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18 results for “SEVIRI”
MSG SEVIRI NDVI dataset for the Horn of Africa 2005-2023
<p>Dataset related to the paper "A high temporal resolution NDVI time series to monitor drought events in the Horn of Africa". The dataset does not contain the bias correction explained in the paper but be can easily applied using the provided formula. The dataset is for the countries Kenya, Ethiopia, Djibouti and Somalia.</p>
Hourly non-gridded volcanic ash properties retrieved from SEVIRI measurements for the Eyjafjallajökull 2010 eruption
<p>- Publishing date:<br> 14.05.2020</p> <p>- Title:<br> Hourly non-gridded volcanic ash properties retrieved from SEVIRI<br> measurements for the Eyjafjallajökull 2010 eruption </p> <p>- Authors of data set:<br> Arve Kylling (aky@nilu.no), NILU - Norwegian Institute for Air Research<br> Espen Sollum, NILU - Norwegian Institute for Air Research</p> <p>- Description:<br> Ash satellite detection and retrievals were made using infrared<br> measurements by SEVIRI on board the MSG-2 satellite. MSG-2 is<br> geostationary, centred at approximately 0N latitude, and has a 70<br> degree view coverage (Schmetz et al., 2002). Pixel resolution is 3 ×<br> 3 km at nadir, while at the edge of the coverage it increases to 10<br> × 10 km. Observations are available every 15 min. Pixels are<br> identified as containing ash if the brightness temperature<br> difference (BTD) between the SEVIRI 10.8 and 12.0 μm channels<br> (Prata, 1989) is below a certain threshold value, here −0.5 K. The<br> BTDs have been adjusted for water vapour absorption using the approach of<br> Yu et al. (2002). Ash clouds give negative BTDs, ice give positive<br> BTDs, and BTDs of water clouds are closer to zero. The ash mass<br> loading and effective ash particle radius are retrieved as described<br> in Kylling et al. (2015). The retrieval is based on a modification<br> of the Bayesian optimal estimation technique used by Francis et<br> al. (2012). We assume andesite ash with refractive index from Pollack<br> et al. (1973), spherical ash particles, and a lognormal size<br> distribution. The lognormal size distribution is described by the<br> geometric mean radius and the geometric standard deviation. The data<br> set includes retrievals for geometric standard deviation of 1.5,<br> 1.75, 2.0, and 2.25, which is a subset of the values used by Francis<br> et al. (2012). The data set has been used by Steensen et al. (2017).</p> <p> Data comes as hourly files broadly covering Iceland, Europe and the<br> surrounding oceans. The files are in bzip2 netcdf-format which<br> should be self-explanatory. </p> <p>- Version:<br> 1.0</p> <p>- Language:<br> English</p> <p>- Keywords<br> Volcanic ash, remote sensing, SEVIRI, Eyjafjallajökull 2010</p> <p>- Additional notes<br> None</p> <p>- Access right:<br> Open access</p> <p>- License:<br> CC BY-SA 4.0 </p> <p>- Funding:<br> Partly funded by the Norwegian ash project financed by the Norwegian<br> Ministry of Transport and Communications and Avinor. </p> <p>- References:<br> Francis, P. N., Cooke, M. C., and Saunders, R.W.: Retrieval of<br> physical properties of volcanic ash using Meteosat: A case study<br> from the 2010 Eyjafjallajokull eruption, J. Geophys. Res. Atmos.,<br> 117, D00U09, https://doi.org/10.1029/2011JD016788, 2012.</p> <p> Kylling, A., Kristiansen, N., Stohl, A., Buras-Schnell, R., Emde,<br> C., and Gasteiger, J.: A model sensitivity study of the impact of<br> clouds on satellite detection and retrieval of volcanic ash, Atmos. <br> Meas. Tech., 8, 1935-1949, https://doi.org/10.5194/amt-8-1935-<br> 2015, 2015.<br> <br> Pollack, J. B., Toon, O. B., and Khare, B. N.: Optical properties of<br> some terrestrial rocks and glasses, Icarus, 19, 372-389,<br> https://doi.org/10.1016/0019-1035(73)90115-2, 1973. </p> <p> Prata, A. J.: Observations of volcanic ash clouds in the 10-12 um<br> window using AVHRR/2 data, Int. J. Remote Sens., 10, 751-761,<br> 1989.</p> <p> Schmetz, J., Pili, P., Tjemkes, S., and Just, D.: An introduction to<br> Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83,<br> 977-992, 2002.<br> <br> Steensen, B. M., Kylling, A., Kristiansen, N. I., and Schulz, M.:<br> Uncertainty assessment and applicability of an inversion method for<br> volcanic ash forecasting, Atmos. Chem. Phys., 17, 9205-9222,<br> https://doi.org/10.5194/acp-17-9205-2017, 2017. </p> <p> Yu, T., Rose, W. I., and Prata, A. J.: Atmospheric correction for<br> satellite-based volcanic ash mapping and retrievals using "split<br> window" IR data from GOES and AVHRR, J. Geophys. Res. Atmos., 107,<br> https://doi.org/10.1029/2001JD000706, 2002. </p>
