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Comparison of the performance of HYBRID ETKF-3DVAR and 3DVAR data assimilation scheme on the forecast of tropical cyclones formed over the Bay of Bengal
Journal of Earth System Science ( IF 1.9 ) Pub Date : 2020-11-21 , DOI: 10.1007/s12040-020-01497-8
Govindan Kutty , Rekha Gogoi , V Rakesh , M Pateria

Abstract

This study compares the performance of hybrid ensemble transform Kalman filter – three dimensional variational data assimilation (HYBRID) system and three dimensional variational (3DVAR) data assimilation system in Weather Research and Forecasting Model (WRF) in simulating tropical cyclones (TC) formed over the Bay of Bengal. An Ensemble Transform Kalman Filter (ETKF) system updates the ensemble system that provides flow-evolving background error covariance for HYBRID data assimilation system. Results indicate that use of flow-evolving ensemble error covariance in 3DVAR system has systematically reduced the TC position and intensity errors in the analysis; however, adding more weights to the ensemble error covariance term in 3DVAR cost function has not made any significant impact. The 3DVAR analysis depicts a stronger TC vortex with a well pronounced warm core structure as compared to that in HYBRID analysis. The forecasts from HYBRID analysis outperform that from 3DVAR in reducing TC track forecast error. The relative improvement in TC landfall position is 43% and 49% for variously configured HYBRID experiments. The forecasts initiated from HYBRID analysis has higher skill in quantitative precipitation forecasts during TC landfall compared to 3DVAR, which may be attributed to improved track prediction in the HYBRID experiments.

Highlights

  • Compared the performance of HYBRID and 3DVAR data assimilation system for Tropical cyclone forecasts.

  • HYBRID has systematically reduced the Tropical cyclone position and intensity errors in the analysis.

  • The forecasts from HYBRID analysis outperform that from 3DVAR in reducing TC track forecast error.

  • The forecasts initiated from HYBRID analysis has higher skill in quantitative precipitation forecasts during Tropical cyclone landfall compared to 3DVAR.



中文翻译:

HYBRID ETKF-3DVAR和3DVAR数据同化方案对孟加拉湾形成的热带气旋的预报性能比较

摘要

本研究在天气研究和预报模型(WRF)中比较了混合集合变换Kalman滤波器–三维变分数据同化(HYBRID)系统和三维变分(3DVAR)数据同化系统在模拟热带气旋(TC)上的性能。孟加拉湾。集成变换卡尔曼滤波器(ETKF)系统更新了集成系统,该系统为HYBRID数据同化系统提供了流演化的背景误差协方差。结果表明,在3DVAR系统中使用流演化集成误差协方差可以系统地减少分析中的TC位置和强度误差;但是,在3DVAR成本函数中的集合误差协方差项中增加更多权重并没有产生任何重大影响。3DVAR分析显示与HYBRID分析相比,TC涡旋更强,暖芯结构明显。HYBRID分析的预测在减少TC轨迹预测误差方面优于3DVAR。对于各种配置的HYBRID实验,TC登陆位置的相对改善分别为43%和49%。与3DVAR相比,由HYBRID分析发起的预测在TC登陆期间的定量降水预测方面具有更高的技能,这可能归因于HYBRID实验中改进的航迹预测。

强调

  • 比较了HYBRID和3DVAR数据同化系统对热带气旋预报的性能。

  • HYBRID在分析中系统地减少了热带气旋的位置和强度误差。

  • HYBRID分析的预测在减少TC轨迹预测误差方面优于3DVAR。

  • 与3DVAR相比,由HYBRID分析启动的预报在热带气旋登陆期间进行定量降水预报的技能更高。

更新日期:2020-11-22
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