Assimilation of Radar Radial Velocity, Reflectivity, and Pseudo–Water Vapor for Convective-Scale NWP in a Variational Framework

Lai, Anwei; Gao, Jidong; Koch, Steven E.; Wang, Yunheng; Pan, Sijie; Fierro, Alexandre O.; Cui, Chunguang; Min, Jinzhong

doi:10.1175/MWR-D-18-0403.1

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Assimilation of Radar Radial Velocity, Reflectivity, and Pseudo–Water Vapor for Convective-Scale NWP in a Variational Framework

2019
By Lai, Anwei ; Gao, Jidong ; Koch, Steven E. ; ...
Source: Mon. Wea. Rev. (2019) 147 (8): 2877–2900.

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Journal Title:

Monthly Weather Review
Personal Author:

Lai, Anwei ; Gao, Jidong ; Koch, Steven E. ; Wang, Yunheng ; Pan, Sijie ; ... More +

Lai, Anwei ; Gao, Jidong ; Koch, Steven E. ; Wang, Yunheng ; Pan, Sijie ; Fierro, Alexandre O. ; Cui, Chunguang ; Min, Jinzhong Less -
NOAA Program & Office:

NOAA Cooperative Institutes ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory)

NOAA Cooperative Institutes ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) Less -
Description:

To improve severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales. The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water (VIL) derived from three-dimensional radar reflectivity fields. Once VIL is obtained, pseudo–water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection. Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast. The third step is to assimilate the derived pseudo–water vapor observations into a convection-resolving-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during 4-h data assimilation cycles. Finally, 3-h forecasts are launched every hour during that period. The performance of this method is examined for two selected high-impact severe thunderstorm events: namely, the 24 May 2011 Oklahoma and 16 May 2017 Texas and Oklahoma tornado outbreaks. Relative to a control simulation that only assimilated radar data, the analyses and forecasts of these supercells (reflectivity patterns, tracks, and updraft helicity tracks) are qualitatively and quantitatively improved in both cases when the water vapor information is added into the analysis.
Keywords:

[+]

Mathematical Models Numerical Weather Forecasting Thunderstorms
Source:

Mon. Wea. Rev. (2019) 147 (8): 2877–2900.
DOI:

https://doi.org/10.1175/MWR-D-18-0403.1
Document Type:

Journal Article
Funding:

Grant no. NA11OAR4320072 ; Grant no. NOAA-NWS-NWSPO-2018-2005317

Grant no. NA11OAR4320072 ; Grant no. NOAA-NWS-NWSPO-2018-2005317 Less -
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urn:sha256:6c5bbd0a4e5760be0d88987c0c939df2e736af31f95772262a10f529b4fe20c2
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https://repository.library.noaa.gov/view/noaa/26138/noaa_26138_DS1.pdf
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Assimilation of Radar Radial Velocity, Reflectivity, and Pseudo–Water Vapor for Convective-Scale NWP in a Variational Framework

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