Outputs of the Jupyter Notebook - SEVIRI Level 1.5
<p>The dataset contains the outputs of the notebook "SEVIRI Level 1.5" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samuel Jackson (author), Science & Technology Facilities Council, <a href="https://github.com/samueljackson92">@samueljackson92</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a>, 18/01/22 (latest revision)</p> </li> </ul> <p><em>Dataset originator/creator</em></p> <p>SEVIRI Level 1.5 Image Data - MSG - 0 degree</p> <ul> <li> <p>European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)</p> </li> </ul> <p>FRPPIXEL</p> <ul> <li> <p>Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)</p> </li> </ul> <p><em>Dataset authors</em></p> <p>SEVIRI Level 1.5 Image Data - MSG - 0 degree</p> <ul> <li> <p>European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)</p> </li> </ul> <p>FRPPIXEL</p> <ul> <li> <p>Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Martin Wooster, Jiangping He, Weidong Xu, and Alessio Lattanzio. Frp - product user manual. URL: <a href="https://nextcloud.lsasvcs.ipma.pt/s/pnDEepeq8zqRyrq">https://nextcloud.lsasvcs.ipma.pt/s/pnDEepeq8zqRyrq</a> (visited on 2021-11-18).</p> </li> <li> <p>MJ Wooster, G Roberts, PH Freeborn, W Xu, Y Govaerts, R Beeby, J He, A Lattanzio, D Fisher, and R Mullen. Lsa saf meteosat frp products–part 1: algorithms, product contents, and analysis. <em>Atmospheric Chemistry and Physics</em>, 15(22):13217–13239, 2015.</p> </li> </ul>
SEVIRI derived ET data sets
<p>There are two relevant SEVIRI derived ET data sets distributed by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF) (<a href="https://landsaf.ipma.pt/en/data/">https://landsaf.ipma.pt/en/data/</a>; last access: 1 December 2021); (i) SEVIRI- actual ET (SEVIRI-ET<sub>a</sub>) that accounts for the water evaporated from the soil surface, vegetation canopy and water bodies, and (ii) SEVIRI- ‎reference ET (SEVIRI-ET<sub>0</sub>) that accounts for the (local) atmospheric demand and corresponds to the ET from a hypothetical well-watered green grass having 12 cm height and 0.23 albedo under the given down-welling short-wave radiation (Allen et al., 1998). These data sets are being provided in near real-time through the LSA SAF operational system and we used them in the GEOEssential project for water stress workflow development. For more information, please contact Dr. Bagher Bayat (bagher.bayat@gmail.com and b.bayat@fz-juelich.de).</p>
SEVIRI derived ET data sets V2
<p>This data set is SEVIRI derived ET distributed by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF) (<a href="https://landsaf.ipma.pt/en/data/">https://landsaf.ipma.pt/en/data/</a>; last access: 1 December 2021). There are two relevant SEVIRI derived ET data sets (i) SEVIRI- actual ET (SEVIRI-ET<sub>a</sub>) that accounts for the water evaporated from the soil surface, vegetation canopy and water bodies, and (ii) SEVIRI- ‎reference ET (SEVIRI-ET<sub>0</sub>) that accounts for the (local) atmospheric demand and corresponds to the ET from a hypothetical well-watered green grass having 12 cm height and 0.23 albedo under the given down-welling short-wave radiation (Allen et al., 1998). These data sets are being provided in near real-time through the LSA SAF operational system and we used them in the GEOEssential project for water stress workflow development. For more information, please contact Dr. Bagher Bayat (bagher.bayat@gmail.com and b.bayat@fz-juelich.de).</p>
SEVIRI AOD/COD Product [DUST2MSG]
<p>In the framework of the DUST2MSG ESA project, the CISAR algorithm, originally developed by Rayference for the joint retrieval of surface reflectance, aerosol and cloud single scattering properties, has been applied to SEVIRI observations acquired from both MSG1 and MSG4 from March to August 2020 to obtain a dust product. This Dataset includes hourly atmospheric products. Specification on the filename convention, format and content of the products can be found on the Product User Manual (PUM)</p>
