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This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system's capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement. [ABSTRACT FROM AUTHOR]

This study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and employs the U-Net deep learning algorithm for the semantic segmentation of images under varying cloud and land surface conditions. The system autonomously generates detailed maps delineating classes such as water, land, vegetation, snow, river ice, cloud, and cloud shadow. The verification of system outputs was performed quantitatively by comparing with existing ice extent maps in the northeastern US and New Brunswick, Canada, yielding a Probability of Detection of 0.77 and a False Alarm rate of 0.12, suggesting commendable accuracy. Qualitative assessments were also conducted, corroborating the reliability of the system and underscoring its utility in monitoring hydraulic and hydrological processes across northern watersheds. The system's proficiency in accurately capturing the phenology of river ice, particularly during onset and breakup times, testifies to its potential as a valuable tool in the realm of river ice monitoring. [ABSTRACT FROM AUTHOR]

Fog augments the wet deposition of airborne particles entrained in its hydrometeors. This article aims to characterize fog deposition processes around the Barakah nuclear power plant (BNPP), in the United Arab Emirates (UAE), and assess the potential impact of fog on the deposition rate of radionuclides in case of an accidental release. To this end, the microphysics of twelve radiation fog events, typical in such arid climate, were measured during the winter seasons of 2021 and 2022 using a fog monitor that was deployed at the BNPP. The impact of fog deposition on the settling of radionuclides is investigated based on model simulations using the Weather Research Forecasting (WRF) model with the MYJ PBL scheme and FLEXPART. All fog events are found to share a common feature of a bimodal distribution in droplet number concentration (Nc), with modes at 4.5 μ m and 23.16 μ m. It was pointed out that despite the high proportion of smaller droplets in the fog associated with the fine mode, the greatest contribution to the liquid water content (LWC) comes essentially from medium to large droplets between 10 and 35 μ m. The deposition flux of fog water at the site and the fog droplet deposition velocity were estimated using an Eddy Covariance (EC) onsite. Typical mean values for fog droplet deposition velocity are found to range between 2.11 and 7.87 cm s-1. The modeling results show that fog deposition contributed by 30–40 % to the total ground deposition of 137Cs, highlighting the importance of incorporating fog deposition as an additional scavenging mechanism in dispersion modeling under foggy conditions. [ABSTRACT FROM AUTHOR]

The goal of this study is to evaluate the performance of the National Water Model (NWM) in time and space across the contiguous United States. Retrospective streamflow simulations were compared to records from 3260 USGS gauging stations, considering both regulated and natural flow conditions. Statistical metrics, including Kling–Gupta efficiency, Percent Bias, Pearson Correlation Coefficient, Root Mean Squared Error, and Normalized Root Mean Squared Error, were employed to assess the agreement between observed and simulated streamflow. A comparison of historical trends in daily flow data between the model and observed streamflow provided additional insight into the utility of retrospective NWM datasets. Our findings demonstrate a superior agreement between the simulated and observed streamflow for natural flow in comparison to regulated flow. The most favorable agreement between the NWM estimates and observed data was achieved in humid regions during the winter season, whereas a reduced degree of agreement was observed in the Great Plains region. Enhancements to model performance for regulated flow are necessary, and bias correction is crucial for utilizing the NWM retrospective streamflow dataset. The study concludes that the model-agnostic NextGen NWM framework, which accounts for regional performance of the utilized model, could be more suitable for continental-scale hydrologic prediction. [ABSTRACT FROM AUTHOR]

