Optimization of Land Surface Parameters for Weather Simulations over Arid and Semi-Arid Regions

Abstract

The Cloud Resolving Storm Simulator (CReSS) model with default parameter settings largely underestimates the diurnal variation of land surface temperature (LST) and hence the formation of summertime diurnal convective clouds over the United Arab Emirates (UAE), which are the primary target of cloud seeding in the UAE. Based on sensitivity experiment results, we optimized a set of land surface parameters to minimize the underestimation of LST and improve the performance of weather simulations over deserts and mountains in the UAE. In the optimized experiment, the difference in LST between Aqua/MODIS observations and CReSS simulation results decreased from 13.0 to 2.3 K under daytime conditions, mainly due to decreased evapotranspiration efficiency and soil thermal diffusivity. Moreover, the difference decreased from 3.8 to 2.0 K under nighttime conditions, mainly due to decreased evapotranspiration efficiency and increased soil temperature at the deepest layer. A decrease in roughness length suppressed the increase in surface air temperature (SAT), contributing to the reproduction of a realistically large difference between LST and SAT during the daytime. The improvement in temperature matching demonstrates that the CReSS model, when used at a 1-km horizontal resolution, accurately simulates the formation of diurnal convective clouds and development of precipitation over deserts in the UAE and low mountains in northeastern UAE.