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Multibeam bathymetry processed data (EM 1002 echosounder entire dataset) of RV MARIA S. MERIAN during cruise MSM62/2

Swath sonar bathymetry data used for that dataset was recorded during RV MARIA S. MERIAN cruise MSM62/2 using Kongsberg EM1002 multibeam echosounder. The cruise took place between 23.03.2017 and 27.03.2017 in the Baltic Sea. The cruise aimed to investigate the impact of the Littorina transgression on the inflow of saline waters into the western Baltic and assessed the potential for future diminution of ventilation in the central and northern deeper basins due to isostatic uplift [CSR]. CI Citation: Paul Wintersteller (seafloor-imaging@marum.de) as responsible party for bathymetry raw data ingest and approval. During the MSM62/2 cruise, the moonpooled KONGSBERG EM1002 multibeam echosounder (MBES) was utilized to perform bathymetric mapping in shallow depths. The echosounder has a curved transducer in which 111 beams are formed for each ping while the seafloor is detected using amplitude and phase information for each beam sounding. For further information on the system, consult https://www.km.kongsberg.com/. Postprocessing and products were conducted by the Seafloor-Imaging & Mapping group of MARUM/FB5, responsible person Paul Wintersteller (seafloor-imaging@marum.de). The open source software MB-System (Caress, D. W., and D. N. Chayes, MB-System: Mapping the Seafloor, https://www.mbari.org/products/research-software/mb-system, 2017) was utilized for this purpose. A sound velocity correction profile was applied to the MSM62/2 data; there were no further corrections for roll, pitch and heave applied during postprocessing. A tide correction was applied, based on the Oregon State University (OSU) tidal prediction software (OTPS) that is retrievable through MB-System. CTD measurements during the cruise were sufficient to represent the changes in the sound velocity throughout the study area. Using Mbeditviz, artefacts were cleaned manually. NetCDF (GMT) grids of the edited data as well as statistics were created with mbgrid. The published bathymetric EM1002 grid of the cruise MSM62/2 has a resolution of 15 m. No total propagated uncertainty (TPU) has been calculated to gather vertical or horizontal accuracy. A higher resolution is, at least partly, achievable. The grid extended with _num represents a raster dataset with the statistical number of beams/depths taken into account to create the depth of the cell. The extended _sd -grid contains the standard deviation for each cell. The DTMs projections are given in Geographic coordinate system Lat/Lon; Geodetic Datum: WGS84.

Monitoring of CO2 emissions from passenger cars, 2023 - Final

The Regulation (EU) No 2019/631 requires Countries to record information for each new passenger car registered in its territory. Every year, each Member State shall submit to the Commission all the information related to their new registrations. In particular, the following details are required for each new passenger car registered: manufacturer name, type approval number, type, variant, version, make and commercial name, specific emissions of CO2 (NEDC and WLTP protocols), masses of the vehicle, wheel base, track width, engine capacity and power, fuel type and mode, eco-innovations and electricity consumption. Data for EU-27 and UK are reported in the main database. Since 2018 Iceland is also included in the database. Since 2019 Norway is also included in the database. For downloading the data in the elastic data viewer please use Edge, Chrome, Firefox or Safari.

Monitoring of CO2 emissions from heavy-duty vehicles, 2025

Regulation (EU) 2018/956 requires EU Member States and manufacturers to report data related to heavy-duty vehicles. Member States report trucks, buses and trailers registered in their territory. Manufacturers report trucks of specific types that are subject to certification requirements. The reporting periods are annual and run from 1st July to 30 June the following year. One exception was the first reporting which covered 1st January 2019 to 30 June 2020. In addition, the dataset covers the United Kingdom and Norway who reported data in line with the Regulation (EU) 2018/956 (the UK was subject to the Regulation in the reporting period 2019-20).

GTS Bulletin: PWDE60 EDZW - Pictorial information (Binary coded) (details are described in the abstract)

The PWDE60 TTAAii Data Designators decode as: T1 (P): Pictorial information (Binary coded) T1T2 (PW): Wind A1 (D): 90°E - 0° northern hemisphere A2 (E): 24 hours forecast T1ii (P60): 600 hPa (Remarks from Volume-C: WAFS W+T H+24 FL 140 (ICAO EU))

GTS Bulletin: HHXI10 EDZW - Grid point information (GRIB) (details are described in the abstract)

The HHXI10 TTAAii Data Designators decode as: T1 (H): Grid point information (GRIB) T1T2 (HH): Height A1 (X): Global Area (area not definable) A2 (I): 48 hours forecast T1ii (H10): 100 hPa (Remarks from Volume-C: H+ 48 (GLOBAL MODEL) HEIGHT 100 HPA)

GTS Bulletin: HHXO20 EDZW - Grid point information (GRIB) (details are described in the abstract)

The HHXO20 TTAAii Data Designators decode as: T1 (H): Grid point information (GRIB) T1T2 (HH): Height A1 (X): Global Area (area not definable) A2 (O): 120 hours forecast (5 days) T1ii (H20): 200 hPa (Remarks from Volume-C: H+ 120 (GLOBAL MODEL) HEIGHT 200 HPA)

