Surface solar radiation forecasts by advecting cloud physical properties derived from Meteosat Second Generation observations

The NASA CERES SYN1deg(Ed4.1) satellite-based products include hourly global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct horizontal irradiance (DirHI) as well as hourly solar zenith angle (SZA), and the direct normal irradiance (DNI) can thus be calculated by dividing the DirHI by cos(SZA). The spatial resolution of the dataset is 1° latitude by 1° longitude, and the time span is from March 2000 to near present (October 2023 as of this writing). While the GHI of the dataset agrees well with the ground-based Baseline Surface Radiation Network (BSRN) data, the DHI and DirHI, and thus DNI, show appreciable biases against the BSRN data. Nevertheless, the uncertainty of the DHI and DNI, as exhibited in the standard deviations of their differences from the BSRN data, are smaller than results from the DirIndex model, a global-to-beam model, applied to the CERES data, albeit the model is one of the two best out of 140 models according to a study. This means that the original CERES hourly DNI, though biased, renders the spatiotemporal variability better than does the DirIndex model. For this reason, we decided to perform a bias correction on the hourly DHI and DNI. With the corrected hourly DHI and DNI, we are able to use the isotropic model to calculate the global tilted irradiance (GTI) and the global tracker irradiance (GTrI) on an hourly basis. The BSRN DHI and DNI, on the other hand, are available on near-instantaneous time scales (1-, 2-, 3, or 5-minute intervals) and thus are more conducive to the isotropic model, and for the first time, we use the BSRN data to validate the GTI and GTrI, as opposed to GHI and DHI that have previously been used for validation purpose. For comparison and validation, we also use the monthly-mean-based LJCR method, an independent method as used by RETScreen®, which requires monthly mean GHI and DHI, to directly calculate the monthly mean DNI, GTI and, to extend the model, GTrI, and the results agree well with the BSRN data, which proves the LJCR method itself and corroborates the corrected DHI and DNI as well as the derived GTI and GTrI. The monthly mean DHI and DNI from the Whitlock Method, also monthly-mean-based, are evaluated as well, and we find that minor modification can dramatically improve the method.

The accuracy and precision are determined of cloud liquid water path (LWP) retrievals from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat-8 using 1 yr of LWP retrievals from microwave radiometer (MWR) measurements of two CloudNET stations in northern Europe. The MWR retrievals of LWP have a precision that is superior to current satellite remote sensing techniques, which justifies their use as validation data. The Cloud Physical Properties (CPP) algorithm of the Satellite Application Facility on Climate Monitoring (CM-SAF) is used to retrieve LWP from SEVIRI reflectances at 0.6 and 1.6 μm. The results show large differences in the accuracy and precision of LWP retrievals from SEVIRI between summer and winter. During summer, the instantaneous LWP retrievals from SEVIRI agree well with those from the MWRs. The accuracy is better than 5 g m−2 and the precision is better than 30 g m−2, which is similar to the precision of LWP retrievals from MWR. The added value of the 15-min sampling frequency of Meteosat-8 becomes evident in the validation of the daily median and diurnal variations in LWP retrievals from SEVIRI. The daily median LWP values from SEVIRI and MWR are highly correlated (correlation > 0.95) and have a precision better than 15 g m−2. In addition, SEVIRI and MWR reveal similar diurnal variations in retrieved LWP values. The peak LWP values occur around noon. During winter, SEVIRI generally overestimates the instantaneous LWP values from MWR, the accuracy drops to about 10 g m2, and the precision to about 30 g m−2. The most likely reason for these lower accuracies is the shortcoming of CPP, and similar one-dimensional retrieval algorithms, to model inhomogeneous clouds. It is suggested that neglecting cloud inhomogeneities leads to a significant overestimation of LWP retrievals from SEVIRI over northern Europe during winter.

