气候变化领域集成服务门户(CCPortal): Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection

CCPortal

DOI	10.1029/2018MS001537
	Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection
	Cardoso-Bihlo E.; Khouider B.; Schumacher C.; De La Chevrotière M.
发表日期	2019
ISSN	19422466
起始页码	1655
结束页码	1684
卷号	11 期号:6
英文摘要	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. ©2019. The Authors.
英文关键词	Bayesian inference; clouds; convective parameterization; radar data; stochastic multicloud model; tropical convection
语种	英语
scopus关键词	Bayesian networks; Climate models; Climatology; Clouds; Inference engines; Meteorological radar; Parameterization; Stochastic systems; Bayesian inference; Bayesian statistical method; Convective parameterization; Madden-Julian oscillation; Radar data; Shared mobile atmospheric research and teachings; Stochastic parametrization; Tropical convection; Stochastic models; atmospheric convection; Bayesian analysis; calibration; climate modeling; cloud; convective system; global climate; Madden-Julian oscillation; parameterization; performance assessment; radar; stochasticity; tropical environment; Maldives
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156899
作者单位	Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada; Now at Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL, Canada; Department of Atmospheric Sciences, Texas A&M University, College Station, TX, United States; Environment and Climate Change Canada, Dorval, QC, Canada
推荐引用方式 GB/T 7714	Cardoso-Bihlo E.,Khouider B.,Schumacher C.,et al. Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection[J],2019,11(6).
APA	Cardoso-Bihlo E.,Khouider B.,Schumacher C.,&De La Chevrotière M..(2019).Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection.Journal of Advances in Modeling Earth Systems,11(6).
MLA	Cardoso-Bihlo E.,et al."Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection".Journal of Advances in Modeling Earth Systems 11.6(2019).