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Model Output Statistics for ANDA (50854)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for ZHENGZHOU (57083)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Plant wax composition of sediment core ROT21 from the Rotsee, Switzerland

This dataset contains compound-specific hydrogen (δ2H) and carbon (δ13C) isotope compositions and concentrations of long-chain n-alkanes and fatty acids (n-alkanoic acids) from the ROT21 sediment record of Rotsee, Central Switzerland (47°04′10″N, 8°18′48″E, 419 m a.s.l.). Sediment cores were retrieved in October 2021 using a UWITEC gravity corer, and the dataset spans the past ~13,000 years based on 19 radiocarbon dates (terrestrial and aquatic macrofossils) integrated with 210Pb and 137Cs profiles (see De Jonge et al., 2025). Laboratory analyses were conducted between February 2023 and November 2024 at the University of Basel. Sediment samples (~2–5 g) were sub-sampled, freeze-dried, spiked with internal standards (n-C19-alkanoic acid, n-C36-alkane, 2-octadecanone, and n-C21-alkanol), and extracted with dichloromethane/methanol (9:1, v/v) using an Accelerated Solvent Extractor (Dionex ASE 350, Thermo Fisher Scientific). Following saponification, neutral fractions were separated via silica gel chromatography, and fatty acids were converted to fatty acid methyl esters (FAMEs). Both n-alkanes and FAMEs were further purified to isolate saturated compounds using AgNO3-impregnated silica gel columns, then analyzed and quantified by gas chromatography with flame ionization detection (GC-FID). Peak areas were normalized to recovery standards to account for potential losses during sample handling, and compounds were identified by comparison with external standards. Compound-specific δ2H and δ13C values were determined by gas chromatography-isotope ratio mass spectrometry (GC-IRMS) and normalized to the VSMOW-SLAP (δ2H) and VPDB (δ13C) scales. Analytical precision was ±3-5 ‰ for δ2H and ±0.2–0.3 ‰ for δ13C. The dataset was generated to reconstruct past hydroclimate and vegetation dynamics in Central Europe using plant wax δ2H records. Full methodological details are provided in the study: Central Europe hydroclimate since the Younger Dryas inferred from vegetation-corrected sedimentary plant wax δ2H values (Santos et al., 2026).

Nitrogen isotope and biomarker data during Late Paleocene–early Eocene

This dataset compiles raw measurements generated to investigate perturbations of the marine nitrogen cycle during the Paleocene–Eocene Thermal Maximum (PETM). It includes abundances of isoprenoidal GDGTs (isoGDGTs) and crenarchaeol mass accumulation rates, (ii) chromatographic peak areas of bacteriohopanetetrol (BHT) and BHT-x, and (iii) the nitrogen isotopic composition of bulk sediments (bulk sediment δ¹⁵N). Samples were collected from multiple ocean basins and regions: the Central Arctic Ocean (IODP 302–M0004), East Tasman Plateau in the Southwest Pacific (ODP Site 1172), Central Northern Caucasus (Kheu River), the New Jersey Shelf/Atlantic Coastal Plain (ODP 174AX Ancora), the Côte d'Ivoire–Ghana Transform Margin in the equatorial Atlantic (ODP 959), the Southeast Newfoundland Ridge in the central North Atlantic (IODP 1403), Fur Island, Denmark (Fur Formation), and the Tarim Basin, western China (Qimugen Formation). Lipid biomarker data were obtained using liquid chromatography coupled to mass spectrometry, and bulk nitrogen isotope data were measured by elemental analysis coupled to isotope-ratio mass spectrometry.

Pan-Arctic Visualization of Landscape Change (2003-2022), Arctic PASSION Permafrost Service

This raster dataset, in Cloud Optimized GeoTIFF format (COG), provides information on land surface changes at the pan-arctic scale. Multispectral Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI imagery (cloud-cover less than 80%, months July and August) was used for detecting disturbance trends (associated with abrupt permafrost degradation) between 2003 and 2022. For each satellite image we calculated the Tasseled Cap multi-spectral index to translate the spectral reflectance signal to the semantic information Brightness, Greenness, and Wetness. In order to characterize change information, we calculated the linear trend of the Brightness, Greenness and Wetness over two decades on the individual pixel level. The final map product therefore contains information on the direction and magnitude of change for all three Tasseled Cap parameters in 30m spatial resolution across the pan-arctic permafrost domain. Features detected include coastal erosion, lake drainage, infrastructure expansion, and fires. The general processing methodology was developed by Fraser et al. 2014 and adapted and expanded by Nitze et al. 2016 and Nitze et al. 2018. Here we upscaled the processing to the circum-arctic permafrost region and the recent 20-year period from 2003 through 2022. The service covers the permafrost region up to 81° North: Alaska (USA), Canada, Greenland, Iceland, Norway, Sweden, Finland, Russia, Mongolia, and China. For Russia and China, regions not containing permafrost were excluded. The data has been processed in Google EarthEngine within the research projects ERC PETA-CARB, ESA CCI+ Permafrost, NSF Permafrost Discovery Gateway, and EU Arctic PASSION. The dataset is a contribution to the 'Panarctic requirements-driven Permafrost Service' of the Arctic PASSION project (see references). Changes in the Tasseled Cap indices Brightness, Greenness, and Wetness are displayed in the image bands red, green, and blue, respectively. Here, coastal erosion (a trend of a land surface transitioning to a water surface) is depicted in dark blue colors, while coastal accretion (a trend of a water surface transitioning to a land surface) is depicted in bright orange colors. Drained lakes appear in bright yellow or orange colors, depending on the soil conditions and vegetation regrowth. Fire scars are a further common feature, which can appear in different colors, depending on the time of the fire and pre-fire land cover. The data can be explored via the Arctic Landscape EXplorer (ALEX, see references) and is available as a public web map service (WMS, see references), both hosted by Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research.

