Through an analysis of land use and land cover (LULC) data for the years 2005 and 2017 from the Advanced Wide Field Sensor onboard the Indian Remote Sensing satellite, we find considerable changes in the LULC in three major states of south India, namely, Tamil Nadu, Telangana and Kerala. This change is mainly due to increasing urbanization, in addition to the change of prevalent mixed forest into deciduous needle/leaf forest in Kerala. Motivated by this finding, we study the impact of these LULC changes over a decade on the extremes of twelve heavy rainfall events in these states through several sensitivity experiments with a convection‐permitting Weather Research and Forecasting model, by changing the LULC boundary conditions. We particularly focus on three representative heavy rainfall events, specifically, over (a) Chennai (1 December 2015), (b) Telangana (24 September 2016), and (c) Kerala (15 August 2018). The simulated rainfall patterns of the three heavy rainfall events are found to be relatively better with the use of the 2017 LULC boundary conditions. The improvement is statistically significant in the case of the Chennai and Kerala events. On analysis of these simulations, and output from additional simulations we have conducted for nine other heavy rainfall events, we suggest that the recent LULC changes result in higher surface temperatures and sensible heat fluxes, and a deeper and moist boundary layer. This causes a relatively higher convective available potential energy and, consequently, heavier rainfall. We find the LULC changes in the three states, mainly dominated by the increasing urbanization in Telangana and Tamil Nadu, enhance the rainfall during the heavy rainfall events by 20–25%. This is the first extensive investigation of multiple and multi‐regional cases over the Indian region.
Here, we investigated the impact of assimilating the satellite-based product of Global Positioning System (GPS) radio occultation (RO) refractivity profiles data on the simulation of selected extreme rainfall events in three states of southern India: Tamil Nadu, Telangana, and Kerala. We assimilated the GPS RO data into the weather research and forecasting model using a 3DVar assimilation technique and evaluated the results against unassimilated (control) simulations. Various observations (e.g., rainfall measurements from AWS/ rain-gauge) and observation-based gridded rainfall were used. The assimilation of the data yielded improved prediction of the spatial distributions of extreme rainfall regions and the amounts of rainfall. The analysis of the simulated dynamical and thermodynamic processes indicated that the assimilation of the data enabled the model to simulate significantly deep convection, high instability, and strong vertical motions. A vorticity budget analysis confirmed the marginally strengthened low-level convergence. The vertical motions because of assimilation facilitated an increased vertical advection of vorticity, which enhanced the extreme conditions in storms. Moreover, the assimilation of the data resulted in enhanced water vapor condensation and high levels of ice, cloud, and rain water in clouds, all of which contributed to extreme rainfall.
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