预报技巧，特别是在数据同化循环的后期阶段。在空间上，3DVAR 实验显示了上半岛地区的干偏差和印度中部和北部地区的轻微湿偏差，而在混合分析的预测中，这种干湿偏差的幅度较小。此外，3DVAR 分析的印度半岛上的西风比混合实验分析的要弱得多。这被认为是在 Hybrid 与 3DVAR 实验中印度陆地降雨预测中干偏差减少的可能原因。