The goal of this study is to assess the temporal variability of the performance of the Soil Moisture Active Passive, SMAP, soil moisture retrievals throughout the seasons as surface conditions change. In-situ soil moisture observations from a network deployed in Millbrook, New York, between 2019 and 2021 are used. The network comprises 25 stations distributed across a 33-km SMAP pixel with a predominantly forest land cover. The in-situ soil moisture observations were collected between 6 and 7 a.m., local time. This article covers the assessment of the temporal accuracy of SMAP soil moisture by incorporating various upscaling methods. Four upscaling methods are used in this study: arithmetic average, Voronoi diagram, topographic wetness index, and land cover weighted average. The agreement between SMAP soil moisture and the upscaled in-situ measurements was gauged using the root-mean-squared difference, the mean difference, and the unbiased root-mean-squared difference. The consistency of the temporal variability of SMAP soil moisture data resulting from the four upscaling methods was analyzed. The results revealed that SMAP retrievals (soil moisture data) are systematically higher than in situ observations during the different seasons. The results indicate that the highest performance of SMAP soil moisture retrievals is in September with an ubRMSD value of 0.03 m3.m−3 for the morning and evening overpasses, which can be attributed to a lower vegetation density during the seasonal transition. The agreement with in-situ observations degrades during March–April with ubRMSD values above 0.04 m3.m−3, reaching ~0.06 m3.m−3 in April, which can be attributed to the non-reliability of in-situ measurements due to freeze\thaw transition and the challenging determination of the soil effective temperature. The ubRMSD is also higher than 0.04 m3.m−3 in the months of May–June, which could be due to the introduced vegetation effect during the growth season. These findings are consistent across all the upscaling methods. The average ubRMSD over the study period is 0.055 m3.m−3, which falls short of meeting the mission's performance target. This study proves the need to enhance SMAP retrieval over forest sites. [ABSTRACT FROM AUTHOR]

The goal of this study is to validate and analyze NASA's Soil Moisture Active Passive (SMAP) products over the desert of Kuwait. The study period was between April 2015 and April 2020. The study domain includes a mission candidate calibration/validation (Cal/Val) site that comprises six permanent soil moisture stations used to verify SMAP estimates. In addition, intensive field campaigns were conducted within and around the candidate Cal/Val site during the study period to collect additional thermogravimetric samples. The mean difference (MD), root mean squared difference (RMSD), unbiased root mean square difference (ubRMSD), and correlation coefficient (R) were computed to assess the agreement between SMAP SM products and in situ observations. The comparison of the six ground station sensors' observations with the thermogravimetric samples led to an absolute mean bias (AMB) of 0.034 m3 m−3, which was then used to calibrate the sensors and bias-correct their measurements. The temporal consistency of the readings from the test site and calibrated sensors was assessed using the mean relative difference (MRD) and its standard deviation of relative difference (SDRD). Using a sampling density analysis, it was determined that a minimum of four ground stations would be required to validate the test site. Furthermore, the consistency between SMAP satellite soil moisture data and those derived from the Soil Moisture and Ocean Salinity (SMOS) satellite operated by the European Space Agency, and their agreement with in situ samples, was analyzed. The comparison of SMAP and SMOS soil moisture data with in situ observations showed that both satellites successfully captured the spatial and temporal distribution of soil moisture. For SMAP and SMOS, the lowest ubRMSE statistics were 0.043 m3 m−3 and 0.045 m3 m−3, respectively, which are slightly higher than the mission target of 0.04 m3 m−3. [ABSTRACT FROM AUTHOR]

Accurate simulation of boundary layer surface meteorological parameters is essential to achieve good forecasting of weather and atmospheric dispersion. This paper is devoted to a model sensitivity study over a coastal hyper-arid region in the western desert of the United Arab Emirates. This region hosts the Barakah Nuclear Power Plant (BNPP), making it vital to correctly simulate local weather conditions for emergency response in case of an accidental release. We conducted a series of high-resolution WRF model simulations using different combinations of physical schemes for the months January 2019 and June 2019. The simulated results were verified against in-situ meteorological surface measurements available offshore, nearshore, and inland at 12 stations. Several statistical metrics were calculated to rank the performance of the different simulations and a near-to-optimal set of physics options that enhance the performance of a WRF model over different locations in this region has been selected. Additionally, we found that the WRF model performed better in inland locations compared to offshore or nearshore locations, suggesting the important role of dynamical SSTs in mesoscale models. Moreover, morning periods were better simulated than evening ones. The impact of nudging towards station observations resulted in an overall reduction in model errors by 5–15%, which was more marked at offshore and nearshore locations. The sensitivity to grid cell resolution indicated that a spatial resolution of 1 km led to better performance compared to coarser spatial resolutions, highlighting the advantage of high-resolution simulations in which the mesoscale coastal circulation is better resolved. [ABSTRACT FROM AUTHOR]

