Journal articles: 'MSG-SEVIRI'

1

Surer, S., J. Parajka, and Z. Akyurek. "Validation of the operational MSG-SEVIRI snow cover product over Austria." Hydrology and Earth System Sciences 18, no. 2 (February 24, 2014): 763–74. http://dx.doi.org/10.5194/hess-18-763-2014.

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Abstract. The objective of this study is to evaluate the mapping accuracy of the MSG-SEVIRI operational snow cover product over Austria. The SEVIRI instrument is aboard the geostationary Meteosat Second Generation (MSG) satellite. The snow cover product provides 32 images per day, with a relatively low spatial resolution of 5 km over Austria. The mapping accuracy is examined at 178 stations with daily snow depth observations and compared with the daily MODIS-combined (Terra + Aqua) snow cover product for the period April 2008–June 2012. The results show that the 15 min temporal sampling allows a significant reduction of clouds in the snow cover product. The mean annual cloud coverage is less than 30% in Austria, as compared to 52% for the combined MODIS product. The mapping accuracy for cloud-free days is 89% as compared to 94% for MODIS. The largest mapping errors are found in regions with large topographical variability. The errors are noticeably larger at stations with elevations that differ greatly from those of the mean MSG-SEVIRI pixel elevations. The median of mapping accuracy for stations with absolute elevation difference less than 50 m and more than 500 m is 98.9 and 78.2%, respectively. A comparison between the MSG-SEVIRI and MODIS products indicates an 83% overall agreement. The largest disagreements are found in Alpine valleys and flatland areas in the spring and winter months, respectively.

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Reuter, M., and S. Pfeifer. "Moments from space captured by MSG SEVIRI." International Journal of Remote Sensing 32, no. 14 (June 30, 2011): 4131–40. http://dx.doi.org/10.1080/01431161.2011.566288.

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Surer, S., J. Parajka, and Z. Akyurek. "Validation of the operational MSG-SEVIRI snow cover product over Austria." Hydrology and Earth System Sciences Discussions 10, no. 10 (October 7, 2013): 12153–85. http://dx.doi.org/10.5194/hessd-10-12153-2013.

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Abstract. The objective of this study is to evaluate the mapping accuracy of the MSG-SEVIRI operational snow cover product over Austria. The SEVIRI instrument is on board of the geostationary Meteosat Second Generation (MSG) satellite. The snow cover product provides 32 images per day with a relatively low spatial resolution of 5 km over Austria. The mapping accuracy is examined at 178 stations with daily snow depth observations and compared with the daily MODIS combined (Terra + Aqua) snow cover product in the period April 2008–June 2012. The results show that the 15 min temporal sampling allows a significant reduction of clouds in the snow cover product. The mean annual cloud coverage is less than 30% in Austria, as compared to 52% for the combined MODIS product. The mapping accuracy for cloud-free days is 89% as compared to 94% for MODIS. The largest mapping errors are found in regions with large topographical variability. The errors are noticeably larger at stations with elevations that differ much from those of the mean MSG-SEVIRI pixel elevations. The median of mapping accuracy for stations with absolute elevation difference less than 50 m and more than 500 m is 98.9% and 78.2%, respectively. A comparison between the MSG-SEVIRI and MODIS products indicates an 83% overall agreement. The largest disagreements are found in alpine valleys and flatland areas in the spring and winter months, respectively.

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Derrien, M., and H. Le Gléau. "MSG/SEVIRI cloud mask and type from SAFNWC." International Journal of Remote Sensing 26, no. 21 (November 10, 2005): 4707–32. http://dx.doi.org/10.1080/01431160500166128.

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Jimenez-Munoz, Juan C., Jose A. Sobrino, Cristian Mattar, Glynn Hulley, and Frank-M. Gottsche. "Temperature and Emissivity Separation From MSG/SEVIRI Data." IEEE Transactions on Geoscience and Remote Sensing 52, no. 9 (September 2014): 5937–51. http://dx.doi.org/10.1109/tgrs.2013.2293791.

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Thieuleux, F., C. Moulin, F. M. Bréon, F. Maignan, J. Poitou, and D. Tanré. "Remote sensing of aerosols over the oceans using MSG/SEVIRI imagery." Annales Geophysicae 23, no. 12 (December 23, 2005): 3561–68. http://dx.doi.org/10.5194/angeo-23-3561-2005.

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Abstract. The SEVIRI instrument on board Meteosat Second Generation (MSG) offers new capabilities to monitor aerosol transport over the Atlantic and the Mediterranean at high temporal and spatial resolutions, in particular, Saharan dust from North Africa, biomass-burning aerosols from subtropical Africa and pollution from Europe. An inversion technique was developed to estimate both aerosol optical thickness and Angström coefficients from SEVIRI measurements at 0.63 and 0.81 µm. This method relies on an optimized set of aerosol models to ensure a fast processing of full-resolution MSG images and to allow the processing of long time series. SEVIRI images for slots 45, 49 and 53 (11:15, 12:15, 13:15 UT) were processed for June 2003. The retrieved optical thicknesses and Angström coefficients are in good agreement with AERONET in-situ measurements in the Atlantic and in the Mediterranean. Monthly mean maps of both parameters are compared to that obtained with the polar orbiting sensor POLDER for June 2003. There is a good consistency between the two monthly means in terms of optical thickness, but the Angström coefficients show significant differences in the Atlantic zone which is affected by dust transport. These differences may be explained by the lack of specific non-spherical dust models within the inversion. The preliminary results presented in this paper demonstrate, nevertheless, the potential of MSG/SEVIRI for the monitoring of aerosol optical properties at high frequencies over the Atlantic and the Mediterranean.

