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Sentinel-5P TROPOMI - Aerosol Optical Depth (AOD), Level 3 - Global

Aerosol optical depth (AOD) as derived from TROPOMI observations. AOD describes the attenuation of the transmitted radiant power by the absence of aerosols. Attenuation can be caused by absorption and/or scattering. AOD is the primary parameter to evaluate the impact of aerosols on weather and climate. Daily AOD observations are binned onto a regular latitude-longitude grid. The TROPOMI instrument onboard the Copernicus SENTINEL-5 Precursor satellite is a nadir-viewing, imaging spectrometer that provides global measurements of atmospheric properties and constituents on a daily basis. It is contributing to monitoring air quality and climate, providing critical information to services and decision makers. The instrument uses passive remote sensing techniques by measuring the top of atmosphere solar radiation reflected by and radiated from the earth and its atmosphere. The four spectrometers of TROPOMI cover the ultraviolet (UV), visible (VIS), Near Infra-Red (NIR) and Short Wavelength Infra-Red (SWIR) domains of the electromagnetic spectrum. The operational trace gas products generated at DLR on behave ESA are: Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Formaldehyde (HCHO), Carbon Monoxide (CO) and Methane (CH4), together with clouds and aerosol properties. This product is created in the scope of the project INPULS. It develops (a) innovative retrieval algorithms and processors for the generation of value-added products from the atmospheric Copernicus missions Sentinel-5 Precursor, Sentinel-4, and Sentinel-5, (b) cloud-based (re)processing systems, (c) improved data discovery and access technologies as well as server-side analytics for the users, and (d) data visualization services.

A spatially explicit Global Reef Island Database (GRID) that captures distribution, diversity and relative vulnerability of the world's low-lying reef islands

Low-lying coral reef islands harbour a distinct, yet highly threatened biological and cultural diversity that is increasingly exposed to climate change impacts. The combination of low elevation, small size, sensitivity to changes in boundary conditions (sea level, waves and currents, locally generated sediment supply) and at some locations high population densities, is why low-lying reef islands (LRIs) are considered among the most vulnerable environments on Earth to climate change. To date, their global distribution and influence of climatic, oceanographic, and geologic setting are only poorly documented or restricted to smaller scales. Here, I present the first detailed global analysis of LRIs utilising freely available global datasets to produce a global reef island database (GRID) and associated intrinsic and extrinsic characteristics that can be used within a coastal vulnerability index (CVI). All datasets used to create the GRID were released between 30 November 2015 and 3 August 2023, while the current version of the GRID database was completed in November 2024. When developing the GRID, LRIs are defined as landmasses <30 km² located on or within 1 km of coral reef and with an elevation of <16 m. Development of the GRID required: 1) the creation of a global shoreline vector file containing the geographic distribution of LRIs and 2) the development of a comprehensive global database of LRIs including eight intrinsic and ten extrinsic variables extracted from global datasets. Intrinsic variables include: 1) human populations, 2) island area, 3) island perimeter, 4) mean elevation, 5) island circularity/shape, 6) underlying reef type, 7) geographic isolation and 8) distance to the nearest neighbouring reef island. Extrinsic variables include: 1) mean water depth, 2) standard deviation of mean water depth, 3) mean annual significant wave height, 4) mean annual wave period, 5) mean spring tidal range, 6) relative tidal range, 7) wave-tide regime, 8) relative wave exposure, 9) relative tropical storm exposure and 10) year-2100 projected median sea level rise rate. The GRID was initially derived from version 2.1 of the UNEP-WCMC Global Island Database, a global shoreline vector file based on geometry data from Open Street Map® (OSM) and released in November 2015. The initial vector file was projected using the Mollweide projection, an equal-area pseudo cylindrical map projection chosen for its accurate derivation of area, especially in regions close to the equator, where most LRIs are located. The final GRID contains 34,404 individual LRIs distributed throughout tropical regions of the world's oceans, amassing a total land area of nearly 11,000 km² with approximately 60,740 km of shoreline and housing around 2.6 million people. While intrinsic variables are typically spatially homogenous, LRIs are generally highly spatially clustered throughout the GRID with respect to extrinsic variables. The spatial distribution of LRIs within the GRID was validated using: 1) published data and 2) quantitative accuracy assessments using satellite imagery. Spatial distributions of LRIs captured in the GRID are extremely consistent with those published in the literature (r² = 0.96) and those derived from independent analysis of satellite imagery (r² = 0.94). Finally, the GRID was used to develop an island vulnerability index (IVI) for each LRI on a scale of 0-1 with 0 representing no vulnerability and 1 representing maximum vulnerability. The GRID database is provided as a tab-delimited text file as well as ESRI shapefiles (points and polygons in WGS84 and Mollweide projection) and a comma-separated value file.

