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Found 203 results.

INSPIRE: German Regional Seismic Network (GRSN)

The German Regional Seismic Network (GRSN) consists of seismological stations equipped with 3-component broadband seismometer and digital data aquisition system. The recorded data are directly transmitted to the data center at BGR Hannover and made available to the public near realtime. The GML file together with a Readme.txt file are provided in ZIP format (GRSN-INSPIRE.zip). The Readme.text file (German/English) contains detailed information on the GML file content. Data transformation was proceeded by using the INSPIRE Solution Pack for FME according to the INSPIRE requirements.

INSPIRE: German Regional Seismic Network (WMS)

The WMS GRSN (INSPIRE) represents the seismological stations of the German Regional Seismic Network (GRSN) equipped with 3-component broadband seismometer and digital data aquisition system. The recorded data are directly transmitted to the data center at BGR in Hannover and made available to the public near realtime. According to the Data Specification on Geology (D2.8.II.4_v3.0, subtopic Geophysics) the information with respect to the seismological stations is INSPIRE-compliant. The WMS GRSN (INSPIRE) contains a layer of the seismological stations (GE.seismologicalStation) displayed correspondingly to the INSPIRE portrayal rules. Via the getFeatureInfo request the user obtains the content of the INSPIRE attributes platformType, relatedNetwork, stationType und stationRank.

METOP GOME-2 - Cloud Top Pressure (CTP) - 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. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud-top pressure for GOME scenes is derived from the cloud-top height provided by ROCINN and an appropriate pressure profile. 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 - Cloud Fraction (CF) - 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. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. 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 - Cloud Optical Thickness (COT) - 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. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. 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/

EMODNET Seafloor Geology (WMS)

The European Marine Observation and Data Network (EMODnet) consists of more than 100 organisations assembling marine data, products and metadata to make these fragmented data resources more available to public and private users relying on quality-assured, standardised and harmonised marine data which are interoperable and free of restrictions on use. EMODnet is currently in its fourth phase. BGR participates in the EMODnet Geology theme and is coordinating the “seafloor geology” work package from the beginning. In cooperation with the project partners BGR compiles and harmonises GIS data layers on the topics geomorphology, pre-Quaternary and Quaternary geology and provides those, based on INSPIRE principles, via the EMODnet Geology portal https://www. emodnet-geology.eu/map-viewer/. These map layers present the pre-Quaternary and Quaternary sea-floor geology and Geomorphology of the European Seas, semantically harmonized based on the INSPIRE data specifications including the terms for lithology, age, event environment, event process and geomorphology. The data are compiled from the project partners, the national geological survey organizations of the participating countries. The data set represents the most detailed available data compilation of the European Seas using a multiresolution approach. Data completeness depending on the availability of data and actual mapping campaigns. This open and freely accessible product was made available by the EMODnet Geology project (https://www.emodnet-geology.eu/), implemented by EMODnet Geology Phase IV partners, and funded by the European Commission Directorate General for Maritime Affairs and Fisheries. These data were compiled by BGR from the EMODnet IV Geology partners. All ownership rights of the original data remain with the data originators, who are acknowledged within the attribute values of each map feature.

Air Quality 2022

This evaluation of air quality in Germany in the year 2022 is based on preliminary data which has not yet been conclusively audited from the air monitoring networks of the federal states and the ⁠ UBA ⁠, valid on 31th January 2023. Due to the comprehensive quality assurance within the monitoring networks, the final data will only be available in mid-2023. The currently available data allows for a general assessment of the past year. The following pollutants were subject to consideration: particulate matter (⁠ PM10 ⁠ and PM2.5), nitrogen dioxide (NO2) and ozone (O3). The evaluation and assessment of the air quality takes place in terms of the limit and target values as well as the air quality guideline (AQG) levels of the World Health Organization. Veröffentlicht in Hintergrundpapier.

Air Quality 2017

This evaluation of air quality in Germany in the year 2017 is based on preliminary data which has not yet been conclusively audited from the air monitoring networks of the federal states and the ⁠ UBA ⁠, valid on 23rd January 2018. Due to the comprehensive quality assurance within the monitoring networks, the final data will only be available in mid-2018. The currently available data allows for a general assessment of the past year. The following pollutants were subject to consideration: particulate matter (⁠ PM10 ⁠ and PM2.5), nitrogen dioxide (NO2) and ozone (O3), since, the limit and target values for the protection of human health are still exceeded for such substances. Veröffentlicht in Hintergrundpapier.

Air Quality 2016

This evaluation of air quality in Germany in the year 2016 is based on preliminary data which has not yet been conclusively audited from the air monitoring networks of the federal states and the ⁠ UBA ⁠, valid on 23rd January 2017. Due to the comprehensive quality assurance within the monitoring networks, the final data will only be available in mid-2017. The currently available data allows for a general assessment of the past year. The following pollutants were subject to consideration: particulate matter (⁠ PM10 ⁠ and PM2.5), nitrogen dioxide (NO2) and ozone (O3), since, the limit and target values for the protection of human health are still exceeded for such substances. Veröffentlicht in Hintergrundpapier.

Air Quality 2018

This evaluation of air quality in Germany in the year 2018 is based on preliminary data which has not yet been conclusively audited from the air monitoring networks of the federal states and the ⁠ UBA ⁠, valid on 18rd January 2019. Due to the comprehensive quality assurance within the monitoring networks, the final data will only be available in mid-2019. The currently available data allows for a general assessment of the past year. The following pollutants were subject to consideration: particulate matter (⁠ PM10 ⁠ and PM2.5), nitrogen dioxide (NO2) and ozone (O3), since, the limit and target values for the protection of human health are still exceeded for such substances. Veröffentlicht in Hintergrundpapier.

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