Pan-Arctic Visualization of Landscape Change (2005-2024), Arctic PASSION Permafrost Service

This raster dataset, in Cloud Optimized GeoTIFF format (COG), provides information on land surface changes at the pan-arctic scale. Multispectral Landsat-5 TM, Landsat-7 ETM+, Landsat-8 OLI, and Landsat-9 OLI-2 imagery (cloud-cover less than 70%, months July and August) was used for detecting disturbance trends (associated with abrupt permafrost degradation) between 2005 and 2024. For each satellite image, we calculated the Tasseled Cap multi-spectral index to translate the spectral reflectance signal to the semantic information Brightness, Greenness, and Wetness. In order to characterize change information, we calculated the linear trend of Brightness, Greenness, and Wetness over two decades at the individual pixel level, based on annually aggregated data. The final map product therefore contains information on the direction and magnitude of change for all three Tasseled Cap parameters at 30 m spatial resolution across the pan-arctic permafrost domain. Features detected include coastal erosion, lake drainage, infrastructure expansion, and fires. The general processing methodology was developed by Fraser et al. (2014) and adapted and expanded by Nitze et al. (2016, 2018). Here, we upscaled the processing to the circum-arctic permafrost region and applied it to the recent 20-year period from 2005 through 2024. The service covers the permafrost region up to 81° North: Alaska (USA), Canada, Greenland, Iceland, Norway, Sweden, Finland, Russia, Mongolia, and China. For Russia and China, regions not containing permafrost were excluded. The data have been processed in Google Earth Engine as part of the research projects ERC PETA-CARB, ESA CCI+ Permafrost, NSF Permafrost Discovery Gateway, and EU Arctic PASSION. The dataset is a contribution to the 'Pan-Arctic Requirements-Driven Permafrost Service' of the Arctic PASSION project (see References). Changes in the Tasseled Cap indices – Brightness, Greenness, and Wetness – are displayed in the image bands red, green, and blue, respectively. Here, coastal erosion (a trend of a land surface transitioning to a water surface) is depicted in dark blue tones, while coastal accretion (a trend of a water surface transitioning to a land surface) is depicted in bright orange colors. Drained lakes are shown in bright yellow or orange colors, depending on the soil conditions and vegetation regrowth. Fire scars are a further common feature, appearing in different colors depending on the time of the fire and the pre-fire land cover. The data can be explored via the Arctic Landscape EXplorer (ALEX; see References) and are available as a public web map service (WMS; see References), both hosted by Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research.

Strong motion earthquake data values of digitized strong-motion accelerograms, 1933-1994

The Strong Motion Earthquake Data Values of Digitized Strong-Motion Accelerograms is a database of over 15,000 digitized and processed accelerograph records from 1933 to 1994. Data were obtained from a variety of structural and geologic environments. Most of the data are available in three levels of processed files. The first type of file contains raw (uncorrected) time, history data points digitized from the analog accelerogram. The second is a filtered, instrument corrected version of the time, history data. This file also contains calculated velocities and displacements obtained by the integration and double integration of the corrected accelerations. The third type of file includes the calculated Fourier and response spectra data. The data are from the United States, Algeria, Argentina, Armenia, Australia, Bulgaria, Canada, Chile, China, El Salvador, Fiji, Germany, Greece, India, Iran, Italy, Japan, Mexico, New Zealand, Nicaragua, Papua New Guinea, Peru, Portugal, Romania, Spain, Taiwan, Turkey, and Uzbekistan. This database is static and is no longer being updated. This dataset has been archived in the framework of the PANGAEA US data rescue initiative 2025.

Model Output Statistics for LANZHOU AIRPORT (52889)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

ZFMK Coleoptera collection

The beetle collection of the Leibniz Institute for the Analysis of Biodiversity Change - Museum Koenig Bonn comprises about 2.5 million specimens and includes a number of larger and smaller collections. Among the more important are the Rhineland collection founded by F. Rüschkamp and extended by the AG Rheinischer Koleopterologen (Wagner 2007), the collection of myrmeco- and termitophile beetles of A. Reichensperger (or at least a part of it - although mentioned in Horn et al. (1990) that it was destroyed during World War II), and a part of the collection R. Oberthür, acquired by the Museum in 1956. Moreover, the collection has grown by expeditions of museum staff, such as those of H. Roer, who was for long time (1963-1991) curator of Coleoptera (Schmitt 2003), and of J. Klapperich, employed as Coleoptera technician from 1935 to 1952 at the museum (Lucht 1988). Klapperich collected during this time much in Fujian (China) and other regions and after his membership to the institute the museum acquired still much of his material (e.g. from Afghanistan). Consequently, many other museums house material of Klapperich, e.g. the National Museum in Prague, the Hungarian Natural History Museum, Budapest and the Staatliche Museum für Naturkunde, Karlsruhe. After Klapperich died 1987 the rest of the material of Klapperich collection was bought by the Staatliches Museum für Naturkunde, Stuttgart (Schmitt 2007). In the recent years additional material has been accumulated from research projects in Eastern Africa mainly through the activities of T. Wagner. Recent accessions comprise material from South Africa, Arunachal Pradesh, and Laos (Scarabaeidae, Chrysomelidae)

Model Output Statistics for CHONGQING (57516)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

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