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Henken, Cintia Carbajal, Maurice J. Schmeits, Hartwig Deneke, and Rob A. Roebeling. "Using MSG-SEVIRI Cloud Physical Properties and Weather Radar Observations for the Detection of Cb/TCu Clouds." Journal of Applied Meteorology and Climatology 50, no. 7 (July 2011): 1587–600. http://dx.doi.org/10.1175/2011jamc2601.1.

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AbstractA new automated daytime cumulonimbus/towering cumulus (Cb/TCu) cloud detection method for the months of May–September is presented that combines information on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG) satellites and weather radar reflectivity factors. First, a pixel-based convective cloud mask (CCM) is constructed on the basis of cloud physical properties [cloud-top temperature, cloud optical thickness (COT), effective radius, and cloud phase] derived from SEVIRI. Second, a logistic regression model is applied to determine the probability of Cb/TCu clouds for the collection of pixels that pass the CCM. In this model, MSG-SEVIRI cloud physical properties and weather radar reflectivity factors are used as potential predictor sources. The predictand is derived from aviation routine weather reports (METAR) made by human observers at Amsterdam Airport Schiphol for 2004–07. Results show that the CCM filters out >70% of the “no” events (no Cb/TCu cloud) and that >93% of the “yes” events (Cb/TCu cloud) are retained. Most skillful predictors are derived from radar reflectivity factors and the COT of high resolution. The derived probabilities from the combined MSG and radar method clearly show skill over sample climatology. Probability thresholds are used to convert derived probabilities into derived group memberships (i.e., yes/no Cb/TCu clouds). When comparing verification scores between the combined MSG and radar method and either the radar-only method or the MSG-only method, the combined MSG and radar method shows slightly better performance. When comparing the combined MSG and radar method with the current Royal Netherlands Meteorological Institute (KNMI) radar-based Cb/TCu cloud detection method, the two methods show comparable probability of detection, but the former shows a false-alarm ratio that is about 8% lower. Moreover, a big advantage of the newly developed method is that it provides probabilities, in contrast to the current KNMI method.

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Vázquez-Navarro, M., B. Mayer, and H. Mannstein. "A fast method for the retrieval of integrated longwave and shortwave top-of-atmosphere upwelling irradiances from MSG/SEVIRI (RRUMS)." Atmospheric Measurement Techniques 6, no. 10 (October 15, 2013): 2627–40. http://dx.doi.org/10.5194/amt-6-2627-2013.

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Abstract. A new Rapid Retrieval of Upwelling irradiances from MSG/SEVIRI (RRUMS) is presented. It has been developed to observe the top-of-atmosphere irradiances of small scale and rapidly changing features that are not sufficiently resolved by specific Earth radiation budget sensors. Our retrieval takes advantage of the spatial and temporal resolution of MSG/SEVIRI and provides outgoing longwave and reflected shortwave radiation only by means of a combination of SEVIRI channels. The longwave retrieval is based on a simple linear combination of brightness temperatures from the SEVIRI infrared channels. The shortwave retrieval is based on a neural network that requires as input the visible and near-infrared SEVIRI channels. Both LW and SW algorithms have been validated by comparing their results with CERES and GERB irradiance observations. While being less accurate than their dedicated counterparts, the SEVIRI-based methods have two major advantages compared to CERES and GERB: their higher spatial resolution and the better temporal resolution. With our retrievals it is possible to observe the radiative effect of small-scale features such as cumulus clouds, cirrus clouds, or aircraft contrails. The spatial resolution of SEVIRI is 3 km × 3 km in the sub-satellite point, remarkably better than that of CERES (20 km) or GERB (45 km). The temporal resolution is 15 min (5 min in the Rapid-Scan mode), the same as GERB, but significantly better than that of CERES which, being on board of a polar orbiting satellite, has a temporal resolution as low as 2 overpasses per day.

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Heinold, B., I. Tegen, K. Schepanski, and J. R. Banks. "New developments in the representation of Saharan dust sources in the aerosol-climate model ECHAM6-HAM2." Geoscientific Model Development Discussions 8, no. 9 (September 11, 2015): 7879–910. http://dx.doi.org/10.5194/gmdd-8-7879-2015.

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Abstract. In the aerosol-climate model ECHAM6-HAM2, dust source activation (DSA) observations from Meteosat Second Generation (MSG) satellite are proposed to replace the original source area parameterization over the Sahara Desert. The new setup is tested in nudged simulations for the period 2007 to 2008. The evaluation is based on comparisons to dust emission events inferred from MSG dust index imagery, AERONET sun photometer observations, and satellite retrievals of aerosol optical thickness (AOT). The model results agree well with AERONET measurements. Good correlations between model results and MSG-SEVIRI dust AOT as well as Multi-angle Imaging Spectro-Radiometer (MISR) AOT indicate that also the spatial dust distribution is well reproduced. ECHAM6-HAM2 computes a more realistic geographical distribution and up to 20 % higher annual Saharan dust emissions, using the MSG-based source map. The representation of dust AOT is partly improved in the southern Sahara and Sahel. In addition, the spatial variability is increased towards a better agreement with observations depending on the season. Thus, using the MSG DSA map can help to circumvent the issue of uncertain soil input parameters. An important issue remains the need to improve the model representation of moist convection and stable nighttime conditions. Compared to sub-daily DSA information from MSG-SEVIRI and results from a regional model, ECHAM6-HAM2 notably underestimates the important fraction of morning dust events by the breakdown of the nocturnal low-level jet, while a major contribution is from afternoon-to-evening emissions.

