Space-based data have provided important advances in understanding climate systems and processes in arid and semi-arid regions, which are hot-spot regions in terms of climate change and variability. This study assessed the performance of land surface temperatures (LSTs), retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua platform, over Egypt. Eight-day composites of daytime and nighttime LST data were aggregated and validated against near-surface seasonal and annual observational maximum and minimum air temperatures using data from 34 meteorological stations spanning the period from July 2002 to June 2015. A variety of accuracy metrics were employed to evaluate the performance of LST, including the bias, normalized root-mean-square error (nRMSE), Yule–Kendall (YK) skewness measure, and Spearman’s rho coefficient. The ability of LST to reproduce the seasonal cycle, anomalies, temporal variability, and the distribution of warm and cold tails of observational temperatures was also evaluated. Overall, the results indicate better performance of the nighttime LSTs compared to the daytime LSTs. Specifically, while nighttime LST tended to underestimate the minimum air temperature during winter, spring, and autumn on the order of −1.3, −1.2, and −1.4 °C, respectively, daytime LST markedly overestimated the maximum air temperature in all seasons, with values mostly above 5 °C. Importantly, the results indicate that the performance of LST over Egypt varies considerably as a function of season, lithology, and land use. LST performs better during transitional seasons (i.e., spring and autumn) compared to solstices (i.e., winter and summer). The varying interactions and feedbacks between the land surface and the atmosphere, especially the differences between sensible and latent heat fluxes, contribute largely to these seasonal variations. Spatially, LST performs better in areas with sandstone formations and quaternary sediments and, conversely, shows lower accuracy in regions with limestone, igneous, and metamorphic rocks. This behavior can be expected in hybrid arid and semi-arid regions like Egypt, where bare rocks contribute to the majority of the Egyptian territory, with a lack of vegetation cover. The low surface albedo of igneous and limestone rocks may explain the remarkable overestimation of daytime temperature in these regions, compared to the bright formations of higher surface albedo (i.e., sandy deserts and quaternary rocks). Overall, recalling the limited coverage of meteorological stations in Egypt, this study demonstrates that LST obtained from the MODIS product can be trustworthily employed as a surrogate for or a supplementary source to near-surface measurements, particularly for minimum air temperature. On the other hand, some bias correction techniques should be applied to daytime LSTs. In general, the fine space-based climatic information provided by MODIS LST can be used for a detailed spatial assessment of climate variability in Egypt, with important applications in several disciplines such as water resource management, hydrological modeling, agricultural management and planning, urban climate, biodiversity, and energy consumption, amongst others. Also, this study can contribute to a better understanding of the applications of remote sensing technology in assessing climatic feedbacks and interactions in arid and semi-arid regions, opening new avenues for developing innovative algorithms and applications specifically addressing issues related to these regions.

This study assesses the spatial and temporal characteristics of nighttime surface urban heat island (SUHI) effects over Greater Cairo: the largest metropolitan area in Africa. This study employed nighttime land surface temperature (LST) data at 1 km resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua sensor for the period 2003–2019. We presented a new spatial anomaly algorithm, which allowed to define SUHI using the most anomalous hotspot and cold spot of LST for each time step over Greater Cairo between 2003 and 2019. Results demonstrate that although there is a significant increase in the spatial extent of SUHI over the past two decades, a significant decrease in the mean and maximum intensities of SUHI was noted. Moreover, we examined the dependency between SUHI characteristics and related factors that influence energy and heat fluxes between atmosphere and land in urban environments (e.g., surface albedo, vegetation cover, climate variability, and land cover/use changes). Results demonstrate that the decrease in the intensity of SUHI was mainly guided by a stronger warming in daytime and nighttime LST in the neighborhood of urban localities. This warming was accompanied by a decrease in surface albedo and diurnal temperature range (DTR) over these areas. Results of this study can provide guidance to local urban planners and decision-makers to adopt more effective mitigation strategies to diminish the negative impacts of urban warming on natural and human environments.

