Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data

Abstract

Abstract. This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016, using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (WRF-Hydro). Six-hourly forecasted forcing records at 0.5∘ spatial resolution, obtained from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF–WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. The model performance was assessed using precipitation from the Global Precipitation Measurement (GPM) mission (30 min, 0.1∘ product), soil moisture from the Advanced Microwave Scanning Radiometer 2 (AMSR2; daily, 0.1∘ product) and the NOAA Soil Moisture Operational Products System (SMOPS; 6-hourly, 0.25∘ product), and cloud fraction retrievals from the Moderate Resolution Imaging Spectroradiometer Atmosphere product (MODATM; daily, 5 km product). The Pearson correlation coefficient (PCC), relative bias (rBIAS), and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF–WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2-derived soil moisture estimates, despite a noticeable dry and wet bias in areas where soil moisture is high and low. Temporal and spatial variabilities of simulated soil moisture compare well to estimates from the NOAA SMOPS product, which indicates the model's capability to simulate surface drainage. Finally, the coupled model showed a shallower planetary boundary layer (PBL) compared to the standalone WRF simulation, which is attributed to the effect of soil moisture feedback. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrological and meteorological forecasts in hyper-arid environments.