Sentinel-5P TROPOMI - Aerosol Single-Scattering Albedo (ASSA), Level 3 - Global

Aerosol single-scattering albedo (ASSA) as derived from TROPOMI observations. ASSA is a measure of how much light is scattered by aerosols compared to how much is absorbed. It is important for understanding the impact of aerosols on climate and radiative forcing. ASSA is unitless; a value of unity implies that extinction is completely due to scattering; conversely, a single-scattering albedo of zero implies that extinction is completely due to absorption. Daily ASSA observations are binned onto a regular latitude-longitude grid. The TROPOMI instrument onboard the Copernicus SENTINEL-5 Precursor satellite is a nadir-viewing, imaging spectrometer that provides global measurements of atmospheric properties and constituents on a daily basis. It is contributing to monitoring air quality and climate, providing critical information to services and decision makers. The instrument uses passive remote sensing techniques by measuring the top of atmosphere solar radiation reflected by and radiated from the earth and its atmosphere. The four spectrometers of TROPOMI cover the ultraviolet (UV), visible (VIS), Near Infra-Red (NIR) and Short Wavelength Infra-Red (SWIR) domains of the electromagnetic spectrum. The operational trace gas products generated at DLR on behave ESA are: Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Formaldehyde (HCHO), Carbon Monoxide (CO) and Methane (CH4), together with clouds and aerosol properties. This product is created in the scope of the project INPULS. It develops (a) innovative retrieval algorithms and processors for the generation of value-added products from the atmospheric Copernicus missions Sentinel-5 Precursor, Sentinel-4, and Sentinel-5, (b) cloud-based (re)processing systems, (c) improved data discovery and access technologies as well as server-side analytics for the users, and (d) data visualization services.

Sentinel-5P TROPOMI – Aerosol Index (AI), Level 3 – Global

Aerosol Index (AI) as derived from TROPOMI observations. AI is an indicator for episodic aerosol plumes from dust outbreaks, volcanic ash, and biomass burning. The TROPOMI instrument onboard the Copernicus SENTINEL-5 Precursor satellite is a nadir-viewing, imaging spectrometer that provides global measurements of atmospheric properties and constituents on a daily basis. It is contributing to monitoring air quality and climate, providing critical information to services and decision makers. The instrument uses passive remote sensing techniques by measuring the top of atmosphere solar radiation reflected by and radiated from the earth and its atmosphere. The four spectrometers of TROPOMI cover the ultraviolet (UV), visible (VIS), Near Infra-Red (NIR) and Short Wavelength Infra-Red (SWIR) domains of the electromagnetic spectrum. The operational trace gas products generated at DLR on behave ESA are: Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Formaldehyde (HCHO), Carbon Monoxide (CO) and Methane (CH4), together with clouds and aerosol properties. This product is created in the scope of the project INPULS. It develops (a) innovative retrieval algorithms and processors for the generation of value-added products from the atmospheric Copernicus missions Sentinel-5 Precursor, Sentinel-4, and Sentinel-5, (b) cloud-based (re)processing systems, (c) improved data discovery and access technologies as well as server-side analytics for the users, and (d) data visualization services.

Schwerpunktprogramm (SPP) 1158: Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; Bereich Infrastruktur - Antarktisforschung mit vergleichenden Untersuchungen in arktischen Eisgebieten, Variation der antarktischen Wolkenkondensationskern- (CCN) und Eiskeim- (INP) Konzentrationen und Eigenschaften an NEumayer III im Vergleich zu deren Werten in der Arktis an der Forschungsstation Villum (VACCINE+)