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Lazri, Mourad, Karim Labad, Jean Michel Brucker, and Soltane Ameur. "Precipitation estimation by a multi-threshold method using cloud optical and microphysical properties from MSG / SEVIRI data." E3S Web of Conferences 170 (2020): 02002. http://dx.doi.org/10.1051/e3sconf/202017002002.

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The aim of this paper is the estimation of precipitation in northern Algeria using satellite data. To do this, a multi-threshold method based on the exploitation of the clouds optical and microphysical properties is developed. Depending on the availability of the MSG / SEVIRI (M eteosat Second Generation/Spinning Enhanced Visible and Infrared Imaging) satellite channels, the database is divided into daytime data and nighttime data. In the learning phase, daytime and nighttime two-dimensional thresholds are determined from comparisons between the MSG / SEVIRI satellite data of rainy season 2010/2011 and the corresponding data from the Sétif meteorological radar. Using linear regression, an empirical relationship between the SEVIRI spectral data and the precipitation intensities from the radar is determined.To estimate rainfall, the determined empirical relationship is ap plied to the validation dataset collected during rainy season 2011/2012. To evaluate the method, a comparison of the estimation results with reference radar data is performed. The results show that these estimations are in good correlation with those measured by radar.

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García-Haro, Francisco Javier, Fernando Camacho, Beatriz Martínez, Manuel Campos-Taberner, Beatriz Fuster, Jorge Sánchez-Zapero, and María Amparo Gilabert. "Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications." Remote Sensing 11, no. 18 (September 9, 2019): 2103. http://dx.doi.org/10.3390/rs11182103.

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The scientific community requires long-term data records with well-characterized uncertainty and suitable for modeling terrestrial ecosystems and energy cycles at regional and global scales. This paper presents the methodology currently developed in EUMETSAT within its Satellite Application Facility for Land Surface Analysis (LSA SAF) to generate biophysical variables from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board MSG 1-4 (Meteosat 8-11) geostationary satellites. Using this methodology, the LSA SAF generates and disseminates at a time a suite of vegetation products, such as the leaf area index (LAI), the fraction of the photosynthetically active radiation absorbed by vegetation (FAPAR) and the fractional vegetation cover (FVC), for the whole Meteosat disk at two temporal frequencies, daily and 10-days. The FVC algorithm relies on a novel stochastic spectral mixture model which addresses the variability of soils and vegetation types using statistical distributions whereas the LAI and FAPAR algorithms use statistical relationships general enough for global applications. An overview of the LSA SAF SEVIRI/MSG vegetation products, including expert knowledge and quality assessment of its internal consistency is provided. The climate data record (CDR) is freely available in the LSA SAF, offering more than fifteen years (2004-present) of homogeneous time series required for climate and environmental applications. The high frequency and good temporal continuity of SEVIRI products addresses the needs of near-real-time users and are also suitable for long-term monitoring of land surface variables. The study also evaluates the potential of the SEVIRI/MSG vegetation products for environmental applications, spanning from accurate monitoring of vegetation cycles to resolving long-term changes of vegetation.

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Govaerts, Y. M., A. Arriaga, and J. Schmetz. "Operational vicarious calibration of the MSG/SEVIRI solar channels." Advances in Space Research 28, no. 1 (January 2001): 21–30. http://dx.doi.org/10.1016/s0273-1177(01)00269-1.

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Heinold, Bernd, Ina Tegen, Kerstin Schepanski, and Jamie R. Banks. "New developments in the representation of Saharan dust sources in the aerosol–climate model ECHAM6-HAM2." Geoscientific Model Development 9, no. 2 (February 25, 2016): 765–77. http://dx.doi.org/10.5194/gmd-9-765-2016.

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Abstract. In the aerosol–climate model ECHAM6-HAM2, dust source activation (DSA) observations from Meteosat Second Generation (MSG) satellite are proposed to replace the original source area parameterization over the Sahara Desert. The new setup is tested in nudged simulations for the period 2007 to 2008. The evaluation is based on comparisons to dust emission events inferred from MSG dust index imagery, Aerosol Robotic Network (AERONET) sun photometer observations, and satellite retrievals of aerosol optical thickness (AOT).The model results agree well with AERONET measurements especially in terms of seasonal variability, and a good spatial correlation was found between model results and MSG-SEVIRI (Spinning-Enhanced Visible and InfraRed Imager) dust AOT as well as Multi-angle Imaging SpectroRadiometer (MISR) AOT. ECHAM6-HAM2 computes a more realistic geographical distribution and up to 20 % higher annual Saharan dust emissions, using the MSG-based source map. The representation of dust AOT is partly improved in the southern Sahara and Sahel. In addition, the spatial variability is increased towards a better agreement with observations depending on the season. Thus, using the MSG DSA map can help to circumvent the issue of uncertain soil input parameters.An important issue remains the need to improve the model representation of moist convection and stable nighttime conditions. Compared to sub-daily DSA information from MSG-SEVIRI and results from a regional model, ECHAM6-HAM2 notably underestimates the important fraction of morning dust events by the breakdown of the nocturnal low-level jet, while a major contribution is from afternoon-to-evening emissions.

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Ghilain, N., A. Arboleda, G. Sepulcre-Cantò, O. Batelaan, J. Ardö, and F. Gellens-Meulenberghs. "Improving evapotranspiration in land surface models by using biophysical parameters derived from MSG/SEVIRI satellite." Hydrology and Earth System Sciences Discussions 8, no. 5 (October 14, 2011): 9113–71. http://dx.doi.org/10.5194/hessd-8-9113-2011.