Fog microphysics characteristics play an important role in fog life cycle, radiation and visibility. Measurements of fog microphysics are important in understanding these processes and improving existing model parameterization schemes. From 28 January to 17 February 2021, we carried out the first measurements of fog microphysics in the arid region of the United Arab Emirates and present initial results in this paper. Five events occurred, the longest lasting about 4 hr 28 min and the remaining lasting 2 hr or less. In this study, the meteorological conditions at synoptic scale associated with the formation of fog, are presented as well as the microphysics characteristics of fog. Maximum cloud droplet count ranged from 80 to 700 cc, Liquid Water Content (LWC) from 0.06 to 0.58 g m−3 and the Median Volume Diameter reached 25 μm in all the cases. The estimated Liquid Water Path reached a maximum of 100 g m−2. The size distribution was bimodal for all visibility classes of mist, fog and thick fog with modes at around 4.5 μm and between 22 and 25 μm. The droplet size distribution broadened as the mature phase progressed, demonstrating a decrease in droplets in the 6–16 μm range and an increase in droplets above 19 μm. During the mature stage, droplet growth was observed with no associated decrease in LWC, suggesting growth by collision and coalescence was occurring. However, LWC did increase concurrently suggesting droplet growth by condensation was also taking place. Plain Language Summary: During the cold season of each year, desert regions experience the formation of radiation fog due to the rapid cooling of the desert surface at night. Cities built on the fringes of major desert regions, such as the United Arab Emirates (UAE) cities, witness very poor visibility due to seasonal fog formation. The fog impacts air and road traffic and disrupts the daily life. Fog microphysics characteristics play a key role in fog life cycle, radiation and visibility. Measurements of fog microphysics are important to understand and improve existing model parameterization schemes for a more accurate forecast of fog occurrence and life cycle. We carried out the first fog microphysics measurements over the UAE, an arid region on the Arabian Peninsula, where fog is a frequent occurrence in winter months. Results from this study are the first of their kind in the region and are expected to advance the current knowledge on fog dynamics and characteristics. Key Points: First microphysical measurements of radiation fog in the United Arab Emirates, a warm, arid region in the subtropicsThe cloud droplet size distribution was bimodal for all visibility classes, with modes at around 4.5 μm and between 22 and 25 µmDroplet growth was observed to be due to collision, coalescence and condensation [ABSTRACT FROM AUTHOR]

Aerosol and cloud microphysical measurements were collected by a research aircraft during August 2019 over the United Arab Emirates (UAE). The majority of scientific flights targeted summertime convection along the eastern Al Hajar Mountains bordering Oman, while one flight sampled non-orographic clouds over the western UAE near the Saudi Arabian border. In this work, we study the evolution of growing cloud turrets from cloud base (9 ∘ C) up to the capping inversion level (-12 ∘ C) using coincident cloud particle imagery and particle size distributions from cloud cores under different forcing. Results demonstrate the active role of background dust and pollution as cloud condensation nuclei (CCN) with the onset of their deliquescence in the subcloud region. Subcloud aerosol sizes are shown to extend from submicron to 100 µ m sizes, with higher concentrations of ultra-giant CCN (d>10 µ m) from local sources closer to the Saudi border, compared with the eastern orographic region where smaller CCN are observed. Despite the presence of ultra-giant CCN from dust and pollution in both regions, an active collision–coalescence (C–C) process is not observed within the limited depths of warm cloud (<1000 m). The state-of-the-art observations presented in this paper can be used to initialize modeling case studies to examine the influence of aerosols on cloud and precipitation processes in the region and to better understand the impacts of hygroscopic cloud seeding on these clouds. [ABSTRACT FROM AUTHOR]

The impact of 26 December 2019 annular solar eclipse (ASE) on meteorological conditions over the southeastern Arabian Peninsula is investigated. Observations sourced from the spinning enhanced visible and infrared imager (SEVIRI) and vertical temperature profiles measured by a microwave radiometer were used. The ASE, which began at 03:36:37.9 Universal Time Coordinated (UTC), that is, 31 m 29.9 s after sunrise, left a significant imprint on the land surface temperature (LST). In particular, in some regions, the LST dropped by more than 4 °C, in comparison to the previous day. In situ soil properties, in particular soil texture, were also found to have modulated the effects of the ASE, with loamy soils experiencing higher heating/cooling rates than sandy soils. Finally, the analysis of atmospheric profiles indicated that the eclipse influenced the flow throughout the atmospheric boundary layer, with a stable layer that was 45-min longer and 90-m deeper compared with the preceding day. [ABSTRACT FROM AUTHOR]