An improved atmospheric profile retrieval system for the current Geostationary Operational Environmental Satellite (GOES) Sounder data process has been developed. This algorithm can also be applied to process Advanced Baseline Imager (ABI) on the next generation GOES-R to continue the current GOES class Sounder legacy products. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) data from the Meteosat Second Generation (MSG) satellite is employed as proxy to test and evaluate the algorithm for ABI legacy product. Since there is only a few sounding spectral bands in SEVIRI, a first guess from forecast is needed in legacy profile retrieval. The results show that if a set of temperature/humidity profiles from weather forecast is applied as first guess, the accuracy of temperature/humidity profiles can be achieved with that from the current GOES Sounder. Considering that there is only one temperaturesensitive spectral band in SEVIRI, temperature information is limited; however, the improvement on humidity profile retrieval over forecast is noticeable because there are two water vapor absorption spectral bands in SEVIRI. The results of total precitable water (TPW) and lift index (LI) from combined SEVIRI and forecast are presented as well.

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.

In the framework of the Satellite Application Facility on Climate Monitoring (CM‐SAF) an algorithm was developed to retrieve Cloud Physical Properties (CPP) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (METEOSAT−8) and the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) satellites. This paper presents the CPP algorithm and determines if SEVIRI can be used together with AVHRR to build a consistent and accurate data set of cloud optical thickness (COT) and cloud liquid water path (CLWP) over Europe for climate research purposes. After quantifying the differences in 0.6 and 1.6 μm operational calibrated reflectances of SEVIRI and AVHRR, a recalibration procedure is proposed to normalize and absolutely calibrate these reflectances. The effects of recalibration, spatial resolution, and viewing geometry differences on the SEVIRI and AVHRR cloud property retrievals are evaluated. The intercomparison of 0.6 and 1.6 μm operationally calibrated reflectances indicates ∼6 and ∼26% higher reflectances for SEVIRI than for AVHRR. These discrepancies result in retrieval differences between AVHRR and SEVIRI of ∼8% for COT and ∼60% for CLWP. Owing to recalibration these differences reduce to ∼5%, while the magnitude of the median COT and CLWP values of AVHRR decrease ∼2 and ∼60% and the SEVIRI values increase ∼10 and ∼55%, respectively. The differences in spatial resolution and viewing geometry slightly influence the retrieval precision. Thus the CPP algorithm can be used to build a consistent and high‐quality data set of SEVIRI and AVHRR retrieved cloud properties for climate research purposes, provided the instrument reflectances are recalibrated, preferably guided by the satellite operators.

Abstract. Solar radiation plays a key role in the Earth's energy balance and is used as an essential input data in radiation-based evapotranspiration (ET) models. Accurate gridded solar radiation data at high spatial and temporal resolution are needed to retrieve ET over large domains. In this work we present an evaluation at hourly, daily and monthly time steps and regional scale (Catalonia, NE Iberian Peninsula) of a satellite-based solar radiation product developed by the Land Surface Analysis Satellite Application Facility (LSA SAF) using data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Product performance and accuracy were evaluated for datasets segmented into two terrain classes (flat and hilly areas) and two atmospheric conditions (clear and cloudy sky), as well as for the full dataset as a whole. Evaluation against measurements made with ground-based pyranometers yielded good results in flat areas with an averaged model RMSE of 65 W m−2 (19%), 34 W m−2 (9.7%) and 21 W m−2 (5.6%), for hourly, daily and monthly-averaged solar radiation and including clear and cloudy sky conditions and snow or ice cover. Hilly areas yielded intermediate results with an averaged model RMSE (root mean square error) of 89 W m−2 (27%), 48 W m−2 (14.5%) and 32 W m−2 (9.3%), for hourly, daily and monthly time steps, suggesting the need of further improvements (e.g., terrain corrections) required for retrieving localized variability in solar radiation in these areas. According to the literature, the LSA SAF solar radiation product appears to have sufficient accuracy to serve as a useful and operative input to evaporative flux retrieval models.