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Abstract. Vegetation parameters derived from the geostationary satellite MSG/SEVIRI have been distributed at a daily frequency since 2007 over Europe, Africa and part of South America, through the LSA-SAF facility. We propose here a method to handle two new remote sensing products from LSA-SAF, leaf area index and Fractional Vegetation Cover, noted LAI and FVC respectively, for land surface models at MSG/SEVIRI scale. The developed method relies on an ordinary least-square technique and a land cover map to estimate LAI for each model plant functional types of the model spatial unit. The method is conceived to be applicable for near-real time applications at continental scale. Compared to monthly vegetation parameters from a vegetation database commonly used in numerical weather predictions (ECOCLIMAP-I), the new remote sensing products allows a better monitoring of the spatial and temporal variability of the vegetation, including inter-annual signals, and a decreased uncertainty on LAI to be input into land surface models. We assess the impact of using LSA-SAF vegetation parameters compared to ECOCLIMAP-I in the land surface model H-TESSEL at MSG/SEVIRI scale. Comparison with in-situ observations in Europe and Africa shows that the results on evapotranspiration are mostly improved, and especially in semi-arid climates. At last, the use of LSA-SAF and ECOCLIMAP-I is compared with simulations over a North-South Transect in Western Africa using LSA-SAF radiation forcing derived from remote sensing, and differences are highlighted.

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Vázquez-Navarro, M., B. Mayer, and H. Mannstein. "A fast method for the retrieval of integrated longwave and shortwave top-of-atmosphere irradiances from MSG/SEVIRI (RRUMS)." Atmospheric Measurement Techniques Discussions 5, no. 4 (July 20, 2012): 4969–5008. http://dx.doi.org/10.5194/amtd-5-4969-2012.

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Abstract. A new Rapid Retrieval of upwelling fluxes from MSG/SEVIRI (RRUMS) is presented. It has been developed to observe the top-of-atmosphere irradiances of small scale and rapidly changing features that are not sufficiently resolved by specific Earth radiation budget sensors. Our retrieval takes advantage of the spatial and temporal resolution of MSG/SEVIRI and provides outgoing longwave and reflected shortwave radiation only by means of a combination of SEVIRI channels. The longwave retrieval is based on a simple linear combination of brightness temperatures from the SEVIRI infrared channels. Two shortwave retrievals are presented and discussed: the first one based on a multilinear parameterisation and the second one based on a neural network. The neural network method is shown to be slightly more accurate and simpler to apply for the desired purpose. Both LW and SW algorithms have been validated by comparing their results with CERES and GERB irradiance observations. While being less accurate than their dedicated counterparts, the SEVIRI-based methods have two major advantages compared to CERES and GERB: their higher spatial resolution and the better temporal resolution. With our retrievals it is possible to observe the radiative effect of small-scale features such as cumulus clouds, cirrus clouds, or aircraft contrails. The spatial resolution of SEVIRI is 3 km &times 3 km in the sub-satellite point, remarkably better than that of CERES (20 km) or GERB (45 km). The temporal resolution is 15 min (5 min in the rapid-scan mode), the same as GERB, but significantly better than that of CERES which, being on board of a polar orbiting satellite, has a temporal resolution as low as 2 overpasses per day.

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Strandgren, Johan, Luca Bugliaro, Frank Sehnke, and Leon Schröder. "Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks." Atmospheric Measurement Techniques 10, no. 9 (September 29, 2017): 3547–73. http://dx.doi.org/10.5194/amt-10-3547-2017.

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Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth–atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data. CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m−2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness. By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

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Benas, Nikos, Stephan Finkensieper, Martin Stengel, Gerd-Jan van Zadelhoff, Timo Hanschmann, Rainer Hollmann, and Jan Fokke Meirink. "The MSG-SEVIRI-based cloud property data record CLAAS-2." Earth System Science Data 9, no. 2 (July 10, 2017): 415–34. http://dx.doi.org/10.5194/essd-9-415-2017.

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Abstract. Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial for monitoring clouds and understanding their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), a new cloud property data record was derived from geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) data record is publicly available via the CM SAF website (https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002). In this paper we present an extensive evaluation of the CLAAS-2 cloud products, which include cloud fractional coverage, thermodynamic phase, cloud top properties, liquid/ice cloud water path and corresponding optical thickness and particle effective radius. Data validation and comparisons were performed on both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and monthly averages and histograms) with reference datasets derived from lidar, microwave and passive imager measurements. The evaluation results show very good overall agreement with matching spatial distributions and temporal variability and small biases attributed mainly to differences in sensor characteristics, retrieval approaches, spatial and temporal samplings and viewing geometries. No major discrepancies were found. Underpinned by the good evaluation results, CLAAS-2 demonstrates that it is fit for the envisaged applications, such as process studies of the diurnal cycle of clouds and the evaluation of regional climate models. The data record is planned to be extended and updated in the future.

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Romano, Filomena, Elisabetta Ricciardelli, Domenico Cimini, Francesco Di Paola, and Mariassunta Viggiano. "Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data." Atmosphere 4, no. 1 (March 5, 2013): 35–47. http://dx.doi.org/10.3390/atmos4010035.

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Peres, L. F., and C. C. DaCamara. "Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI." IEEE Transactions on Geoscience and Remote Sensing 43, no. 8 (August 2005): 1834–44. http://dx.doi.org/10.1109/tgrs.2005.851172.

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Lu, Lei, Valentijn Venus, Andrew Skidmore, Tiejun Wang, and Geping Luo. "Estimating land-surface temperature under clouds using MSG/SEVIRI observations." International Journal of Applied Earth Observation and Geoinformation 13, no. 2 (April 2011): 265–76. http://dx.doi.org/10.1016/j.jag.2010.12.007.