GTS Bulletin: HRXX30 EDZW - Grid point information (GRIB) (details are described in the abstract)

The HRXX30 TTAAii Data Designators decode as: T1 (H): Grid point information (GRIB) T1T2 (HR): Relative humidity A1 (X): Global Area (area not definable) A2 (X): Not assigned T1ii (H30): 300 hPa (Remarks from Volume-C: H+ 66 (GLOBAL MODEL) RELATIVE HUMIDITY 700 HPA)

Project OTC-Genomics: Environmental and microbial time series data from the Warnow estuary and the Baltic Sea coast

Estuaries and coasts are characterized by ecological dynamics that bridge the boundary between habitats, such as fresh and marine water bodies or the open sea and the land. Because of this, these ecosystems harbor ecosystem functions that shaped human history. At the same time, they display distinct dynamics on large and small temporal and spatial scales, impeding their study. Within the framework of the OTC-Genomics project, we compiled a data set describing the community composition as well as abiotic state of an estuary and the coastal region close to it with unprecedented spatio-temporal resolution. We sampled fifteen locations in a weekly to twice weekly rhythm for a year across the Warnow river estuary and the Baltic Sea coast. From those samples, we measured temperature, salinity, and the concentrations of Chlorophyll a, phosphate, nitrate, and nitrite (physico-chemical data); we sequenced the 16S and 18S rRNA gene to explore taxonomic community composition (sequencing data and bioinformatic processing workflow); we quantified cell abundances via flow cytometry (flow cytometry data); and we measured organic trace substances in the water (organic pollutants data). Processed data products are further available on figshare.

CO2 Emission Factors for Fossil Fuels

Germany is obligated to report its national emissions of greenhouse gases, annually, to the European Union and the United Nations. Over 80 % of the greenhouse-gas emissions reported by Germany occur via combustion of fossil fuels. The great majority of the emissions consist of carbon dioxide. To calculate carbon dioxide emissions, one needs both the relevant activity data and suitable emission factors, with the latter depending on the applicable fuel quality and input quantities. In light of these elements' importance for emission factors, the German inventory uses country-specific emission factors rather than international, average factors. To determine such factors, one requires a detailed knowledge of the fuel compositions involved, especially with regard to carbon content and net calorific values. The present publication provides an overview of the quality characteristics of the most important fuels used in Germany and of the CO 2 emission factors calculated on the basis of those characteristics. Since annual greenhouse-gas emissions have to be calculated back to 1990, the study also considers fuels that are no longer used today. To that end, archival data are used. Gaps in the data are closed with the help of methods for recalculation back through the base year. Veröffentlicht in Climate Change | 29/2022.

Data and value-based decision-making for a sustainable land use - Datenbasierte Bewertung der multifunktionalen und digitalen Transformation eines Landwirtschaftsbetriebs anhand des Beispiels von Gut & Bösel in Alt Madlitz, Teilprojekt D

Der Zukunftsbetrieb schafft es die Daten seines Betriebs und der Umwelt so zu erfassen, zu bündeln und als Entscheidungsgrundlage zu nutzen, dass er das ökologische, soziale und regionalökonomische Optimum erreicht. Dafür möchten wir mit diesem Projekt die Grundlage schaffen. Ziel ist es, auf dem potenziellen Zukunftsbetrieb, welcher mit seinem Standort in Brandenburg bereits jetzt spürbar vom Klimawandel betroffen ist, einen Prototypen für die integrierte Datenerhebung, -vernetzung und -auswertung zu entwickeln, welcher zukünftig auf andere Betriebe übertragbar ist. Dieses Ziel erreichen wir, indem wir die komplexen Zusammenhänge von Boden, Wasser, Biodiversität, (Mikro-)Klima, Tieren und Bewirtschaftungsformen mithilfe von digitalen Instrumenten messen, mittels Mobilfunks verfügbar machen, die Messungen u.a. durch künstliche Intelligenz (KI) auswerten und mithilfe geeigneter Bewertungssystematiken monetär bewerten. Die Erkenntnisse sollen für die zukünftige Landnutzung in Deutschland zugänglich gemacht und darüber hinaus öffentlich diskutiert werden, um die Basis für die weitere Transformation hin zu einer nachhaltigen Landwirtschaft zu schaffen. Derzeit gehen viele negative und positive Effekte der Land- und Ernährungswirtschaft als Externalitäten nicht in die betriebliche Kostenrechnung der Produzenten ein. So bilden die Marktpreise nicht die Realität für Mensch und Umwelt ab. Eine monetäre Bewertung der Externalitäten und die integrierte Darstellung mit allen wesentlichen Daten des Betriebs und seiner Umwelt hilft LandwirtInnen gute Entscheidungen zu treffen und gibt VerbraucherInnen die notwendige Transparenz bei der Kaufentscheidung, da zukünftig ein Preis alle wesentlichen Kosten und Wertbeiträge abbilden könnte. Das Projekt nutzt die Digitalisierung, um ökologisch vorteilhafte Anbausysteme bewert-, plan- und umsetzbar zu machen. Ein solcher integrativer Ansatz zahlt direkt auf die Empfehlungen der Zukunftskommission Landwirtschaft ein.

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