This study investigates the atmospheric dynamics of the major dust storm that occurred in June 2020 over the Sahara and during which dust clouds associated with the highest‐on‐record aerosol optical depths were transported toward the America. An anomalous atmospheric circulation pattern in the mid‐latitudes, linked to a circumglobal wavetrain, resulted in the development of a subtropical high‐pressure system to the west of the Saharan heat low. This created a pressure dipole and generated anomalously strong northeasterlies over the Sahara, which caused continuous dust emissions over 4 days. Occurring along the northern fringes of the intertropical discontinuity, the dust was transported to higher altitudes (6 km) by the strong updraft in this region. This injected the dust at the African Easterly Jet (AEJ) altitudes and favored a rapid westward long‐range transport. The AEJ was also anomalously strong, being strengthened by the anticyclonic circulation associated with the anomalous high.

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.

The aerodynamic roughness length is a crucial parameter that controls surface variables including the horizontal wind, surface temperature, and heat fluxes. Despite its importance, in the Weather Research and Forecasting (WRF) model, this parameter is typically assigned a predefined value, mostly based on the dominant land‐use type. In this work, the roughness length is first estimated from eddy‐covariance measurements at Al Ain in the United Arab Emirates (UAE), a hyper‐arid region, and then ingested into WRF. The estimated roughness length is in the range 1.3–2.2 mm, one order smaller than the default value used in WRF. In line with previous studies, and from WRF model simulations during the warm and cold seasons, it is concluded that, when the roughness length is decreased by an order of magnitude, the horizontal wind speed increases by up to 1 m s⁻¹, the surface temperature rises by up to 2.5°C, and the sensible heat flux decreases by as much as 10 W m⁻². In comparison with in situ station and eddy covariance data, and when forced with the updated roughness length, WRF gives more accurate 2‐m air temperature and sensible heat flux predictions. For prevailing wind speeds >6 m s⁻¹, the model underestimates the strength of the near‐surface wind, a tendency that can be partially corrected, typically by 1–3 m s⁻¹, when the updated roughness length is considered. For low wind speeds (<4 m s⁻¹), however, WRF generally overestimates the strength of the wind.

This study provides insights on the composition and variability of atmospheric aerosols over the United Arab Emirates (UAE) by analyzing the atmospheric conditions together with 14 years (2006–2019) of aerosol optical depth (AOD) retrieved from CALIPSO (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite, and 7 years of AOD measured from the ground‐based Aerosol Robotic Network (AERONET).We found that mineral dust is the most prevailing aerosol subtype. In addition, polluted dust and polluted continental aerosols are observed mostly in the cold season. The AOD is higher in spring and summer, when the atmospheric conditions are more favorable to the occurrence of dust events. Moreover, there is another peak in winter associated with dust storms triggered by mid‐latitude baroclinic systems. In summer’s daytime, extinction coefficients in excess of 0.2 km⁻¹ are observed up to 3–4 km above the surface, as a result of the warmer and windier conditions. In the cold season and at night, the dust layers are confined to the lower atmosphere below 2 km. On a climatological time scale, we found that the AOD over the UAE has been decreasing since 2009, possibly due to the increasing trend in precipitation and changes in land use.This study highlights the large contribution of dust aerosols to the total aerosol load over the UAE and stresses on the need to account for mineral dust aerosols in climate‐air pollution related studies as well as weather and air quality forecasts.

A thorough evaluation of the Weather Research and Forecasting (WRF) Model is conducted over the United Arab Emirates, for the period September 2017–August 2018. Two simulations are performed: one with the default model settings (control run), and another one (experiment) with an improved representation of soil texture and land use land cover (LULC). The model predictions are evaluated against observations at 35 weather stations, radiosonde profiles at the coastal Abu Dhabi International Airport, and surface fluxes from eddy-covariance measurements at the inland city of Al Ain. It is found that WRF’s cold temperature bias, also present in the forcing data and seen almost exclusively at night, is reduced when the surface and soil properties are updated, by as much as 3.5 K. This arises from the expansion of the urban areas, and the replacement of loamy regions with sand, which has a higher thermal inertia. However, the model continues to overestimate the strength of the near-surface wind at all stations and seasons, typically by 0.5–1.5 m s⁻¹. It is concluded that the albedo of barren/sparsely vegetated regions in WRF (0.380) is higher than that inferred from eddy-covariance observations (0.340), which can also explain the referred cold bias. At the Abu Dhabi site, even though soil texture and LULC are not changed, there is a small but positive effect on the predicted vertical profiles of temperature, humidity, and horizontal wind speed, mostly between 950 and 750 hPa, possibly because of differences in vertical mixing.

Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates (UAE) where extreme events like heat waves, flash floods, and dust storms are severe. Hence, accurate forecasting of quantities like surface temperatures and humidity is very important. To date, there have been few seasonal-to-annual scale verification studies with WRF at high spatial and temporal resolution. This study employs a convection-permitting scale (2.7 km grid scale) simulation with WRF with Noah-MP, in daily forecast mode, from 1 January to 30 November 2015. WRF was verified using measurements of 2 m air temperature (T_2 m), 2 m dew point (TD_2 m), and 10 m wind speed (UV_10 m) from 48 UAE WMO-compliant surface weather stations. Analysis was made of seasonal and diurnal performance within the desert, marine, and mountain regions of the UAE. Results show that WRF represents temperature (T_2 m) quite adequately during the daytime with biases $\le +\mathrm{1}$ ^∘C. There is, however, a nocturnal cold bias (−1 to −4 ^∘C), which increases during hotter months in the desert and mountain regions. The marine region has the smallest T_2 m biases ($\le -\mathrm{0.75}$ ^∘C). WRF performs well regarding TD_2 m, with mean biases mostly ≤ 1 ^∘C. TD_2 m over the marine region is overestimated, though (0.75–1 ^∘C), and nocturnal mountain TD_2 m is underestimated ($\sim -\mathrm{2}$ ^∘C). UV_10 m performance on land still needs improvement, and biases can occasionally be large (1–2 m s⁻¹). This performance tends to worsen during the hot months, particularly inland with peak biases reaching ∼ 3 m s⁻¹. UV_10 m is better simulated in the marine region (bias ≤ 1 m s⁻¹). There is an apparent relationship between T_2 m bias and UV_10 m bias, which may indicate issues in simulation of the daytime sea breeze. TD_2 m biases tend to be more independent. Studies such as these are vital for accurate assessment of WRF nowcasting performance and to identify model deficiencies. By combining sensitivity tests, process, and observational studies with seasonal verification, we can further improve forecasting systems for the UAE.