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Eckardt, F. D., S. Bekiswa, J. R. Von Holdt, C. Jack, N. J. Kuhn, F. Mogane, J. E. Murray, N. Ndara, and A. R. Palmer. "South Africa’s agricultural dust sources and events from MSG SEVIRI." Aeolian Research 47 (December 2020): 100637. http://dx.doi.org/10.1016/j.aeolia.2020.100637.

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Debaecker, Vincent, Sultan Kocaman, Sebastien Saunier, Kevin Garcia, Sila Bas, and Dieter Just. "On the geometric accuracy and stability of MSG SEVIRI images." Atmospheric Environment 262 (October 2021): 118645. http://dx.doi.org/10.1016/j.atmosenv.2021.118645.

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Mecikalski, John R., Wayne M. MacKenzie, Marianne König, and Sam Muller. "Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part II: Use of Visible Reflectance." Journal of Applied Meteorology and Climatology 49, no. 12 (December 1, 2010): 2544–58. http://dx.doi.org/10.1175/2010jamc2480.1.

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Abstract This study is a companion research effort to “Part I,” which emphasized use of infrared data for understanding various aspects of growing convective clouds in the Meteosat Second Generation (MSG) satellite’s Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery. Reflectance and derived brightness variability (BV) fields from MSG SEVIRI are used here to understand relationships between cloud-top signatures and physical processes for growing cumulus clouds prior to known convective initiation (CI) events, or the first occurrence of a ≥35-dBZ echo from a new convective cloud. This study uses daytime SEVIRI visible (VIS) and near-infrared (NIR) reflectances from 0.6 to 3.9 μm (3-km sampling distance), as well as high-resolution visible (1-km sampling distance) fields. Data from 123 CI events observed during the 2007 Convection and Orographically Induced Precipitation Study (COPS) field experiment conducted over southern Germany and northeastern France are processed, per convective cell, so to meet this study’s objectives. These data are those used in Part I. A total of 27 VIS–NIR and BV “interest fields” are initially assessed for growing cumulus clouds, with correlation and principal component analyses used to highlight the fields that contain the most unique information for describing principally cloud-top glaciation, as well as the presence of vigorous updrafts. Time changes in 1.6- and 3.9-μm reflectances, as well as BV in advance of CI, are shown to contain the most unique information related to the formation and increase in size of ice hydrometeors. Several methods are proposed on how results from this analysis may be used to monitor growing convective clouds per MSG pixel or per cumulus cloud “object” over 1-h time frames.

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Ricciardelli, E., D. Cimini, F. Di Paola, F. Romano, and M. Viggiano. "A statistical approach for rain class evaluation using Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager observations." Hydrology and Earth System Sciences Discussions 10, no. 11 (November 12, 2013): 13671–706. http://dx.doi.org/10.5194/hessd-10-13671-2013.

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Abstract. Precipitation measurements are essential for short term hydrological and long term climate studies. Operational networks of rain gauges and weather radars provide fairly accurate rain rate measurements, but they leave large areas uncovered. Because of this, satellite remote sensing is a useful tool for the detection and characterization of the raining areas in regions where this information remains missing. This study exploits the Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) observations to evaluate the rain class at high spatial and temporal resolutions. The Rain Class Evaluation from Infrared and Visible (RainCEIV) observations technique is proposed. The purpose of RainCEIV is to supply continuous monitoring of convective as well as of stratiform rainfall events. It applies a supervised classifier to the spectral and textural features of infrared and visible MSG-SEVIRI images to classify the cloudy pixels as non rainy, light to moderate rain, or heavy to very heavy rain. The technique considers in input also the water vapour channels brightness temperatures differences for the MSG-SEVIRI images acquired 15/30/45 min before the time of interest. The rainfall rates used in the training phase are obtained with the Precipitation Estimation at Microwave frequencies (PEMW), an algorithm for rain rate retrievals based on Atmospheric Microwave Sounder Unit (AMSU)-B observations. The results of RainCEIV have been validated against radar-derived rainfall measurements from the Italian Operational Weather Radar Network for some case studies limited to the Mediterranean area. The dichotomous assessment shows that RainCEIV is able to detect rainy areas with an accuracy of about 91%, a Heidke skill score of 56%, a Bias score of 1.16, and a Probability of Detection of rainy areas of 66%.

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Kumah, Kingsley K., Joost C. B. Hoedjes, Noam David, Ben H. P. Maathuis, H. Oliver Gao, and Bob Z. Su. "Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation." Atmosphere 11, no. 9 (August 19, 2020): 884. http://dx.doi.org/10.3390/atmos11090884.

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Accurate rainfall detection and estimation are essential for many research and operational applications. Traditional rainfall detection and estimation techniques have achieved considerable success but with limitations. Thus, in this study, the relationships between the gauge (point measurement) and the microwave links (MWL) rainfall (line measurement), and the MWL to the satellite observations (area-wide measurement) are investigated for (area-wide) rainfall detection and rain rate retrieval. More precisely, we investigate if the combination of MWL with Meteosat Second Generation (MSG) satellite signals could improve rainfall detection and rainfall rate estimates. The investigated procedure includes an initial evaluation of the MWL rainfall estimates using gauge measurements, followed by a joint analysis of the rainfall estimates with the satellite signals by means of a conceptual model in which clouds with high cloud top optical thickness and large particle sizes have high rainfall probabilities and intensities. The analysis produced empirical thresholds that were used to test the capability of the MSG satellite data to detect rainfall on the MWL. The results from Kenya, during the “long rains” of 2013, 2014, and 2018 show convincing performance and reveal the potential of MWL and MSG data for area-wide rainfall detection.