This study presents the first comprehensive evaluation of the performance of three globally high‐resolution remotely sensed products in replicating the main characteristics of rainfall over the Middle East, with special emphasis on extreme wet events. Specifically, we employed daily observational data from a network of rain gauges (N = 217), spanning the retrospective period 1998–2013 and covering six countries in the Middle East (i.e., Egypt, Iraq, Jordan, Libya, Saudi Arabia, and Syria), against data derived from three global satellite‐based precipitation products: the Version 7 TRMM (Tropical Rainfall Measuring Mission) Multi‐satellite Precipitation Analysis 3B42 product (TRMM‐3B42), the Climate Prediction Center MORPHing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Alongside a range of conventional statistical error measures (e.g., bias, normalized root‐mean‐square error [nRMSE] and Spearman's rho correlation coefficient), this study also gives priority to evaluate the skill of these products in reproducing characteristics of extreme wet events (e.g., frequency, intensity, duration, onset, anomaly). Results demonstrate that TRMM‐3B42 generally performs well in estimating rainfall totals during the rainy season (ONDJFMA), with a mean bias of 0.05 mm, nRMSE of 0.15 mm, and Spearman's rho of 0.74 for the whole Middle East. In contrast, PERSIANN‐CDR and CMORPH‐BLD underestimate the observed rainfall. Importantly, TRMM‐3B42 outperforms other products in reproducing the frequency and intensity of the most extreme wet events, while PERSIANN‐CDR and CMORPH‐BLD fail to reproduce these key characteristics. Notably, all products perform poorly in reproducing the climatology of the anomalous wet events in the region, indicating that careful scrutiny must be warranted before using these products, particularly for hydrological modelling. Considering the daily resolution of these remotely sensed precipitation products and their reasonable spatial resolution (0.25 × 0.25°) in comparison to available in situ data over the Middle East, results of this work provide a solid scientific reference for national stakeholders and policy makers to decide on the most useful product for their specific applications (e.g., hydrological modelling, streamflow forecasts, water resources management, and hydrometeorological hazard assessment and mitigation).

In this paper, we will investigate the influence of the microphysics schemes on the rainfall pattern of the extreme storm that impacted Egypt on 12 March 2020. The aim is to improve rainfall forecasting using the numerical Weather Research and Forecasting (WRF) model for an effective Early Warning System (EWS). The performance of six microphysics schemes were evaluated using the Model Object-based Evaluation analysis tool (MODE) forced by three selected satellite-based datasets (CMORPH, PERSIANN, PERSIANN-CCS, etc.) and one reanalysis dataset (ERA5). Six numerical simulations were performed using the WRF model, considering the following microphysics schemes: Lin, WSM6, Goddard, Thompson, Morrison, and NSSL2C. The models were evaluated using both conventional statistical indices and MODE, which is much more suitable in such studies. The results showed that the Lin scheme outperformed the other schemes such as WSM6, Goddard, Thompson, Morrison, and NSSL2C, in rainfall forecasting. The Thompson scheme was found to be the least reliable scheme. An extension for this study is recommended in other regions where the observational rain gauges data are available.

Several heatwaves (HWs) have been recorded in Egypt in recent years. Some of these HWs were mild, while others were severe and resulted in mortalities and morbidities. On the other hand, air pollution is considered a health issue in Egypt’s megacities, especially the capital city, Cairo, and its surroundings, the Greater Cairo (GC) region. In this study, we examine a number of HWs that have hit Egypt in recent years, along with the state of air quality, in terms of PM10, NO₂, and O₃, during the period of HW incidence, with a focus on the GC region. During the period of study, the frequency, intensity, and duration of HWs have been observed to increase. The total number of recorded HW events was 190, encompassing 376 HW days. The time series of daily mean NO₂ showed no correlation with temperature during the months that experienced HWs cases. Conversely, PM10 and O₃ concentrations exhibited a similar pattern as that of the daily maximum temperature. This increase in the two pollutant concentrations led to a degradation of the air quality, as demonstrated by the fact that the Air Quality Health Index went from “moderate risk”, on normal days, to “high risk” during the HWs.