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. Key Points: Over the UAE, dust is the prevailing aerosol subtype, polluted dust and polluted continental aerosols are also observed mostly in winterIn summer, aerosols are observed up to 3–4 km above the surface, due to a deeper boundary layer. In winter they are confined below 2 kmAOD is higher in spring and summer and it has been decreasing since 2009 in correlation with positive precipitation trends [ABSTRACT FROM AUTHOR]

Ice shelf instability is one of the main sources of uncertainty in Antarctica's contribution to future sea level rise. Calving events play a crucial role in ice shelf weakening but remain unpredictable, and their governing processes are still poorly understood. In this study, we analyze the unexpected September 2019 calving event from the Amery Ice Shelf, the largest since 1963 and which occurred almost a decade earlier than expected, to better understand the role of the atmosphere in calving. We find that atmospheric extremes provided a deterministic role in this event. A series of anomalously deep and stationary explosive twin polar cyclones over the Cooperation and Davis seas generated tides and wind-driven ocean slope, leading to fracture amplification along the pre-existing rift and ultimately calving of the massive iceberg. The calving was triggered by high oceanward sea surface slopes produced by the storms. The observed record-anomalous atmospheric conditions were promoted by blocking ridges and Antarctic-wide anomalous poleward transport of heat and moisture. Blocking highs helped in (i) directing moist and warm air masses towards the ice shelf and (ii) maintaining the observed extreme cyclones stationary at the front of the ice shelf for several days. Accumulation of cold air over the ice sheet, due to the blocking highs, led to the formation of an intense cold high pressure over the ice sheet, which helped fuel sustained anomalously deep cyclones via increased baroclinicity. Our results stress the importance of atmospheric extremes in ice shelf dynamics via tides and sea surface slope and its need to be accounted for when considering Antarctic ice shelf variability and contribution to sea level, especially given that more of these extremes are predicted under a warmer climate. [ABSTRACT FROM AUTHOR]

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 (T2 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 (T2 m) quite adequately during the daytime with biases ≤+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 T2 m biases (≤-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 (∼-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 T2 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. [ABSTRACT FROM AUTHOR]

A summertime convective event that developed on 5 September 2017 over the United Arab Emirates (UAE) is investigated in this study. Atmospheric profiles from a ground‐based microwave radiometer along with satellite observations and in situ data from three weather stations in the UAE were used. The event was simulated using the Weather Research and Forecasting (WRF) model, forced with four input datasets: Global Forecast System (GFS), Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium‐Range Weather Forecasts ERA‐5 and ERA‐Interim reanalyses. The afternoon and evening convection was triggered by the intrusion of a mid‐level trough from midlatitudes and was favoured by the convergence over land of thermally forced maritime air masses. Near‐surface observations at a weather station revealed a 7 °C drop in air temperature, a doubling of the wind speed to 9 m·s−1 in 30 min, and a shift in wind direction from easterly, to southerly and then westerly in about 45 min, associated with the passage of the cold pool emanating from a Mesoscale Convective System (MCS). At the location where the microwave radiometer was deployed, the pre‐squall low and wake low signatures are also captured, with a 5 m·s−1 increase in the wind speed in just 5 min. The observed features of the studied MCS were found to compare with those reported for MCSs in the Tropics. The four experiments gave a similar performance, although the GFS simulation generally generated higher skill scores. The investigated MCS event was not captured by WRF, which was attributed to a misrepresentation of soil moisture in the model. This study highlights the difficulties regional models like WRF may have in reproducing MCSs over arid/hyperarid regions, which may result in a misrepresentation of their impacts in climate projection studies. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Dust is an important constituent of the Earth's atmosphere, with a wide range of impacts ranging from human health to effects on climate. In June 2020, massive amounts of dust were lifted from the Sahara, the major dust source region in the world, and transported all the way into the Americas across the tropical Atlantic Ocean. This event was caused by the development of a subtropical high‐pressure system over northwest Africa which resulted in sustained strong northeasterlies over the Sahara generating continuous dust emissions for 4 days. Due to the strong low‐level convergence along the intertropical discontinuity region, the dust was lifted to roughly 5–6 km above the surface, and then transported westward by the stronger mid‐atmospheric winds (>20 m s−1). At Cape Verde and over large swaths of the Atlantic Ocean, the amount of dust suspended in the atmosphere was associated with the largest aerosol optical depths on record. Key Points: The dust storm was triggered by the development of a subtropical high off the coast of West Africa embedded in a circumglobal wavetrainThe stationary subtropical high increased the north‐south pressure gradient over the Sahara and generated sustained strong northeasterliesThe anticyclonic circulation associated with the high intensified the African Easterly Jet favoring a rapid westward transport of the dust plumes [ABSTRACT FROM AUTHOR]

