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A comparison between EDA-EnVar and ETKF-EnVar data assimilation techniques using radar observations at convective scales through a case study of Hurricane Ike (2008)

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Abstract

This study examines the impacts of assimilating radar radial velocity (Vr) data for the simulation of hurricane Ike (2008) with two different ensemble generation techniques in the framework of the hybrid ensemble-variational (EnVar) data assimilation system of Weather Research and Forecasting model. For the generation of ensemble perturbations we apply two techniques, the ensemble transform Kalman filter (ETKF) and the ensemble of data assimilation (EDA). For the ETKF-EnVar, the forecast ensemble perturbations are updated by the ETKF, while for the EDA-EnVar, the hybrid is employed to update each ensemble member with perturbed observations. The ensemble mean is analyzed by the hybrid method with flow-dependent ensemble covariance for both EnVar. The sensitivity of analyses and forecasts to the two applied ensemble generation techniques is investigated in our current study. It is found that the EnVar system is rather stable with different ensemble update techniques in terms of its skill on improving the analyses and forecasts. The EDA-EnVar-based ensemble perturbations are likely to include slightly less organized spatial structures than those in ETKF-EnVar, and the perturbations of the latter are constructed more dynamically. Detailed diagnostics reveal that both of the EnVar schemes not only produce positive temperature increments around the hurricane center but also systematically adjust the hurricane location with the hurricane-specific error covariance. On average, the analysis and forecast from the ETKF-EnVar have slightly smaller errors than that from the EDA-EnVar in terms of track, intensity, and precipitation forecast. Moreover, ETKF-EnVar yields better forecasts when verified against conventional observations.

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Acknowledgements

This research was primarily supported by the Natural Science Foundation of Jiangsu Province under Grant No. BK20160954, the Beijige Funding from Jiangsu Research Institute of Meteorological Science (Grant No. BJG201510, BJG201604), the National Natural Science Foundation of China (41505089, 41375025), the Startup Foundation for Introducing Talent of NUIST (Grants 2016r27 and 2016r043), the 973 Program (Grant No. 2013CB430102), Project for data application of Fengyun3 meteorological satellite (FY-3(02)-UDS-1.1.2) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We appreciate the comments and suggestions provided by LaDue, Daphne S. that significantly improved the content and clarity of this manuscript. Supercomputers at TACC, University of Texas, and SGI Altix3700 BX2, Nanjing University of Information Science & Technology were used.

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Authors and Affiliations

  1. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing, 210044, China

    Feifei Shen, Dongmei Xu, Ming Xue & Jinzhong Min

  2. Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK, 73072, USA

    Ming Xue

  3. School of Meteorology, University of Oklahoma, Norman, OK, 73072, USA

    Ming Xue

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  1. Feifei Shen
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  2. Dongmei Xu
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Correspondence to Feifei Shen.

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Shen, F., Xu, D., Xue, M. et al. A comparison between EDA-EnVar and ETKF-EnVar data assimilation techniques using radar observations at convective scales through a case study of Hurricane Ike (2008). Meteorol Atmos Phys 130, 649–666 (2018). https://doi.org/10.1007/s00703-017-0544-7

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