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Seethala, Chellappan, Jan Fokke Meirink, Ákos Horváth, Ralf Bennartz, and Rob Roebeling. "Evaluating the diurnal cycle of South Atlantic stratocumulus clouds as observed by MSG SEVIRI." Atmospheric Chemistry and Physics 18, no. 17 (September 14, 2018): 13283–304. http://dx.doi.org/10.5194/acp-18-13283-2018.

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Abstract. Marine stratocumulus (Sc) clouds play an essential role in the earth radiation budget. Here, we compare liquid water path (LWP), cloud optical thickness (τ), and cloud droplet effective radius (re) retrievals from 2 years of collocated Spinning Enhanced Visible and Infrared Imager (SEVIRI), Moderate Resolution Imaging Spectroradiometer (MODIS), and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations; estimate the effect of biomass burning smoke on passive imager retrievals; and evaluate the diurnal cycle of South Atlantic marine Sc clouds.The effect of absorbing aerosols from biomass burning on the retrievals was investigated using the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI). SEVIRI and MODIS LWPs were found to decrease with increasing AI relative to TMI LWP, consistent with well-known negative visible/near-infrared (VIS/NIR) retrieval biases in τ and re. In the aerosol-affected months of July–August–September, SEVIRI LWP – based on the 1.6 µm re – was biased low by 14 g m−2 ( ∼ 16 %) compared to TMI in overcast scenes, while MODIS LWP showed a smaller low bias of 4 g m−2 ( ∼ 5 %) for the 1.6 µm channel and a high bias of 8 g m−2 ( ∼ 10 %) for the 3.7 µm channel compared to TMI. Neglecting aerosol-affected pixels reduced the mean SEVIRI–TMI LWP bias considerably. For 2 years of data, SEVIRI LWP had a correlation with TMI and MODIS LWP of about 0.86 and 0.94, respectively, and biases of only 4–8 g m−2 (5 %–10 %) for overcast cases.The SEVIRI LWP diurnal cycle was in good overall agreement with TMI except in the aerosol-affected months. Both TMI and SEVIRI LWP decreased from morning to late afternoon, after which a slow increase was observed. Terra and Aqua MODIS mean LWPs also suggested a similar diurnal variation. The relative amplitude of the 2-year-mean and seasonal-mean LWP diurnal cycle varied between 35 % and 40 % from morning to late afternoon for overcast cases. The diurnal variation in SEVIRI LWP was mainly due to changes in τ, while re showed only little diurnal variability.

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Amraoui, M., C. C. DaCamara, and J. M. C. Pereira. "Detection and monitoring of African vegetation fires using MSG-SEVIRI imagery." Remote Sensing of Environment 114, no. 5 (May 2010): 1038–52. http://dx.doi.org/10.1016/j.rse.2009.12.019.

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Pérez, J. C., A. González, and M. Armas-Padilla. "Remote sensing of water cloud properties from MSG/SEVIRI nighttime imagery." Remote Sensing of Environment 115, no. 2 (February 15, 2011): 738–46. http://dx.doi.org/10.1016/j.rse.2010.10.015.

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Le Borgne, Pierre, Gérard Legendre, and Sonia Péré. "Comparison of MSG/SEVIRI and drifting buoy derived diurnal warming estimates." Remote Sensing of Environment 124 (September 2012): 622–26. http://dx.doi.org/10.1016/j.rse.2012.06.015.

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Sifakis, Nicolaos I., Christos Iossifidis, Charalabos Kontoes, and Iphigenia Keramitsoglou. "Wildfire Detection and Tracking over Greece Using MSG‑SEVIRI Satellite Data." Remote Sensing 3, no. 3 (March 9, 2011): 524–38. http://dx.doi.org/10.3390/rs3030524.

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Ganci, Gaetana, Annamaria Vicari, Sergio Bonfiglio, Giovanni Gallo, and Ciro Del negro. "A texton-based cloud detection algorithm for MSG-SEVIRI multispectral images." Geomatics, Natural Hazards and Risk 2, no. 3 (September 2011): 279–90. http://dx.doi.org/10.1080/19475705.2011.578263.

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González, Albano, Juan C. Pérez, Jonathan Muñoz, Zebensui Méndez, and Montserrat Armas. "Watershed image segmentation and cloud classification from multispectral MSG–SEVIRI imagery." Advances in Space Research 49, no. 1 (January 2012): 135–42. http://dx.doi.org/10.1016/j.asr.2011.09.023.

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Meyer, Hanna, Johannes Schmidt, Florian Detsch, and Thomas Nauss. "Hourly gridded air temperatures of South Africa derived from MSG SEVIRI." International Journal of Applied Earth Observation and Geoinformation 78 (June 2019): 261–67. http://dx.doi.org/10.1016/j.jag.2019.02.006.

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Hall, J. V., R. Zhang, W. Schroeder, C. Huang, and L. Giglio. "Validation of GOES-16 ABI and MSG SEVIRI active fire products." International Journal of Applied Earth Observation and Geoinformation 83 (November 2019): 101928. http://dx.doi.org/10.1016/j.jag.2019.101928.

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Vázquez-Navarro, M., H. Mannstein, and S. Kox. "Contrail life cycle and properties from 1 year of MSG/SEVIRI rapid-scan images." Atmospheric Chemistry and Physics 15, no. 15 (August 10, 2015): 8739–49. http://dx.doi.org/10.5194/acp-15-8739-2015.

