Impact of physical parameterizations on simulation of the Caspian Sea lake-effect snow |
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| Affiliation: | 1. St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg 199034, Russia;2. NIERSC, Nansen International Environmental and Remote Sensing Centre, 14th line, 7, St. Petersburg 199034, Russia;3. Arctic and Antarctic Research Institute, Bering str., 38, St. Petersburg 199397, Russia;4. Pacific Oceanological Institute of the Russian Academy of Sciences, 43 Baltiiskaya St., 690041 Vladivostok, Russia;1. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environmental Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China;2. Joint Innovation Center for Modern Forestry Studies, College of Biology and Environment, Nanjing Forestry University, Nanjing, 210037, China;3. Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY, 12222, USA |
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| Abstract: | The southwestern coast of the Caspian Sea often experiences heavy snowfall during winter season due to the lake effect. The accurate estimation of snowfall in this region is still a challenge for weather forecasters. This study attempts to investigate the simulation of lake-effect snow (LES) event occurring along the southwest coastline of the Caspian Sea from 31 January to 4 February 2014 using Weather Research and Forecasting (WRF) model. The study evaluates the sensitivity of four microphysics (WSM6, Goddard, Morrison, and Thompson) schemes and two planetary boundary layer (PBL) schemes (the Yonsei University (YSU) and the Mellor-Yamada-Janjic (MYJ)), yielding eight distinct combinations. The results indicated that all the simulations overestimated the precipitation. However, the best configurations for estimation of precipitation and snow in terms of their spatiotemporal variation were the Morrison-MYJ and the Goddard-MYJ, respectively. Analyses of the vertical profiles of hydrometeor species showed that the combination of Goddard and MYJ schemes created more snow and graupel than the other configurations. Although the combination of WSM-MYJ schemes revealed the least bias, it was not appropriate for the prediction of snow. A comparison of the two boundary layer schemes showed that the MYJ scheme simulated better intensity and distribution of precipitation than the YSU scheme compared to observations. Also, the maximum radar reflectivity of the model output was useful for identifying the location of maximum precipitation. |
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| Keywords: | Lake-effect snow Caspian Sea WRF model Parameterization Hydrometeors Radar |
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