Direct Assimilation of Radar Data With Ensemble Kalman Filter and Hybrid Ensemble-Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand-Alone Regional FV3 Model at a Convection-Allowing Resolution

Tong, Chong‐Chi; Jung, Youngsun; Xue, Ming; Liu, Chengsi

doi:10.1029/2020GL090179

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Direct Assimilation of Radar Data With Ensemble Kalman Filter and Hybrid Ensemble-Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand-Alone Regional FV3 Model at a Convection-Allowing Resolution

2020
By Tong, Chong‐Chi ; Jung, Youngsun ; Xue, Ming ; ...
Source: Geophysical Research Letters, 47, e2020GL090179

[PDF-4.20 MB]

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Journal Title:

Geophysical Research Letters
Personal Author:

Tong, Chong‐Chi ; Jung, Youngsun ; Xue, Ming ; Liu, Chengsi

Tong, Chong‐Chi ; Jung, Youngsun ; Xue, Ming ; Liu, Chengsi Less -
NOAA Program & Office:

CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; NWS (National Weather Service) ; OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office)

CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; NWS (National Weather Service) ; OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office) Less -
Description:

Capabilities to directly assimilate radar radial velocity (Vr) and reflectivity (Z) data are implemented within the operational GSI data assimilation (DA) framework and coupled with the new stand-alone regional (SAR) FV3 model. The effectiveness and performance of 3DVar, EnKF, and hybrid En3DVar methods are evaluated with a storm cluster over the U.S. Central Plains at 3-km grid spacing. During the DA cycles, 3DVar analyses show better fit to Z observations but fastest error growth, while EnKF and pure En3DVar lead to smaller forecast errors. For Vr, EnKF outperforms other methods in both analysis and forecast. Good correspondence with tornado reports is obtained by most experiments for probabilistic forecast of updraft helicity (UH), except for 3DVar which shows insufficient confidence in certain regions. Overall, EnKF and hybrid En3DVar show best forecast skills in terms of composite reflectivity and UH. Tests with more cases are needed to draw more general conclusions, however.
Keywords:

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Radar DA SAR FV3 Storm-scale Forecast
Source:

Geophysical Research Letters, 47, e2020GL090179
DOI:

https://doi.org/10.1029/2020GL090179
Document Type:

Journal Article
Funding:

Grant no. NA16OAR4320115 ; Grant no. NA18OAR4590385

Grant no. NA16OAR4320115 ; Grant no. NA18OAR4590385 Less -
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urn:sha256:0df11a6c129e35e6a31eda7028192487e0536fc73e5a9b9669acfaf661a40677
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https://repository.library.noaa.gov/view/noaa/31655/noaa_31655_DS1.pdf
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Direct Assimilation of Radar Data With Ensemble Kalman Filter and Hybrid Ensemble-Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand-Alone Regional FV3 Model at a Convection-Allowing Resolution

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