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The automatic contrail tracking algorithm (ACTA) – developed to automatically follow contrails as they age, drift and spread – enables the study of a large number of contrails and the evolution of contrail properties with time. In this paper we present a year's worth of tracked contrails, from August 2008 to July 2009 in order to derive statistically significant mean values. The tracking is performed using the 5 min rapid-scan mode of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellites. The detection is based on the high spatial resolution of the images provided by the Moderate Resolution Imaging Spectroradiometer on board the Terra satellite (Terra/MODIS), where a contrail detection algorithm (CDA) is applied. The results show the satellite-derived average lifetimes of contrails and contrail-cirrus along with the probability density function (PDF) of other geometric characteristics such as mean coverage, distribution and width. In combination with specifically developed algorithms (RRUMS; Rapid Retrieval of Upwelling irradiance from MSG/SEVIRI and COCS (Cirrus Optical properties derived from CALIOP and SEVIRI), explained below) it is possible to derive the radiative forcing (RF), energy forcing (EF), optical thickness (τ) and altitude of the tracked contrails. Mean values here retrieved are duration, 1 h; length, 130 km; width, 8 km; altitude, 11.7 km; optical thickness, 0.34. Radiative forcing and energy forcing are shown for land/water backgrounds in day/night situations.

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Bley, S., and H. Deneke. "A robust threshold-based cloud mask for the HRV channel of MSG SEVIRI." Atmospheric Measurement Techniques Discussions 6, no. 2 (March 20, 2013): 2829–55. http://dx.doi.org/10.5194/amtd-6-2829-2013.

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Abstract. A robust threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the METEOSAT SEVIRI instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures which cannot be detected by the low resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behaviour for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test dataset depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as estimate of cloud fraction.

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Meyer, Hanna, Johannes Drönner, and Thomas Nauss. "Satellite-based high-resolution mapping of rainfall over southern Africa." Atmospheric Measurement Techniques 10, no. 6 (June 6, 2017): 2009–19. http://dx.doi.org/10.5194/amt-10-2009-2017.

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Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010–2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.

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Lombardo, Valerio, Stefano Corradini, Massimo Musacchio, Malvina Silvestri, and Jacopo Taddeucci. "Eruptive Styles Recognition Using High Temporal Resolution Geostationary Infrared Satellite Data." Remote Sensing 11, no. 6 (March 19, 2019): 669. http://dx.doi.org/10.3390/rs11060669.

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The high temporal resolution of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument aboard Meteosat Second Generation (MSG) provides the opportunity to investigate eruptive processes and discriminate different styles of volcanic activity. To this goal, a new detection method based on the wavelet transform of SEVIRI infrared data is proposed. A statistical analysis is performed on wavelet smoothed data derived from SEVIRI Mid-Infrared( MIR) radiances collected from 2011 to 2017 on Mt Etna (Italy) volcano. Time-series analysis of the kurtosis of the radiance distribution allows for reliable hot-spot detection and precise timing of the start and end of eruptive events. Combined kurtosis and gradient trends allow for discrimination of the different activity styles of the volcano, from effusive lava flow, through Strombolian explosions, to paroxysmal fountaining. The same data also allow for the prediction, at the onset of an eruption, of what will be its dominant eruptive style at later stages. The results obtained have been validated against ground-based and literature data.

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Stisen, Simon, Inge Sandholt, Anette Nørgaard, Rasmus Fensholt, and Lars Eklundh. "Estimation of diurnal air temperature using MSG SEVIRI data in West Africa." Remote Sensing of Environment 110, no. 2 (September 2007): 262–74. http://dx.doi.org/10.1016/j.rse.2007.02.025.

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Sun, D., and R. T. Pinker. "Retrieval of surface temperature from the MSG‐SEVIRI observations: Part I. Methodology." International Journal of Remote Sensing 28, no. 23 (November 20, 2007): 5255–72. http://dx.doi.org/10.1080/01431160701253246.

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Calle, A., J. L. Casanova, and F. González-Alonso. "Impact of point spread function of MSG-SEVIRI on active fire detection." International Journal of Remote Sensing 30, no. 17 (August 20, 2009): 4567–79. http://dx.doi.org/10.1080/01431160802609726.

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Bouchouicha, Kada, Abdelhak Razagui, Nour El Islam Bachari, and Nouar Aoun. "Hourly global solar radiation estimation from MSG-SEVIRI images-case study: Algeria." World Journal of Engineering 13, no. 3 (June 13, 2016): 266–74. http://dx.doi.org/10.1108/wje-06-2016-036.

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Purpose This paper aims to propose an approach based on physical model integration for surface and cloud albedo computation using an approximate form of the atmospheric radiative transfer equation and sun-pixel-satellite. Design/methodology/approach The data used in this study are global irradiance collected from for various sites in Algeria, and data were obtained from the processing of the high-resolution visible images taken by the Meteosat Second Generation satellite in 2010. Findings The results suggest that the standard deviation obtained with this method is similar to that obtained with current estimation methods. The hourly and daily correlation coefficients range between 0.95 and 0.97 and between 0.97 and 0.99, respectively. The hourly and daily mean bias errors range between −0.2 and +1.2 per cent and between −0.2 and +1.4 per cent, respectively. The hourly and daily root mean square errors range between 10 and 17 per cent and between 4 and 8 per cent, respectively. Originality/value This paper developed a new estimating method that derives the hourly global horizontal solar irradiation at a ground level from geostationary satellite data under local climate conditions.

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Peres, Leonardo F., Renata Libonati, and Carlos C. DaCamara. "Land-Surface Emissivity Retrieval in MSG–SEVIRI TIR Channels Using MODIS Data." IEEE Transactions on Geoscience and Remote Sensing 52, no. 9 (September 2014): 5587–600. http://dx.doi.org/10.1109/tgrs.2013.2290778.