Supplementary Information to: Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high–resolution visible channel: Evaluation of candidate approaches with MODIS observations
<p>This repository contains the Python programmes and datasets used for</p> <p>the research described in the following paper:<br> https://doi.org/10.5194/amt-2019-334</p> <p>It has been prepared as supplementary information to the final paper.<br> <br> Note that the actual Cloud Property Retrieval (CPP) which would be<br> required for full reproducability of the results cannot be made<br> available by the authors, that the code included here is based on Python2, and that<br> paths to the dataset have to be adapted in the code.<br> <br> The repository consists of three separate parts/directories:<br> <br> * method: Python routines for generating the cloud property retrieval<br> input based on the Meteosat and MODIS data<br> <br> * analysis: Python routines for analysing the different experiments<br> from the CPP outputs, producing the figures and calculating the<br> comparison statistics<br> <br> * datasets: the various input and output datasets used for the<br> analyses of the paper</p>
Meteosat SEVIRI level-1 files and NWCSAF/Geo cloud products (v2013 and v2016)
<p>Meteosat-11 (MSG4) SEVIRI level-1 HRIT files and NWCSAF/Geo cloud products (v2013 and v2016)</p> <p>The v2013 products have been derived at SMHI, and the v2016 products have been derived at EUMETSAT and disseminated over EUMETCast</p>
Meteosat 11 SEVIRI lvl 1.5, HRIT, 201802281500
<p>Meteosat 11 SEVIRI lvl 1.5, HRIT, 201802281500</p>
GHRSST L3C hourly Atlantic reprocessed sub-skin sea surface temperature data record v1.0 from SEVIRI on MSG produced by OSISAF
A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the MSG satellites (Meteosat-8 and Meteosat-9). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) has reprocessed SST products in (long) delayed-mode from MSG/SEVIRI. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm and the cloud mask (CM) from OSI SAF. Atmospheric profiles of water vapor and temperature from a numerical weather prediction (NWP) model, OSTIA Sea Surface Temperature re-analysis and analysis, together with a radiative transfer model (RTTOV), are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15-minute slot is processed at full satellite resolution. The products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all available 15-minute SST data into hourly files with priority being given to the value closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.
NOAA GHRSST Level 2P Atlantic Ocean Regional Skin Sea Surface Temperature v1.0 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation-4 (MSG-4) satellite
The GHRSST L2P MSG04 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-11 (MSG4) satellite. It provides the full disk SEVIRI imagery covering the Atlantic Ocean region from its position at 0.0°E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. On Feb. 2, 2018 the Meteosat-11 (MSG4) took over the Meteosat-10 (MSG3) (MSG03-OSPO-L2P-v1.0) and produced the L2P SST data from Sept 10. 2018 to March 24, 2023. In March 2023, Meteosat-10 and Meteosat-11 were swapped roles and orbital positions. The MSG03 has started to produce the L2P SST data again over the Atlantic Ocean region. Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors. <br><br>The SST measurements from SEVIRI are parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977. <br><br>This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.
NOAA GHRSST Level 2P Indian Ocean Regional Skin Sea Surface Temperature v1.0 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation-1 (MSG-1) satellite
The GHRSST L2P MSG01 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-8 (MSG1) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5°E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. The full data records stretch from Sept. 18, 2018 to June 1, 2022. After June 1, 2022, the Meteosat-9 (MSG2) took over as the prime geostationary satellite for the Indian Ocean region (MSG02-OSPO-L2P-v1.0). Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors. <br><br>The SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977. <br><br>This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.
