A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 10¹² satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km² and 45 km². The mean density of good-quality observations is 13 km⁻² yr⁻¹. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr⁻¹ of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

We demonstrate improvements in CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations) dust extinction retrievals over northern Africa and Europe when corrections are applied regarding the Saharan dust lidar ratio assumption, the separation of the dust portion in detected dust mixtures, and the averaging scheme introduced in the Level 3 CALIPSO product. First, a universal, spatially constant lidar ratio of 58 sr instead of 40 sr is applied to individual Level 2 dust-related backscatter products. The resulting aerosol optical depths show an improvement compared with synchronous and collocated AERONET (Aerosol Robotic Network) measurements. An absolute bias of the order of −0.03 has been found, improving on the statistically significant biases of the order of −0.10 reported in the literature for the original CALIPSO product. When compared with the MODIS (Moderate-Resolution Imaging Spectroradiometer) collocated aerosol optical depth (AOD) product, the CALIPSO negative bias is even less for the lidar ratio of 58 sr. After introducing the new lidar ratio for the domain studied, we examine potential improvements to the climatological CALIPSO Level 3 extinction product: (1) by introducing a new methodology for the calculation of pure dust extinction from dust mixtures and (2) by applying an averaging scheme that includes zero extinction values for the nondust aerosol types detected. The scheme is applied at a horizontal spatial resolution of 1° × 1° for ease of comparison with the instantaneous and collocated dust extinction profiles simulated by the BSC-DREAM8b dust model. Comparisons show that the extinction profiles retrieved with the proposed methodology reproduce the well-known model biases per subregion examined. The very good agreement of the proposed CALIPSO extinction product with respect to AERONET, MODIS and the BSC-DREAM8b dust model makes this dataset an ideal candidate for the provision of an accurate and robust multiyear dust climatology over northern Africa and Europe.

This study assesses the impact of dust on surface solar radiation focussing on an extreme dust event. For this purpose, we exploited the synergy of AERONET measurements and passive and active satellite remote sensing (MODIS and CALIPSO) observations, in conjunction with radiative transfer model (RTM) and chemical transport model (CTM) simulations and the 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The area of interest is the eastern Mediterranean where anomalously high aerosol loads were recorded between 30 January and 3 February 2015. The intensity of the event was extremely high, with aerosol optical depth (AOD) reaching 3.5, and optical/microphysical properties suggesting aged dust. RTM and CTM simulations were able to quantify the extent of dust impact on surface irradiances and reveal substantial reduction in solar energy exploitation capacity of PV and CSP installations under this high aerosol load. We found that such an extreme dust event can result in Global Horizontal Irradiance (GHI) attenuation by as much as 40–50 % and a much stronger Direct Normal Irradiance (DNI) decrease (80–90 %), while spectrally this attenuation is distributed to 37 % in the UV region, 33 % in the visible and around 30 % in the infrared. CAMS forecasts provided a reliable available energy assessment (accuracy within 10 % of that obtained from MODIS). Spatially, the dust plume resulted in a zonally averaged reduction of GHI and DNI of the order of 150 W m⁻² in southern Greece, and a mean increase of 20 W m⁻² in the northern Greece as a result of lower AOD values combined with local atmospheric processes. This analysis of a real-world scenario contributes to the understanding and quantification of the impact range of high aerosol loads on solar energy and the potential for forecasting power generation failures at sunshine-privileged locations where solar power plants exist, are under construction or are being planned.

This study estimates the impact of dust aerosols on surface solar radiation and solar energy in Egypt based on Earth Observation (EO) related techniques. For this purpose, we exploited the synergy of monthly mean and daily post processed satellite remote sensing observations from the MODerate resolution Imaging Spectroradiometer (MODIS), radiative transfer model (RTM) simulations utilizing machine learning, in conjunction with 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). As cloudy conditions in this region are rare, aerosols in particular dust, are the most common sources of solar irradiance attenuation, causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The proposed EO-based methodology is based on the solar energy nowcasting system (SENSE) that quantifies the impact of aerosol and dust on solar energy potential by using the aerosol optical depth (AOD) in terms of climatological values and day-to-day monitoring and forecasting variability from MODIS and CAMS, respectively. The forecast accuracy was evaluated at various locations in Egypt with substantial PV and CSP capacity installed and found to be within 5–12% of that obtained from the satellite observations, highlighting the ability to use such modelling approaches for solar energy management and planning (M&P). Particulate matter resulted in attenuation by up to 64–107 kWh/m² for global horizontal irradiance (GHI) and 192–329 kWh/m² for direct normal irradiance (DNI) annually. This energy reduction is climatologically distributed between 0.7% and 12.9% in GHI and 2.9% to 41% in DNI with the maximum values observed in spring following the frequent dust activity of Khamaseen. Under extreme dust conditions the AOD is able to exceed 3.5 resulting in daily energy losses of more than 4 kWh/m² for a 10 MW system. Such reductions are able to cause financial losses that exceed the daily revenue values. This work aims to show EO capabilities and techniques to be incorporated and utilized in solar energy studies and applications in sun-privileged locations with permanent aerosol sources such as Egypt.

Methods for estimating the spatial distribution of PM_2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM_2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the S-BPNN for both yearly and seasonal mean PM_2.5 concentrations. In addition, principal components analysis was employed to reduce the dimensionality of the data and a grid of neural network models was run to optimize the model design. The S-BPNN was cross-validated against an analogous but SLV-free BPNN model using the coefficient of determination (R²) and root mean squared error (RMSE) as statistical measures of goodness of fit. The inclusion of the SLV led to demonstrably superior performance of the S-BPNN over the BPNN with R² values increasing from 0.80 to 0.89 and with the RMSE decreasing from 8.1 to 5.8 μg/m³. The yearly mean PM_2.5 concentration in China during the study period was found to be 41.8 μg/m³ and the model estimated spatial distribution was found to exceed Level 2 of the China Ambient Air Quality Standards (CAAQS) enacted in 2012 (>35 μg/m³) in more than 70% of the Chinese territory. The inclusion of spatial correlation upgrades the performance of conventional BPNN models and provides a more accurate estimation of PM_2.5 concentrations for air quality monitoring.