Investigating the performance of PBL parametrizations in WRF model for front enhanced SES simulation in istanbul megacity

Abstract

Sea-effect snow (SES) is a meteorological phenomenon resulting from cold air moving over warmer waters. Accurate prediction of SES is vital for emergency management, transportation, and water resource planning. A thundersnow event in Istanbul from 17–19 February 2015 caused significant disruptions, with traffic and flights affected, highways temporarily closed, and trees falling due to heavy snowfall. This study investigates the influence of different parameterization schemes in the Weather Research and Forecasting (WRF) model on SES simulations. Six distinct PBL parameterization schemes were used in a series of WRF simulations. In addition, the following factors pivotal to SES event have also been investigated: 1000–500 hPa thickness, total and latent heat fluxes, radar and satellite analyses, temperature gradients, wind shear, inversion levels, and atmospheric stability indices. Additionally, the formation of SES during the cold front transition further contributed to these elements in the Black Sea region. The simulations displayed notably high total heat flux and latent heat flux values, particularly following the passage of the cold front. Furthermore, the northeast-southwest oriented SES cloud, distinguished by its banded structure, was successfully validated using radar and satellite imagery. However, it's worth noting that the model positioned it farther west than its actual location. This study highlights the challenges in precise prediction and analysis of such convective activities. In this thundersnow event, the local closure schemes, particularly MYNN in first place and second MYJ, demonstrated superior performance compared to non-local schemes within the parameterization options.