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.

On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

“…As was the case for the winter period, and in line with other studies such as Gunwani and Mohan (2017), both WRF and NICAM generally underestimate the observed cloud cover, as concluded by visually comparing the two (not shown) and in line with other studies such as Schwitalla et al (2019) and Wehbe et al (2019), leading to enhanced downward shortwave radiation fluxes at the surface and warmer daytime air temperatures. The exception is when heavier than observed precipitation amounts are predicted, as seen by comparing Figs.…”

“…This is confirmed when the predicted cloud amounts are visually compared with those given by the Spinning Enhanced Visible and Infrared Imager (SEVIRI; Schmetz et al 2002) instrument (not shown). A similar result was obtained by Gunwani and Mohan (2017) (2019) noted a tendency of WRF to generate less clouds than those observed over the UAE for a summertime convective event, and Wehbe et al (2019) for an extreme event in the cold season. NICAM has also been found to underpredict the observed cloud amounts, in particular, midtropospheric clouds (e.g., Kodama et al 2012).…”

“…This contrasting behavior between single-moment and double-moment schemes has been found to be the case for the cloud microphysics schemes used in the WRF Model and considered here [e.g., for a summertime convective event in the Nepalese Himalayas (Orr et al 2017), for an atmospheric river event in California (Jankov et al 2011), and for summertime convective events in the United States (Cintineo et al 2014;Putnam et al 2017)]. Since one of the events targeted in this study is convective in nature, this suggests that WRF may overestimate the observed rainfall amounts, as reported by Wehbe et al (2019) over the UAE with the Morrison double-moment scheme (Morrison et al 2009). The LSMs employed in WRF and NICAM also share many similarities (e.g., they are one-dimensional, employ the bulk aerodynamic formula in the estimation of the surface heat fluxes, and the roughness lengths for momentum and heat are differently set up.…”

“…The main goal of the work is to better understand the limitations of the two models in a hyperarid environment, where numerical models are known to underperform and satellite-derived meteorological variables, such as surface temperature, are not very reliable (e.g., Gunwani and Mohan 2017;Ozturk et al 2012;Wehbe et al 2017Wehbe et al , 2018Fonseca et al 2019). This study is particularly relevant as desert regions are predicted to expand in a hypothetical warmer world and are known to be very sensitive to climate change (e.g., Feng et al 2014;Huang et al 2017;Kumar et al 2017;Aldababseh et al 2018).…”

“…The model underestimates the observed precipitation, with the 10-m wind speed generally higher than that observed except in the evening hours. Wehbe et al (2019) used both the standalone WRF model and WRF coupled with its hydrological modeling extension package (WRF-Hydro), to investigate the added value of coupled land surface-atmosphere modeling in the simulation of an extreme event in the UAE, which took place in March 2016. This is particularly relevant even during dry periods due to the rapidly changing land surface conditions in the UAE with the expanding green areas and urban development (Aldababseh et al 2018).…”