Das aktuelle Klima der Erde verändert sich schneller, als von den meisten wissenschaftlichen Prognosen vorhergesagt wurde. Dabei erwärmen sich die Polargebiete schnellsten von allen Regionen der Erde. Die Polargebiete haben auch starke globale Auswirkungen auf das Erdklima und beeinflussen daher das Leben und die Lebensgrundlagen auf der ganzen Welt. Trotz der großen Fortschritte der Polarforschung der letzten Jahre gibt es nach wie vor schlecht verstandene Prozesse; einer davon ist die Aerosol-Wolke-Klima-Wechselwirkung, die daher auch nicht zufriedenstellend modelliert werden können. Wolken und deren Wechselwirkungen im Klimasystem sind eine der schwierigsten Komponenten bei der Modellierung, insbesondere in den Polarregionen, da es dort besonders schwierig ist, qualitativ hochwertige Messungen zu erhalten. Die Verfügbarkeit hochwertiger Messungen ist daher von entscheidender Bedeutung, um die zugrunde liegenden Prozesse zu verstehen und in Modelle integrieren zu können. Im ersten Teil des hier vorgeschlagenen Projekts schlagen wir, d.h. TROPOS, vor, die bestehenden Aerosolmessungen an der Neumayer III-Station um in-situ Wolkenkondensationskern- (CCN) und Eiskeim- (INP) Messungen zu erweitern für einen Zeitraum von fast zwei Jahren. Die erfassten Daten wie Anzahl der Konzentrationen, Hygroskopizität, INP-Gefrierspektren usw. werden mit meteorologischen Informationen (z.B. Rückwärtstrajektorien) und Informationen über die chemische Zusammensetzung der vorherrschenden Aerosolpartikel verknüpft, um Quellen für INP und CCN über den gesamten Jahreszyklus zu identifizieren. In einem optionalen dritten Jahr wollen wir die Ergebnisse der südlichen Hemisphäre mit den TROPOS-Langzeitmessungen des CCN und INP aus der Arktis (Villum Research Station) vergleichen, welche uns im Rahmen dieses Projekts von DFG-finanzierten TR 172, AC3, Projekt B04 zur Verfügung stehen werden. Ein Ergebnis des beantragten Projekts wird ein tieferes Verständnis dafür sein, welche Prozesse die CCN- und INP-Population in hohen Breiten dominieren. Die im Rahmen des vorliegenden Projekts gesammelten quantitativen Informationen über CCN und INP in hohen Breiten werden öffentlich zugänglich veröffentlicht, z.B. für die Evaluierung globaler Modelle und Satellitenretrievals.

Kombination der Niederschlagsschätzung von opportunistischen Sensoren und geostationären Satelliten

Der Umsetzungsplan der COP27 enthält eine sehr klare Aussage. "Ein Drittel der Welt, darunter 60% von Afrika, hat keinen Zugang zu Frühwarn- und Klimainformationsdiensten". Dies gilt vor allem für niederschlagsbezogene Warnungen. Der Grund dafür ist das fast vollständige Fehlen von Wetterradaren auf in Afrika und die mangelnde Dichte von Niederschlagsmessstationen. Im Gegensatz dazu sind geostationäre Satelliten (GEOsat) und potentiell auch kommerzielle Richtfunkstrecken (CML) und Satelliten-Mikrowellenverbindungen (SML) nahezu in Echtzeit verfügbar und können zur Niederschlagsschätzung verwendet werden. Die quantitative Niederschlagsschätzung (QPE) aus GEOsat-Daten ist jedoch aufgrund der indirekten Beziehung zwischen der Niederschlagsmenge und den tatsächlichen Messungen, die im sichtbaren und infraroten Spektrum durchgeführt werden, eine Herausforderung. Für die QPE aus SML- und CML-Daten, insbesondere auf der Grundlage groß angelegter CML-Studien in Europa, wurde gezeigt, dass sie mit der QPE aus Radar- und Regenmessern gleichwertig sein kann. In Ermangelung von Referenzdaten, wie es in Entwicklungsländern häufig der Fall ist, sind die bestehenden maßgeschneiderten semi-empirischen Prozessierungsmethoden jedoch oft nicht direkt anwendbar. GEOsat-Daten haben das Potenzial, die CML/SML-Prozessierung in diesen Regionen zu unterstützen, und umgekehrt könnte die CML/SML-QPE zur Anpassung der GEOsat-QPE verwendet werden. Das übergeordnete Ziel des Projekts MERGOSAT ist daher die Entwicklung neuartiger Methoden zur Erstellung verbesserter Echtzeit-Niederschlagskarten für datenarme Regionen durch eine Kombination von GEOsat-Daten und CML/SML-QPE. Um dieses Ziel zu erreichen, werden wir uns auf drei Aspekte konzentrieren: 1) Schaffung einer Grundlage für allgemeinere CML/SML-QPE-Modelle durch Verbesserung des Verständnisses der Prozesse die die EM-Ausbreitung von CML und SML beeinflussen. 2) Entwicklung geeigneter CML/SML-QPE-Modelle, die in datenarmen Regionen anwendbar sind, aufbauend auf den neuen Erkenntnissen über WAA und DSD und unter innovativer Nutzung von GEOsat-Daten. 3) Verbesserung der GEOsat-QPE mit DeepLearning-Methoden und Entwicklung eines neuen Verfahrens, das die Zusammenführung mit CML/SML-Daten mit sub-stündlicher Auflösung ermöglicht. Wir werden unsere Forschung auf unser umfangreiches Archiv von CML-Daten, auch aus Afrika, und die zunehmende Verfügbarkeit von SML-Daten stützen. Zusätzliche Daten aus Feldexperimenten werden mit modernsten Simulationen der EM-Ausbreitung kombiniert. Darüber hinaus werden wir neueste Techniken des DeepLearnings und unsere Hochleistungs-Recheninfrastruktur nutzen. In Kombination mit den erweiterten Fähigkeiten des kürzlich gestarteten MTG GEOsat wird uns dies ermöglichen, unsere Ziele erfolgreich anzugehen und die methodische Grundlage zu schaffen, die erforderlich ist, um datenarme Regionen mit verbesserten und zuverlässigen Niederschlagsinformationen nahezu in Echtzeit zu versorgen.

