The Impact of Constrained Data Assimilation on the Forecasts of Three Convection Systems During the ARM MC3E Field Campaign

Wang, Jia; Zhang, Minghua

doi:10.1175/mwr-d-22-0144.1

Title: The Impact of Constrained Data Assimilation on the Forecasts of Three Convection Systems During the ARM MC3E Field Campaign

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Abstract

A constrained data assimilation (CDA) system based on the ensemble variational (EnVar) method and physical constraints of mass and water conservations is evaluated through three convective cases during the Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program. Compared to the original data assimilation (ODA), the CDA is shown to perform better in the forecasted state variables and simulated precipitation. The CDA is also shown to greatly mitigate the loss of forecast skills in observation denial experiments when radar radial winds are withheld in the assimilation. Modifications to the algorithm and sensitivities of the CDA to the calculation of the time tendencies in the constraints are described.

Authors:

Wang, Jia ^[1]; Zhang, Minghua ^[1]

Stony Brook Univ., NY (United States)

Publication Date:: Wed Oct 19 00:00:00 EDT 2022

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Contributing Org.:: Pacific Northwest National Laboratory (PNNL); Brookhaven National Laboratory (BNL); Argonne National Laboratory (ANL); Oak Ridge National Laboratory (ORNL)

OSTI Identifier:: 1894283

Grant/Contract Number:: SC0016336

Resource Type:: Accepted Manuscript

Journal Name:: Monthly Weather Review

Additional Journal Information:: Journal Volume: 151; Journal Issue: 2; Journal ID: ISSN 0027-0644

Publisher:: American Meteorological Society

Country of Publication:: United States

Language:: English

Subject:: 54 ENVIRONMENTAL SCIENCES

Citation Formats


                    Wang, Jia, and Zhang, Minghua. The Impact of Constrained Data Assimilation on the Forecasts of Three Convection Systems During the ARM MC3E Field Campaign.  United States: N. p., 2022. 
Web.  doi:10.1175/mwr-d-22-0144.1.

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                    Wang, Jia, & Zhang, Minghua. The Impact of Constrained Data Assimilation on the Forecasts of Three Convection Systems During the ARM MC3E Field Campaign.  United States.  https://doi.org/10.1175/mwr-d-22-0144.1

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                    Wang, Jia, and Zhang, Minghua. Wed .  
"The Impact of Constrained Data Assimilation on the Forecasts of Three Convection Systems During the ARM MC3E Field Campaign".  United States.  https://doi.org/10.1175/mwr-d-22-0144.1.  https://www.osti.gov/servlets/purl/1894283.

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@article{osti_1894283,

  title        = {The Impact of Constrained Data Assimilation on the Forecasts of Three Convection Systems During the ARM MC3E Field Campaign},

  author       = {Wang, Jia and Zhang, Minghua},

  abstractNote = {A constrained data assimilation (CDA) system based on the ensemble variational (EnVar) method and physical constraints of mass and water conservations is evaluated through three convective cases during the Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program. Compared to the original data assimilation (ODA), the CDA is shown to perform better in the forecasted state variables and simulated precipitation. The CDA is also shown to greatly mitigate the loss of forecast skills in observation denial experiments when radar radial winds are withheld in the assimilation. Modifications to the algorithm and sensitivities of the CDA to the calculation of the time tendencies in the constraints are described.},

  doi          = {10.1175/mwr-d-22-0144.1},

  journal      = {Monthly Weather Review},

  number       = 2,

  volume       = 151,

  place        = {United States},

  year         = {Wed Oct 19 00:00:00 EDT 2022},

  month        = {Wed Oct 19 00:00:00 EDT 2022}

}

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