Abstract/Summary

In this study, the impact of different initial land conditions on the simulation of thunderstorms and monsoon depressions is investigated using the Weather Research and Forecasting (WRF) model. A control run (CNTL) and a simulation with an improved land state (soil moisture and temperature) using the High Resolution Land Data Assimilation System (HRLDAS) are compared for three different rainfall cases in order to examine the robustness of the assimilation system. The study comprises two thunderstorm cases (one in the pre-monsoon and one during the monsoon) and one monsoon depression case that occurred during the Interaction of Convective Organisation, Atmosphere, Surface and Sea (INCOMPASS) field campaign of the 2016 Indian monsoon. HRLDAS is shown to yield improvements in the representation of location-specific rainfall, particularly over land. Further, it is found that the surface fluxes as well as the convective indices are better captured for the pre-monsoon thunderstorm case in HRLDAS. By analysing components of the vorticity tendency equation, it is found that the vertical advection term is the major contributor towards the positive vorticity tendency in HRLDAS compared to CNTL, hence improving localised convection and consequently facilitating rainfall. Significant improvements in the simulation of the pre-monsoon thunderstorm are noted, as seen using Automatic Weather Station (AWS) validation, whereas improvements in the monsoon depression is minimal. Further, it is found that vertical advection (moisture flux convergence) is the major driver modulating the convective circulation in localised thunderstorm (monsoon depression) cases and these dynamics are better represented by HRLDAS compared to CNTL. These findings underline the importance of accurate and high resolution land-state conditions in model initial conditions for forecasting severe weather systems, particularly the simulation of localised thunderstorms over India.