Impact of Land State conditions on location-specific Heavy Rainfall Events

Abstract:

In a climate-changing scenario, hyper-local forecasting is a huge challenge for the forecasting community and model development. Recent studies have shown the substantial impact of the land state on extreme weather events over the region. The objective of this study to assess the performance of land parameterization and the land state impacting the intense rainfall over the city of Bhubaneswar (85.7˚E, 20.17˚N) and its neighbourhood regions using weather research and forecasting (WRF) model. From the past findings over the region, the best combination of cloud microphysics, planetary boundary layer, and cumulus scheme is configured in the WRF in a nested manner with a horizontal resolution of 9 and 3 kms respectively. The simulations will be carried out up to day-4 (96hrs lead-time) for 20 cases of heavy rainfall events for pre-monsoon and monsoon seasons that have contributed to rainfall over the region. The five land surface parameterization schemes, namely Noah, Noah-MP, Thermal diffusion, Rapid Update Cycle, and CLM4, will be used for carrying out these experiments. The model results will be validated against the available surface observations, i.e., Automatic Weather Station, Micro rain radar, Ceilometer, and Disdrometer deployed over the study location. Besides, satellite observations and India Meteorological Organization (IMD) surface and upper-air data sets will be used to validate the results. The aim is to find out the best forecast skills of the model with special reference to rainfall. The results will be presented in the context of land-atmosphere coupling strength and their role in modulating the nature of the forecast, particularly at the hyper local scale. Key near-surface parameters such as temperature, wind, and stability structure will be examined in the perspective of evolution, development, and sustainability of intense convection over the location.