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Gemeinsame nationale Initiative zur Validierung von EarthCARE, Teilvorhaben LMU München

Gemeinsame nationale Initiative zur Validierung von EarthCARE, Teilvorhaben Freie Universität Berlin

Sentinel-5P TROPOMI Surface Nitrogendioxide (NO2), Level 4 – Regional (Germany and neighboring countries)

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 displays the Nitrogen Dioxide (NO2) near surface concentration for Germany and neighboring countries as derived from the POLYPHEMUS/DLR air quality model. Surface NO2 is mainly generated by anthropogenic sources, e.g. transport and industry. POLYPHEMUS/DLR is a state-of-the-art air quality model taking into consideration - meteorological conditions, - photochemistry, - anthropogenic and natural (biogenic) emissions, - TROPOMI NO2 observations for data assimilation. This Level 4 air quality product (surface NO2 at 15:00 UTC) is based on innovative algorithms, processors, data assimilation schemes and operational processing and dissemination chain developed in the framework of the INPULS project. The DLR project INPULS 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 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.

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.

Statistical-dynamical methods for scale dependent model evaluation and short term precipitation forecasting (STAMPF)

Das Ziel des Projektes ist die skalenabhängige Evaluierung von Niederschlagsprognosen der DWD-Modellkette (LM/GME) bezüglich dynamischer Parameter und Wolkeneigenschaften. Ein neu entwickelter dynamischer Zustandsindex (DSI), die mit der spezifischen Feuchte gewichtete Divergenz sowie Wolkentyp, Bedeckung und Höhe der Wolkenobergrenze sind die Evaluierungsparamater. Der DSI wurde aus den ursprünglichen Gleichungen abgeleitet und beschreibt die Abweichungen von einem verallgemeinerten dynamischen Gleichgewicht, verursacht durch Instationarität und diabatische Prozesse. Die Evaluierung konzentriert sich auf die Wechselwirkungen zwischen der synoptischen und konvektiven Skala, die häufig die Ursache für extreme Niederschlagsereignisse sind. Sie untersucht die Beziehung zwischen den synoptisch-skaligen Prozessen und der konvektiven Parameterisierung. Eine Voraussetzung der Evaluierung ist eine vom Modell unabhängige feldmäßige Analyse des täglichen Niederschlages und der Wolkenparameter in der Gitterauflösung des LM/GME. Ein schon existierendes Analyseschema der synoptischen Beobachtungen wird weiter verbessert und erweitert durch Satellitendaten. Diese liefern kontinuierliche Wolkendaten und Niederschlagsraten. Die Genauigkeit der analysierten Felder wird mit Hilfe moderner statistischer Methoden abgeschätzt. In einem weiteren Schritt werden die getesteten dynamischen Parameter zu einer quasi-prognostischen Niederschlagsvorhersage oder als Prediktoren für einen MOS-Ansatz verwendet.

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 - Water Vapour (H2O) - 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 H2O 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 total H2O column is retrieved from GOME solar backscattered measurements in the red wavelength region (614-683.2 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method. 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/

Collection of data sets for the Clouds over cOMPlEX environment - EarthCARE (COMPEX-EC) campaign, carried out in Kiruna in spring 2025

This data collection unites the individual data sets of the COMPEX-EC (Clouds over cOMPlEX environment - EarthCARE) campaign, carried out in Kiruna 2.-16.4.2025. COMPEX-EC has been designed as an EarthCARE validation campaign. For that purpose, Polar 5 (C-GAWI) has been equipped with instrumentation similar to the one operated on EarthCARE (W-band radar, lidar, radiometers, spectral imagers). Seven research flights (summing up to more than 30 flight hours) were conducted each of them underflying the EarthCARE satellite to validate its performance.

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Cottbus, Germany from 1984 – 2023

The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.

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