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-NOAHMP, in daily forecast mode, from January 01 to November 30 2015. WRF was verified using measurements of 2 m air temperature (T-2m), dew point (TD-2m), and 10 m windspeed (UV-10m) from 48 UAE surface 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-2m) quite adequately during the daytime with biases ≤ +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 lowest T-2m biases (≤−0.75 ˚C). WRF performs well regarding TD-2m, with mean biases mostly ≤ 1 ˚C. TD-2m over the marine region is overestimated though (0.75–1 ˚C), and nocturnal mountain TD-2m is underestimated (~ −2 ˚C). UV-10m 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-10m are better simulated in the marine region (bias ≤ 1 m s−1). There is an apparent relationship between T-2m bias and UV-10m bias, which may indicate issues in simulation of the daytime sea breeze. TD-2m 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. [ABSTRACT FROM AUTHOR]

The central aim of this work is to investigate the characteristics of fog events over the United Arab Emirates (UAE) and identify the underlying physical processes responsible for fog initiation and dissipation. To achieve this, hourly meteorological measurements at eight airport stations, along with ERA5 reanalysis data (1995–2018), are utilized. The analysis indicates the dominance of radiation fog (RAD) as, on average, 70% of the observed events fall under this category. Fog in the UAE typically forms between 2000 and 0200 local time (LT) and dissipates between 0600 and 0900 LT. During a typical dense fog event recorded during 22–23 December 2017, cooling and moistening tendencies of up to 1.2 K h−1 and 0.7 g kg−1 h−1 are observed ~5–6 h before fog onset. In the vertical, a dry and warm layer above 750 hPa gradually descends from above 500 hPa to promote the development of fog. Similar conclusions are reached when analyzing composites of fog events. Further, the variability of fog occurrence associated with El Niño–Southern Oscillation (ENSO) patterns is explored. It is concluded that the El Niño (warm) and La Niña (cold) phases exhibit very different spatial characteristics with respect to surface meteorological variables. In particular, during El Niño events, the near-surface atmosphere is cooler and moister compared to La Niña events, favoring RAD fog formation over the UAE. Besides, fog events during El Niño years tend to last longer compared to La Niña years due to an earlier onset. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are forced with the Global Forecast System (GFS) data and run over the United Arab Emirates (UAE) for two 4-day periods: one in the cold season (16–18 December 2017) and another in the warm season (13–15 April 2018). The models' performance is evaluated against four observational datasets: weather station observations, eddy-covariance flux measurements at Al Ain, microwave radiometer–derived temperature profile, and twice-daily radiosonde measurements at Abu Dhabi. An overestimation of the daily mean air temperature by 1°–3°C is noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux. A comparison with the eddy-covariance data suggested that both models also underestimate the observed albedo. However, when the models predict heavier amounts of precipitation, they tend to be colder than observations, typically by 2°–3°C. NICAM and WRF overpredict the strength of the near-surface wind speed at all weather stations by roughly 1–3 m s−1, which has been attributed to a poor representation of its subgrid-scale fluctuations and surface drag parameterization. WRF tends to be wetter and NICAM drier than the station observations, possibly because of differences in the cloud microphysics schemes. While the performance of both models for the near-surface fields is comparable, NICAM outperforms WRF in the simulation of vertical profiles of temperature, relative humidity, and wind speed, being able to partially correct some of the biases in the GFS data. [ABSTRACT FROM AUTHOR]