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

Journal Article · Wed Oct 19 00:00:00 EDT 2022 · Monthly Weather Review

DOI:https://doi.org/10.1175/mwr-d-22-0144.1· OSTI ID:1894283

Wang, Jia ^[1]; Zhang, Minghua ^[1]

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1. Stony Brook Univ., NY (United States)

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Contributing Organization: Argonne National Laboratory (ANL); Brookhaven National Laboratory (BNL); Oak Ridge National Laboratory (ORNL); Pacific Northwest National Laboratory (PNNL)
• Grant/Contract Number: SC0016336
• OSTI ID: 1894283
• Journal Information: Monthly Weather Review, Journal Name: Monthly Weather Review Journal Issue: 2 Vol. 151; ISSN 0027-0644
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES