{"title":"Improving simulations of warm rain in a bulk microphysics scheme","authors":"Robert Conrick, Clifford F. Mass, Lynn McMurdie","doi":"10.1175/mwr-d-23-0035.1","DOIUrl":null,"url":null,"abstract":"Abstract Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson-Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are (1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson-Eidhammer parameterization, and (2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015-16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson-Eidhammer configuration. For the multi-month OLYMPEX period, we show that rain rates, rain water mixing ratios, and rain drop number concentrations were increased relative to the Thompson-Eidhammer microphysical parameterization, while concurrently decreasing rain drop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"73 1","pages":"0"},"PeriodicalIF":2.8000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Monthly Weather Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1175/mwr-d-23-0035.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson-Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are (1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson-Eidhammer parameterization, and (2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015-16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson-Eidhammer configuration. For the multi-month OLYMPEX period, we show that rain rates, rain water mixing ratios, and rain drop number concentrations were increased relative to the Thompson-Eidhammer microphysical parameterization, while concurrently decreasing rain drop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest.
期刊介绍:
Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.