Ten ensembles with hourly high spatial resolution datasets for severe precipitation events in Egypt.

In this article, we created a new dataset comprising 10 ensembles for severe rainfall simulations at a high spatial resolution (10 km grid spacing, 120 × 120 grid points) in Egypt using the Weather Research and Forecast model Version 3.8 (WRF 3.8). The vertical grid had over 41 levels, extending from the surface to 10 hPa. The defined domain, a Lambert conformal conic projection, started from 24°E to 36°E. The ensembles were generated using 10 different microphysics schemes within the WRF 3.8. The severe rainfall event occurred between October 26 and 29, 2016. Final analysis data from National Center for Environmental Predictions were used for the initial and boundary conditions every 6 h at a spatial resolution of 1°â€¯× 1°. The geographical static input data, such as land use, albedo, and terrain height, were interpolated and prepared using a geogrid program in the WRF preprocessing system. This dataset is the first of its kind. It is addressing a need for this type of high resolution data over Egypt using physically- based numerical weather prediction models.

Spatiotemporal Clustering of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Incidence in Saudi Arabia, 2012-2019.

Middle East respiratory syndrome coronavirus (MERS-CoV) is a great public health concern globally. Although 83% of the globally confirmed cases have emerged in Saudi Arabia, the spatiotemporal clustering of MERS-CoV incidence has not been investigated. This study analysed the spatiotemporal patterns and clusters of laboratory-confirmed MERS-CoV cases reported in Saudi Arabia between June 2012 and March 2019. Temporal, seasonal, spatial and spatiotemporal cluster analyses were performed using Kulldorff's spatial scan statistics to determine the time period and geographical areas with the highest MERS-CoV infection risk. A strongly significant temporal cluster for MERS-CoV infection risk was identified between April 5 and May 24, 2014. Most MERS-CoV infections occurred during the spring season (41.88%), with April and May showing significant seasonal clusters. Wadi Addawasir showed a high-risk spatial cluster for MERS-CoV infection. The most likely high-risk MERS-CoV annual spatiotemporal clusters were identified for a group of cities (n = 10) in Riyadh province between 2014 and 2016. A monthly spatiotemporal cluster included Jeddah, Makkah and Taif cities, with the most likely high-risk MERS-CoV infection cluster occurring between April and May 2014. Significant spatiotemporal clusters of MERS-CoV incidence were identified in Saudi Arabia. The findings are relevant to control the spread of the disease. This study provides preliminary risk assessments for the further investigation of the environmental risk factors associated with MERS-CoV clusters.

Spatial variation in the association between NO2 concentrations and shipping emissions in the Red Sea.

Air pollution from shipping emissions poses significant health and environmental risks, particularly in the coastal regions. For the first time, this region as one of the busiest seas and most important international shipping lane in the world with significant nitrogen dioxide (NO2) emissions has been analyzed comprehensively. This paper aims to characterize and quantify the contribution of maritime transport sector emissions to NO2 concentrations in the Red Sea using local Geographically Weighted Regression (GWR) model in a geographic information system (GIS) environment. Maritime traffic volume was estimated using SaudiSat satellite-based Automatic Identification System (S-AIS) data, and the remotely measured tropospheric NO2 concentrations data was acquired from the ozone monitoring instrument (OMI) satellite. A significant spatial variation in the NO2 values was detected across the Red Sea, with values ranging from 4.03 × 1014 to 41.39 × 1014 molecules/cm2. Most notably, the NO2 concentrations in international waters were more than double those in the western coastal regions, whereas the concentrations close to seaports were 100% higher than those over international waters. The results indicated that the local GWR model performed significantly better than the global ordinary least squares (OLS) regression model. The GWR model had a strong and significant overall coefficient of determination with an r2 of 0.94 (p < 0.005) in comparison to the OLS model with an r2 of 0.45 (p < 0.005). Maritime traffic volume and proximity to seaports weighted by shipping activities explained about 94% of the variations of NO2 concentrations in the Red Sea. The results of this study suggest that the S-AIS data and environmental satellite measurements can be used to assess the impacts of NO2 concentrations from shipping emissions. These findings should stimulate further research into using additional covariates to explain the NO2 concentrations in areas near seaports where the standardized residuals are high.