Near real-time estimation of Sea and Land surface temperature for MSG SEVIRI sensors

Abstract. Satellite instruments provide high-temporal-resolution data on a global scale, but extracting 3D information from current instruments remains a challenge. Most observational data are two-dimensional (2D), offering either cloud top information or vertical profiles. We trained a neural network (Res-UNet) to merge high-resolution satellite images from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) with 2D CloudSat radar reflectivities to generate 3D cloud structures. The Res-UNet extrapolates the 2D reflectivities across the full disk of MSG SEVIRI, enabling a reconstruction of the cloud intensity, height, and shape in three dimensions. The imbalance between cloudy and clear-sky CloudSat profiles results in an overestimation of cloud-free pixels. Our root mean square error (RMSE) accounts for 2.99 dBZ. This corresponds to 6.6 % error on a reflectivity scale between −25 and 20 dBZ. While the model aligns well with CloudSat data, it simplifies multi-level and mesoscale clouds in particular. Despite these limitations, the results can bridge data gaps and support research in climate science such as the analysis of deep convection over time and space.

In this study, the remote sensing of aerosol optical depth (τa) from the geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) is demonstrated. The proposed method is based on the analysis of a time series of SEVIRI's 0.6 μm channel images. Top‐of‐atmosphere reflectance is precorrected for the effect of atmospheric gases and a background aerosol amount. Subsequently, surface reflectance for each pixel is estimated by determining its lowest precorrected reflectance within the observed time period for each satellite observation time of the day. The resulting diurnal surface reflectance curve in combination with the radiative transfer code SMAC are finally used to derive τa. This approach is applied to SEVIRI subscenes of central Europe (40.8–51.3°N, 0.3°W–19.9°E) from August 2004, daily acquired between 0612 and 1712 UTC in intervals of 15 min. SEVIRI τa are related to Aerosol Robotic Network (AERONET) Sun photometer measurements from nine sites. About 3200 instantaneous SEVIRI and Sun photometer τa are compared. An overall correlation of 0.9 and a root mean square error of 0.08 are obtained. Further, the spatial distribution of SEVIRI τa maps for August 2004 represent expectable features like higher concentrations in industrialized regions or lower loading in higher altitudes. It is concluded that the described method is able to provide an estimate of τa from MSG‐SEVIRI data. Such aerosol maps of high temporal frequency could be of interest to atmospheric related sciences, e.g., to track aerosol particle transport.

Abstract. In this paper, we evaluate the performance of two global horizontal solar irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from the 1-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3 × 5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km.The performances of the MSG-GHI estimate and RAMS-GHI 1-day forecast are evaluated for 1 year (1 June 2013–31 May 2014) against data of 12 ground-based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate.Statistics for hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysisResults for hourly data show an evident dependence on the sky conditions, with the root mean square error (RMSE) increasing from clear to cloudy conditions. The RMSE is substantially higher for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics computed from hourly data for the RAMS model, the RMSE ranges from 152 W m−2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W m−2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics computed from hourly data for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W m−2, 14 %), while the maximum is for Aosta (181 W m−2, 51 %). The mean bias error (MBE) shows the tendency of RAMS to over-forecast the GHI, while no specific behaviour is found for MSG-GHI.Results for daily integrated GHI show a lower RMSE compared to hourly GHI evaluation for both RAMS-GHI 1-day forecast and MSG-GHI estimate. Considering the yearly evaluation, the RMSE of daily integrated GHI is at least 9 % lower (in percentage units, from 31 to 22 % for RAMS in Cozzo Spadaro) than the RMSE computed for hourly data for each station. A partial compensation of underestimation and overestimation of the GHI contributes to the RMSE reduction. Furthermore, a post-processing technique, namely model output statistics (MOS), is applied to improve the GHI forecast at hourly and daily temporal scales. The application of MOS shows an improvement of RAMS-GHI forecast, which depends on the site considered, while the impact of MOS on MSG-GHI RMSE is small.