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Gao, Caixia, Xiaoguang Jiang, Hua Wu, Bohui Tang, Ziyang Li, and Zhao-Liang Li. "Comparison of land surface temperatures from MSG-2/SEVIRI and Terra/MODIS." Journal of Applied Remote Sensing 6, no. 1 (November 22, 2012): 063606. http://dx.doi.org/10.1117/1.jrs.6.063606.

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Lima, Wagner F. A., and Luiz A. T. Machado. "Cloud reflectivity profile classification using MSG/SEVIRI infrared multichannel and TRMM data." International Journal of Remote Sensing 34, no. 12 (March 22, 2013): 4384–405. http://dx.doi.org/10.1080/01431161.2013.776720.

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Martins, João P. A., Isabel F. Trigo, Nicolas Ghilain, Carlos Jimenez, Frank-M. Göttsche, Sofia L. Ermida, Folke-S. Olesen, Françoise Gellens-Meulenberghs, and Alirio Arboleda. "An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations." Remote Sensing 11, no. 24 (December 17, 2019): 3044. http://dx.doi.org/10.3390/rs11243044.

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A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

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Gouhier, Mathieu, Andrew Harris, Sonia Calvari, Philippe Labazuy, Yannick Guéhenneux, Franck Donnadieu, and Sébastien Valade. "Lava discharge during Etna's January 2011 fire fountain tracked using MSG-SEVIRI." Bulletin of Volcanology 74, no. 4 (December 3, 2011): 787–93. http://dx.doi.org/10.1007/s00445-011-0572-y.

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Aires, Filipe, Francis Marquisseau, Catherine Prigent, and Geneviève Sèze. "A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations." Monthly Weather Review 139, no. 8 (August 2011): 2347–66. http://dx.doi.org/10.1175/mwr-d-10-05012.1.

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AbstractA statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as the MW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations.Clear sky and low, medium, and opaque–high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds.

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Bernard, E., C. Moulin, D. Ramon, D. Jolivet, J. Riedi, and J. M. Nicolas. "Validation of an AOT product over land at the 0.6 μm channel of the SEVIRI sensor onboard MSG." Atmospheric Measurement Techniques Discussions 4, no. 3 (May 24, 2011): 3147–98. http://dx.doi.org/10.5194/amtd-4-3147-2011.

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Abstract. The Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) launched in 2003 by EUMETSAT is dedicated to the Nowcasting applications and Numerical Weather Prediction and to provide information for climate monitoring and research. We use the data in visible and near infrared channels to derive the Aerosol Optical Thickness (AOT) over land. The algorithm is based on the assumption that the Top Of the Atmosphere (TOA) reflectance increases with the aerosol load. This is a reasonable assumption except in case of absorbing aerosols above bright surfaces. We assume that the minimum in a 14-day time series of the TOA reflectance is, once corrected from gaseous scattering and absorption, representative of the surface reflectance. The AOT and the aerosol model (a set of 5 models are used), are retrieved by matching the simulated TOA reflectance with the TOA reflectances measured by SEVIRI in its visible and Near Infra-Red (NIR) spectral bands. The high temporal resolution of the data acquisition by SEVIRI allows to retrieve the AOT every 15 min with a spatial resolution of 3km at sub-satellite point, over the whole SEVIRI disk which covers Europe, Africa and part of South America. The resulting AOT, a Level 2 product at the same temporal and spatial resolution than SEVIRI, is presented and evaluated in this paper. The AOT has been validated using ground based measurements from AERONET, a sun-photometer network, focusing over Europe for 3 months in 2006. The SEVIRI estimates correlate well with the AERONET measurements, r = 0.64, with a slight underestimate, bias = −0.017. The sources of errors are mainly the cloud contamination and the bad estimation of the surface reflectance. The temporal evolutions exhibited by both dataset show very good agreement which allows to conclude that the AOT Level 2 product from SEVIRI can be used to quantify the aerosol content and to monitor its daily evolution with a high temporal frequency. The comparison with daily maps of MODIS AOT level 3 product shows qualitative good agreements in the retrieved geographic patterns of AOT. Given the high spatial and temporal resolutions obtained with this approach, our results have clear potential for applications ranging from air quality monitoring to climate studies.

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Schulz, H. M., B. Thies, J. Cermak, and J. Bendix. "1 km fog and low stratus detection using pan-sharpened MSG SEVIRI data." Atmospheric Measurement Techniques 5, no. 10 (October 22, 2012): 2469–80. http://dx.doi.org/10.5194/amt-5-2469-2012.

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Abstract. In this paper a new technique for the detection of fog and low stratus in 1 km resolution from MSG SEVIRI data is presented. The method relies on the pan-sharpening of 3 km narrow-band channels using the 1 km high-resolution visible (HRV) channel. As solar and thermal channels had to be sharpened for the technique, a new approach based on an existing pan-sharpening method was developed using local regressions. A fog and low stratus detection scheme originally developed for 3 km SEVIRI data was used as the basis to derive 1 km resolution fog and low stratus masks from the sharpened channels. The sharpened channels and the fog and low stratus masks based on them were evaluated visually and by various statistical measures. The sharpened channels deviate only slightly from reference images regarding their pixel values as well as spatial features. The 1 km fog and low stratus masks are therefore deemed of high quality. They contain many details, especially where fog is restricted by complex terrain in its extent, that cannot be detected in the 3 km resolution.

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Journal articles on the topic 'MSG-SEVIRI'

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