Study region: United Arab Emirates. Study focus: Numerous global precipitation products have been developed and calibrated. However, their performance over arid regions such as the United Arab Emirates (UAE) has been revealing notable differences triggered by a combination of climatic and terrestrial attributes. The objective of this study is to cross-validate and analyze the consistency of four global precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the UAE using a network of 53 rain gauges from 2000 to 2010. The spatial analysis of their consistency versus topography and land cover is expected to reveal the factors affecting the country’s rainfall regime. The study also identifies and calibrates the best statistically performing precipitation product as an essential climatic input for monitoring, forecasting, and modeling hydrologic applications over the UAE. New hydrological insights for the region: The UAE, similar to other dryland environments lacking adequate hydrologic monitoring networks, presents a unique area to evaluate satellite remote sensing products and the erratic spatiotemporal nature of precipitation in diverse environments. Statistical analyses indicate that the TMPA V7 precipitation products record the highest overall agreement with the observational network. Within the UAE, areas that receive high rainfall and fall within the vegetated highlands (e.g., >250 m), provide the most promise for incorporating satellite precipitation into hydrologic monitoring, modeling, or water resource management

Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z^0.12), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z^−0.18). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash–Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring.

Operational cloud seeding programs have been increasingly deployed in several countries to augment natural rainfall amounts, particularly over water-scarce and arid regions. However, evaluating operational programs by quantifying seeding impacts remains a challenging task subject to complex uncertainties. In this study, we investigate seeding impacts using both long-term rain gauge records and event-based weather radar retrievals within the framework of the United Arab Emirates (UAE) National Center of Meteorology’s operational cloud seeding program. First, seasonal rain gauge records are inter-compared between unseeded (1981–2002) and seeded (2003–2019) periods, after which a posteriori target/control regression is developed to decouple natural and seeded rainfall time series. Next, trend analyses and change point detection are carried out over the July-October seeding periods using the modified Mann-Kendall (mMK) test and the Cumulative Sum (CUSUM) method, respectively. Results indicate an average increase of 23% in annual surface rainfall over the seeded target area, along with statistically significant change points detected during 2011 with decreasing/increasing rainfall trends for pre-/post-change point periods, respectively. Alternatively, rain gauge records over the control (non-seeded) area show non-significant change points. In line with the gauge-based statistical findings, a physical analysis using an archive of seeded (65) and unseeded (87) storms shows enhancements in radar-based storm properties within 15–25 min of seeding. The largest increases are recorded in storm volume (159%), area cover (72%), and lifetime (65%). The work provides new insights for assessing long-term seeding impacts and has significant implications for policy- and decision-making related to cloud seeding research and operational programs in arid regions.

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.

The regional-scale consistency between four precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the Arabian Peninsula was assessed. Their macroscale relationships were inter-compared with soil moisture and total water storage (TWS) estimates from AMSR-E and GRACE. The consistency analysis was studied with multivariate statistical hypothesis testing and Pearson correlation metrics for the period from January 2000 to December 2010. The products and GRACE estimates were assessed over a representative sub-domain (United Arab Emirates) with available in situ well observations. Next, geographically temporally weighted regression (GTWR) was employed to examine the interdependencies among the peninsula’s hydrological components. The results showed GPCC-TRMM recording the highest correlation (0.85) with insignificant mean differences over more than 90% of the peninsula. The highest GTWR predictive performance of TWS (R² = 0.84) was achieved with TRMM forcing, which indicates its potential to monitor changes in TWS over the arid peninsular region.