This article provides a description of a land surface temperature (LST) data set generated (and provided in near-real-time or offline) based on infrared data from sensors onboard different geostationary (GEO) satellites: Meteosat Second Generation (MSG), Geostationary Operational Environmental Satellite (GOES), and Multifunction Transport Satellite (MTSAT). Given the different characteristics of the imagers onboard each GEO platform, different algorithmic methodologies for the retrieval of LST are presented and implemented – namely the Generalized Split-Window (GSW) algorithm and the Dual Algorithm (DA) in its mono- and dual-channel forms – using semi-empirical functions that relate LST to top-of-atmosphere brightness temperatures in infrared window channels. The assumptions and physics underlying each methodology, as well as the uncertainties of LST estimates, are discussed. The formulations are trained using a data set of radiative transfer simulations for a wide range of atmospheric and surface conditions. The performance of each algorithm is then assessed by comparing its output against an independent set of simulations, suggesting that product uncertainties range from 2°C (for GSW and the two-channel algorithm) to 4°C (for the one-channel algorithm). Finally, LST retrievals from different GEO satellites are merged into a single field. In overlapping areas, the average discrepancies between LST products derived from GOES and from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard MSG are within 1°C during night-time, but may reach 3°C during daytime. Over those areas, the merged LST field is obtained as a weighted average of available LST retrievals for the same time slot, taking into account the respective error bar.

Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community, and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites), a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day Albedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a climate data record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: One for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition, that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.

Cloud detection is an important pre-processing step to derive operational products from meteorological satellites. This work presents a new cloud-detection algorithm with Meteosat Second Generation (MSG) images, operative at global scale. The algorithm takes advantage of the spectral and temporal resolution of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor. The algorithm is fully automatic in all its stages, including the thresholds definition by means of a self-learning methodology. These properties remove the need for ancillary data and restrictions in the area of application. This algorithm has been used in order to generate cloud masks during 2009. These cloud masks have been compared to the masks obtained with the National Aeronautics and Space Administration algorithm MOD35 with Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) images and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) algorithm for MSG–SEVIRI in Spain territory. The result shows an 88% agreement with EUMETSAT and a better than 83% agreement with the MOD35 algorithm.

Abstract. Novel methods of cloud detection are applied to airborne remote sensing observations from the unique Fennec aircraft dataset, to evaluate the Met Office-derived products on cloud properties over the Sahara based on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on-board the Meteosat Second Generation (MSG) satellite. Two cloud mask configurations are considered, as well as the retrievals of cloud-top height (CTH), and these products are compared to airborne cloud remote sensing products acquired during the Fennec campaign in June 2011 and June 2012. Most detected clouds (67 % of the total) have a horizontal extent that is smaller than a SEVIRI pixel (3 km × 3 km). We show that, when partially cloud-contaminated pixels are included, a match between the SEVIRI and aircraft datasets is found in 80 ± 8 % of the pixels. Moreover, under clear skies the datasets are shown to agree for more than 90 % of the pixels. The mean cloud field, derived from the satellite cloud mask acquired during the Fennec flights, shows that areas of high surface albedo and orography are preferred sites for Saharan cloud cover, consistent with published theories. Cloud-top height retrievals however show large discrepancies over the region, which are ascribed to limiting factors such as the cloud horizontal extent, the derived effective cloud amount, and the absorption by mineral dust. The results of the CTH analysis presented here may also have further-reaching implications for the techniques employed by other satellite applications facilities across the world.

Land Surface Temperature (LST) and Surface Net Radiation (SNR) are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions. Although high-resolution LST and SNR retrievals are available, they have large gaps due to cloud-cover that hinder their use as input in models. Here, we present a downscaled and continuous daily LST and SNR product across Europe for 2018–2019. The LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite, and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel 3 satellites. The product combines the medium spatial (approx. 5–7 km) but high temporal (30 minute) resolution, gap-free data from MSG, with the low temporal (2–3 days) but high spatial (1 km) resolution of the Sentinel 3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias-correction and Kalman Filter assimilation. Longwave outgoing radiation is computed from the merged LST product in combination with MSG-based emissivity data. Shortwave outgoing radiation is computed from the incoming shortwave radiation from MSG and downscaled albedos using 1 km PROBA-V data. MSG incoming shortwave and longwave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the root mean squared error by ca. 8 % with a substantial increase in spatial detail compared to the original MSG product. The resulting pan-European LST and SNR dataset can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future.