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. 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 favourable 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.

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 to 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, the surface temperature rises by up to 2.5ºC, and the sensible heat flux decreases by as much as 10 W m. 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, the model underestimates the strength of the near-surface wind, a tendency that can be partially corrected, typically by 1-3 m s, when the updated roughness length is considered. For low wind speeds (< 4 m s), however, WRF generally overestimates the strength of the wind.

In this study, we discuss a new mountain peak observatory in the United Arab Emirates (UAE). Using coordinated scan patterns, a Doppler lidar and cloud radar were employed to study seedable convective clouds, and identify pre-convection initiation (CI) clear-air signatures. The instruments were employed for approximately two years in an extreme environment with a high vantage point for observing valley wind flows and convective cells. The instruments were configured to run synchronized polar (PPI) scans at 0°, 5°, and 45° elevation angles and vertical cross-section (RHI) scans at 0°, 30°, 60, 90°, 120°, and 150° azimuth angles. Using this output imagery, along with local C-band radar and satellite data, we were able to identify and analyze several convective cases. To illustrate our results, we selected two cases in unstable conditions - the 5 and 6 September 2018. In both cases, we observed areas of convergence/divergence, particularly associated with wind flow around a peak 2 km to the south-west. The extension of these deformations were visible in the atmosphere as high as 3 km above sea level. Subsequently, we observed convective cells developing in the same directions – apparently connected with these phenomena. The cloud radar images provided detailed observations of cloud structure, evolution, and precipitation. In both convective cases, pre-convective signatures were apparent before CI, in the form of convergence, wind shear structures, and updrafts. These results demonstrate the value of synergetic observations for understanding convection initiation, improvement of forecast models, and cloud seeding guidance.