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Title: Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection

Abstract

Stochastic parameterizations are increasingly becoming skillful in representing unresolved atmospheric processes for global climate models. The stochastic multicloud model, used to simulate the life cycle of the three most common cloud types (cumulus congestus, deep convective, and stratiform) in tropical convective systems, is one example. In this model, these clouds interact with each other and with their environment according to intuitive-probabilistic rules determined by a set of predictors, depending on the large-scale atmospheric state and a set of transition time scale parameters. Here we use a Bayesian statistical method to infer these parameters from radar data. The Bayesian approach is applied to precipitation data collected by the Shared Mobile Atmospheric Research and Teaching Radar truck-mounted C-band radar located in the Maldives archipelago, while the corresponding large-scale predictors were derived from meteorological soundings taken during the Dynamics of the Madden-Julian Oscillation field campaign. The transition time scales were inferred from three different phases of the Madden-Julian Oscillation (suppressed, initiation, and active) and compared with previous studies. The performance of the stochastic multicloud model is also assessed, in a stand-alone mode, where the cloud model is forced directly by the observed predictors without feedback into the environmental variables. The results showed amore » wide spread in the inferred parameter values due in part to the lack of the desired sensitivity of the model to the predictors and the shortness of the training periods that did not include both active and suppressed convection phases simultaneously. Nonetheless, the resemblance of the stand-alone simulated cloud fraction time series to the radar data is encouraging.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3];  [4]
  1. Department of Mathematics and StatisticsUniversity of Victoria Victoria British Columbia Canada, Now at Mathematics and StatisticsMemorial University of Newfoundland St. John's Newfoundland and Labrador Canada
  2. Department of Mathematics and StatisticsUniversity of Victoria Victoria British Columbia Canada
  3. Department of Atmospheric SciencesTexas A&,M University College Station TX USA
  4. Environment and Climate Change Canada Dorval Quebec Canada
Publication Date:
Research Org.:
Texas A & M Univ., College Station, TX (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1560156
Alternate Identifier(s):
OSTI ID: 1560161; OSTI ID: 1612445
Grant/Contract Number:  
SC0016245
Resource Type:
Published Article
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Name: Journal of Advances in Modeling Earth Systems Journal Volume: 11 Journal Issue: 6; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences; convective parameterization; clouds; Bayesian inference; radar data; stochastic multicloud model; tropical convection

Citation Formats

Cardoso‐Bihlo, E., Khouider, B., Schumacher, C., and De La Chevrotière, M. Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection. United States: N. p., 2019. Web. doi:10.1029/2018MS001537.
Cardoso‐Bihlo, E., Khouider, B., Schumacher, C., & De La Chevrotière, M. Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection. United States. https://doi.org/10.1029/2018MS001537
Cardoso‐Bihlo, E., Khouider, B., Schumacher, C., and De La Chevrotière, M. Wed . "Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection". United States. https://doi.org/10.1029/2018MS001537.
@article{osti_1560156,
title = {Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection},
author = {Cardoso‐Bihlo, E. and Khouider, B. and Schumacher, C. and De La Chevrotière, M.},
abstractNote = {Stochastic parameterizations are increasingly becoming skillful in representing unresolved atmospheric processes for global climate models. The stochastic multicloud model, used to simulate the life cycle of the three most common cloud types (cumulus congestus, deep convective, and stratiform) in tropical convective systems, is one example. In this model, these clouds interact with each other and with their environment according to intuitive-probabilistic rules determined by a set of predictors, depending on the large-scale atmospheric state and a set of transition time scale parameters. Here we use a Bayesian statistical method to infer these parameters from radar data. The Bayesian approach is applied to precipitation data collected by the Shared Mobile Atmospheric Research and Teaching Radar truck-mounted C-band radar located in the Maldives archipelago, while the corresponding large-scale predictors were derived from meteorological soundings taken during the Dynamics of the Madden-Julian Oscillation field campaign. The transition time scales were inferred from three different phases of the Madden-Julian Oscillation (suppressed, initiation, and active) and compared with previous studies. The performance of the stochastic multicloud model is also assessed, in a stand-alone mode, where the cloud model is forced directly by the observed predictors without feedback into the environmental variables. The results showed a wide spread in the inferred parameter values due in part to the lack of the desired sensitivity of the model to the predictors and the shortness of the training periods that did not include both active and suppressed convection phases simultaneously. Nonetheless, the resemblance of the stand-alone simulated cloud fraction time series to the radar data is encouraging.},
doi = {10.1029/2018MS001537},
journal = {Journal of Advances in Modeling Earth Systems},
number = 6,
volume = 11,
place = {United States},
year = {2019},
month = {6}
}

Journal Article:
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