Assessment of the WRF Model as a Guidance Tool Into Cloud Seeding Operations in the United Arab Emirates

With the projected expansion of arid/semi-arid regions in a warming world, precipitation enhancement activities such as cloud seeding will become increasingly popular and relied upon. Due to the inherent costs, a successful planning is crucial, which involves accurate model predictions. In this study, the usefulness of the Weather Research and Forecasting (WRF) model forecasts for guidance into seeding operations in the United Arab Emirates, where seeding activities have been conducted for more than two decades, is assessed. The WRF predictions are compared with ground-based, satellite-derived and radar reflectivity data, and in-situ observations onboard the airplanes used to perform the seeding operations. WRF is found to have higher skill in simulating the observed cloud top pressure/temperature than the cloud fraction, with the model vertical velocity predictions also more skillful than those of the radar reflectivity. A stronger Arabian Heat Low (AHL) in the model leads to drier conditions which, together with a surface cold bias, limits the spatial extent and vertical depth of the simulated convective clouds. Development of convective rolls in the boundary layer is reported in both observations and simulations and their interaction with cold pools from convective clouds promote the development of secondary convection. Sensitivity to the choice of the Planetary Boundary Layer (PBL) scheme is also noticed, with the Yonsei University PBL scheme giving the best performance. When considering the two factors needed for a successful seeding operation that is, the presence of an updraft and clouds, the model-predicted seeding regions largely match the areas where precipitation was observed. As the proposed WRF set up can be used operationally, the model forecasts will bring added value to the seeding activities in the country.