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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.
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.
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.
This database expands the Poulton et al., 2018 (doi:10.1594/PANGAEA.888182) database of pelagic calcium carbonate (CP) rate measurements from isotopic tracer uptake in incubated discrete water samples, as discussed in Daniels et al., 2018 (doi:10.5194/essd-10-1859-2018), and accompanies Marsh et al. (in prep.). The database now includes more CP (new data n = 400; complete database n = 3165), net primary production rate (PP) (new data n = 399; complete database n = 3150), total coccolithophore cell counts (new data n = 240; complete database n = 1512), and Emiliania huxleyi cell counts (new data n = 27; complete database n = 612). This expanded database maintains the record of data, including the principal investigator, expedition, OS region, doi reference (where available), collection date and year, sample ID, latitude, longitude, sampling and light depth, and method of measuring CP. We further expand the Poulton et al. (2018) data collection by including ancillary and environmental data, including: optical depth (OD, n = 3165), pHtotal (hereinafter referred to as pHT, n = 398), temperature (n = 1160), salinity (n = 1161), and the concentrations of chlorophyll a (n = 1363), NOx (NO3 or the sum of NO3 + NO2, n = 1161), silicic acid (Si(OH)4, n= 1156), phosphate (PO4, n = 1232), dissolved inorganic carbon (DIC, n = 318), total alkalinity (TA, n = 307), bicarbonate ion concentration (n = 349), and carbonate ion concentration (n = 352). All data was matched to CP, sample bottle identifiers (Niskin bottle numbers), and/or sampling depth values. This global database (81 °N - 64 °S, 132 °E - 174 °W) now covers expeditions and upper ocean measurements (0 - 193 m) from 1989 to 2024. Global in-situ geolocated data spanning time is valuable for modelling, satellite algorithms, and capturing calcium carbonate production in the global ocean. This expanded database, including the environmental, nutrient, chlorophyll a, and carbonate chemistry data, also allows for analysis of factors influencing calcium carbonate production on a global scale. This data amalgamation contributes to understanding the biogeochemistry of the oceans, global carbon cycle, and ocean acidification.
The geomagnetic field shields our habitat against solar wind and radiation from space. Due to the geometry of the field, the shielding in general is weakest at high latitudes. It is also anomalously weak in a region around the south Atlantic known as South Atlantic Anomaly (SAA), and the global dipole moment has been decreasing by nearly 10 percent since direct measurements of field intensity became possible in 1832. Due to our limited understanding of the geodynamo processes in Earths core, it is impossible to reliably predict the future evolution of both dipole moment and SAA over the coming decades. However, lack of magnetic field shielding as would be a consequence of further weakening of dipole moment and SAA region field intensity would cause increasing problems for modern technology, in particular satellites, which are vulnerable to radiation damage. A better understanding of the underlying processes is required to estimate the future development of magnetic field characteristics. The study of the past evolution of such characteristics based on historical, archeo- and paleomagnetic data, on time-scales of centuries to millennia, is essential to detect any recurrences and periodicities and provide new insights in dynamo processes in comparison to or in combination with numerical dynamo simulations. We propose to develop two new global spherical harmonic geomagnetic field models, spanning 1 and 10 kyrs, respectively, and designed in particular to study how long the uninterrupted decay of the dipole moment has been going on prior to 1832, and if the SAA is a recurring structure of the field.We will combine for the first time all available historical and archeomagnetic data, both directions and intensities, in a spherical harmonic model spanning the past 1000 years. Existing modelling methods will be adapted accordingly, and existing data bases will be complemented with newly published data. We will further acquire some new archeomagnetic data from the Cape Verde islands from historical times to better constrain the early evolution of the present-day SAA. In order to study the long-term field evolution and possible recurrences of similar weak field structures in this region, we will produce new paleomagnetic records from available marine sediment cores off the coasts of West Africa, Brazil and Chile. This region is weakly constrained in previous millennial scale models. Apart from our main aim to gain better insights into the previous evolution of dipole moment and SAA, the models will be used to study relations between dipole and non-dipole field contributions, hemispheric symmetries and large-scale flux patterns at the core-mantle boundary. These observational findings will provide new insights into geodynamo processes when compared with numerical dynamo simulation results.Moreover, the models can be used to estimate past geomagnetic shielding above Earths surface against solar wind and for nuclide production from galactic cosmic rays.
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.
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/
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/
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 total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region (425-450 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/
Ziel: Auswertemodelle fuer multispektrale Aufnahmen zur Erkennung von Umwelteinfluessen. Vorgehensweise: Multispektrale und thermale Aufnahmen von verschiedenen Testgebieten durch Flugzeug und Satellit. Anwendung verschiedener statistischer Verfahren zur Charakterisierung verschiedener Phaenomene. Darstellung in Diagramm und Bild (Karte). Beobachtung von Phaenomenen durch Langzeitaufnahmen.
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