GHRSST Level 3C Indian-Ocean (IO) sub-skin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG in GDS2 format produced by OSISAF
This dataset is produced by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument onboard the Meteosat Second Generation (MSG-1), Meteosat-8 satellite (launched on 28 August 2002). The dataset covers the Indian Ocean region with latitude of 60S-60N and longitude of 101.5E-18.5W. Level-3C SST, in the NetCDF format recommended by Group for High Resolution Sea Surface Temperature (GHRSST), is identical to Level-2P GHRSST products, 3 refers to gridded products and C to the fact that hourly products result from compositing 15 minutes (MSG) or 30 minutes (GOES-E) data.The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), OSI SAF is producing SST products in near real time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating all 15-minute SST data available in one-hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.
NOAA GHRSST Level 2P Indian Ocean Regional Skin Sea Surface Temperature v1.0 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation-2 (MSG-2) satellite
The GHRSST L2P MSG02 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-9 (MSG2) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5°E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. On June 1, 2022, the Meteosat-9 (MSG2) replaced the Meteosat-8 (MSG1) (MSG01-OSPO-L2P-v1.0) and produced the L2P SST data from June 11. 2022 to the present. This dataset will be updated every 15 minutes as a forward data stream with 3-24 hours nominal latency. Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors.<br><br>The SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977. <br><br>This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.
GHRSST Level 3C Atlantic sub-skin Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG at 0 degree longitude (GDS V2) produced by OSI SAF
A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG-3) satellites (launched 5 July 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre deMeteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrievedfrom the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm.Atmospheric profiles of water vapor and temperature from a numerical weather prediction model,together with a radiatiave transfer model, are used to correct the multispectral algorithm forregional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot isprocessed at full satellite resolution. The operational products are then produced by remappingover a 0.05 degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all 15minute SST data available in one hour time, and the priority being given to the value the closest intime to the product nominal hour. The product format is compliant with the GHRSST DataSpecification (GDS) version 2.
GHRSST Level 2P Atlantic Regional Skin Sea Surface Temperature from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG-3) satellite (GDS version 2)
The Meteosat Second Generation (MSG-3) satellites are spin stabilized geostationary satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) to provide accurate weather monitoring data through its primary instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels. Eight of these channels are in the thermal infrared, providing among other information, observations of the temperatures of clouds, land and sea surfaces at approximately 5 km resolution with a 15 minute duty cycle. This Group for High Resolution Sea Surface Temperature (GHRSST) dataset produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) is derived from the SEVIRI instrument on the second MSG satellite (also known as Meteosat-9) that was launched on 22 December 2005. Skin sea surface temperature (SST) data are calculated from the infrared channels of SEVIRI at full resolution every 15 minutes. L2P data products with Single Sensor Error Statistics (SSES) are then derived following the GHRSST-PP Data Processing Specification (GDS) version 2.0.
Cloud-CCI+ SEVIRI CRE case study dataset
<p>A dataset of tracked DCC cores and anvils over sub-Saharan Africa (approximately 18°W-46°E, 31°S-15°N) for four months (May 2016 to August 2016). The dataset tracks the cloud properties and their cloud radiative effect over the lifetime of each DCC.</p> <p>The files contained within <em>seviri_labels_* </em>contain daily data of tracked DCCs on the SEVIRI grid with spatial masks corresponding to each detected anvil and core at each time step.</p> <p>The files contained within <em>seviri_statistics</em> contain the properties of each DCC core and anvil at each timestep along their lifetime. Each file contains all DCCs tracked within one month.</p> <p>This dataset accompanies the article <em>"A Lagrangian Perspective on the Lifecycle and Cloud Radiative Effect of Deep Convective Clouds Over Africa" </em>by William k. Jones, Martin Stengel and Philip Stier, which is to be submitted to ACP</p>
HRIT SEVIRI data 12:30 UTC, January 28, 2017
<p>HRIT Meteosat-10 SEVIRI data received at SMHI 12:30 UTC, January 28, 2017. Full disk.</p> <p>Copyright EUMETSAT</p>
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