Dynamic World training dataset for global land use and land cover categorization of satellite imagery

The Dynamic World Training Data is a dataset of over 5 billion pixels of human-labeled ESA Sentinel-2 satellite image, distributed over 24000 tiles collected from all over the world. The dataset is designed to train and validate automated land use and land cover mapping algorithms. The 10m resolution 5.1km-by-5.1km tiles are densely labeled using a ten category classification schema indicating general land use land cover categories. The dataset was created between 2019-08-01 and 2020-02-28, using satellite imagery observations from 2019, with approximately 10% of observations extending back to 2017 in very cloudy regions of the world. This dataset is a component of the National Geographic Society - Google - World Resources Institute Dynamic World project. The dataset consists of two file types: GeoTIFF files of 510x510 pixel 10m resolution satellite image tiles markup provided by human labelers, and Excel (.xlsx) tables of metadata and class statistics for the above GeoTIFF files. The data is organized into three main folders. One folder contains training data labeled by a team of 25 expert human labelers recruited by National Geographic Society specifically for this project. A second folder contains training data labeled by a larger group of commissioned labelers provided by a commercial crowd-labeler service. The data in these folders is organized by hemisphere and biome number from the RESOLVE Ecoregions2017 biomes categories (https://ecoregions2017.appspot.com/). A third folder contains a validation dataset. This is a holdout set of training data for assessing model accuracy. None of this data is intended to be used in the formulation of the model. Each validation tile was independently labeled by three experts. The validation set contains two versions: the individual markup from each expert labeler, and the image composites of the individual markups. Each GeoTIFF file encodes information on the location of landscape feature classes as determined by a given labeler. Classes were labeled by visual examination of true color (RGB) composites of Sentinel-2 MultiSpectral Level-2A scenes. The Tier 1 class values used in this phase of the project are as follows: 0 No data (left unmarked), 1 Water, 2 Trees, 3 Grass, 4 Flooded Vegetation, 5 Crops, 6 Scrub, 7 Built Area, 8 Bare Ground, 9 Snow/Ice, 10 Cloud. This dataset does not include the original Sentinel-2 imagery tiles, but metadata on the exact image ID and date is provided The original Sentinel-2 imagery was obtained via Google Earth Engine. This data is available under a Creative Commons BY-4.0 license and requires the following attribution: This dataset is produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute. Development of the Dynamic World training data was funded in part by the Gordon and Betty Moore Foundation.

Forschergruppe (FOR) 1740: Ein neuer Ansatz für verbesserte Abschätzungen des atlantischen Frischwasserhaushalts und von Frischwassertransporten als Teil des globalen Wasserkreislaufs, The Atmospheric Side of the Freshwater Budget

The focus of this project is to analyse the observed surface freshwater fluxes through improved estimates of evaporation and precipitation and their individual error characteristics in the HOAPS climatology and its ground validation in climate-related hotspots of the Atlantic Ocean. To enable that in a consistent manner we propose to establish an error characterization of the HOAPS evaporation data by triple collocations with ship and buoy measurements and between individual satellites and to improve the error characterization of the HOAPS precipitation by analysing available shipboard disdrometer data using point to area statistics. After these improvements, an analysis of the spatio-temporal variability of the surface fresh water balance E-P over the Atlantic Ocean is planned, especially with respect to the Hadley circulation and the hotspot regions of interest to related WPs. Also the atmospheric water transport shall be analysed in order to find the source or target region of local fresh water imbalances. And finally, a consistent inter-comparison of the upcoming global ocean surface salinity fields from SMOS with freshwater fluxes from the HOAPS climatology is proposed.

METOP GOME-2 - Sulfur Dioxide (SO2) - Global

The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational SO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. GDP 4.x performs a DOAS fit for SO2 slant column followed by an AMF / VCD computation using a single wavelength. Corrections are applied to the slant column for equatorial offset, interference of SO2 and SO2 absorption, and SZA dependence. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/

METOP GOME-2 - Tropospheric Nitrogen Dioxide (NO2) - Global

The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The operational NO2 tropospheric column products are generated using the algorithm GDP (GOME Data Processor) version 4.x for NO2 [Valks et al. (2011)] integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region using the DOAS method. An additional algorithm is applied to derive the tropospheric NO2 column: after subtracting the estimated stratospheric component from the total column, the tropospheric NO2 column is determined using an air mass factor based on monthly climatological NO2 profiles from the MOZART-2 model. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/

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