Editorial Type:
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Article Type:
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Development of Multiscale EnKF within GSI and Its Applications to Multiple Convective Storm Cases with Radar Reflectivity Data Assimilation Using the FV3 Limited-Area Model

Chong-Chi Tong aCenter for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Ming Xue aCenter for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
bSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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https://orcid.org/0000-0003-1976-3238
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Chengsi Liu aCenter for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Jingyao Luo cShanghai Typhoon Institute, China Meteorological Administration, Shanghai, China
dKey Laboratory of Mesoscale Severe Weather, Ministry of Education and School of Atmospheric Sciences, Nanjing University, Nanjing, China
eKey Laboratory of Numerical Modeling for Tropical Cyclones, China Meteorological Administration, Shanghai, China

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Youngsun Jung aCenter for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Online Publication:
02 Aug 2024
Print Publication:
01 Aug 2024
DOI:
https://doi.org/10.1175/MWR-D-23-0235.1
Page(s):
1839–1857
Restricted access

Abstract

To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multiscale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the Gridpoint Statistical Interpolation analysis system (GSI) data assimilation framework coupled with the Finite-Volume Cubed-Sphere Dynamical Core (FV3) limited-area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner. 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together, while radar data are assimilated in the second step. The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 h. More accurate hourly precipitation forecasts with MEnKF can last up to 24 h for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events.

Jung’s current affiliation: NOAA/NWS/Office of Science and Technology Integration, Silver Spring, Maryland.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ming Xue, mxue@ou.edu

Abstract

To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multiscale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the Gridpoint Statistical Interpolation analysis system (GSI) data assimilation framework coupled with the Finite-Volume Cubed-Sphere Dynamical Core (FV3) limited-area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner. 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together, while radar data are assimilated in the second step. The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 h. More accurate hourly precipitation forecasts with MEnKF can last up to 24 h for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events.

Jung’s current affiliation: NOAA/NWS/Office of Science and Technology Integration, Silver Spring, Maryland.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ming Xue, mxue@ou.edu
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