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Continuous water level observations at station BEFmate_S10upp, 2020-01 to 2023-09

Data presented here were collected between 2020-01 and 2023-09 at station BEFmate_S10upp within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Groundwater levels at different elevation zones were measured using pressure loggers deployed in dip wells within the experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using Hobo U20L Water Level Loggers (Onset Computer Corporation, Bourne, MA/USA). All devices were pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the HOBOware Pro (V3.7.28) software, Subsequent data processing was done using MATLAB (R2024b). Atmospheric pressure correction for water-level calculations was applied using data from a nearby weather station. Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) an outlier detection, and (c) visual checks. Outliers in water level and temperature time series were detected using a moving-median filter and a 3-sigma criterion, with additional cross-checking against a reference sensor. Identified outliers were removed, and height-corrected water level series were produced to ensure consistency across sensors and years.

Continuous wave and tide observations at DynaCom artificial islands in the back-barrier tidal flat, Spiekeroog, Germany, 2019-01 to 2019-12

Data presented here were collected between January 2019 to December 2019 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.78 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.

High-resolution measurements of essential climate variables in the North Sea from the autonomous surface vehicle HALOBATES during RV Heincke cruise HE614

The autonomous surface vehicle HALOBATES measured Essential Climate Variables (ECV), such as sea surface temperature (SST) and salinity (SSS), during the RV Heincke cruise HE614 in the German Bight. HALOBATES captured the SST and SSS at seven depths with a high vertical resolution of about 10 cm, from the near-surface layer (NSL) (between 30 and 100 cm) and the sea surface microlayer (SML) (upper millimeter). Conductivity, temperature, and depth (CTD) sensors measured temperature and conductivity (for salinity calculation) via a flow-through system on HALOBATES. Additional temperature sensors were mounted underneath the catamaran to measure in-situ temperature in situ at six depths in the NSL. Salinity was corrected with discrete water samples to remove biases between the sensors. Two data loggers with several meteorological stations on the catamaran captured important weather variables during operation time. The surfactant concentration was measured from discrete samples of SML and 100 cm depth. HALOBATES was operated between 01 March 2023 and 22 March 2023.

GTS Bulletin: FTYG31 LYYN - Forecast (details are described in the abstract)

The FTYG31 TTAAii Data Designators decode as: T1 (F): Forecast T1T2 (FT): Aerodrome (VT >= 12 hours) A1A2 (YG): Serbia (Remarks from Volume-C: NilReason)

WMS Wettvermittlungsstellen Hamburg

Web Map Service (WMS) zum Thema Wettvermittlungsstellen in Hamburg. Zur genaueren Beschreibung der Daten und Datenverantwortung nutzen Sie bitte den Verweis zur Datensatzbeschreibung. Erläuterung zum Fachbezug: Für die Aktualität der Daten nutzen Sie bitte den Verweis zur Datensatzbeschreibung.

Schwerpunktprogramm (SPP) 1158: Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; Bereich Infrastruktur - Antarktisforschung mit vergleichenden Untersuchungen in arktischen Eisgebieten, Einflüsse von Schnee auf antarktisches Meereis (SCASI)

Die Ausdehnung des antarktischen Meereises nahm im Laufe der letzten Jahre zu und steht damit im Gegensatz zur Abnahme in der Arktis. Die Gründe hierfür sind Gegenstand aktueller Forschungsprojekte. Wechselwirkungen mit der Atmosphäre und dem Ozean spielen sicherlich eine wesentliche Rolle, aber auch die dicke und heterogene Schneeauflage des Meereises hat einen große Einfluss auf das Meereis und seine Rolle im globalen Klima und Wettergeschehen. Zugleich erschwert die Schneeauflage flugzeug- und satellitenbasierte Messungen über Meereis, da sie die Oberflächeneigenschaften bestimmt und zu großen Unsicherheiten beiträgt. Entsprechend ist eine bessere Kenntnis der Schneeverteilung auf Meereis dringend erforderlich, um Veränderungen besser verstehen und simulieren zu können. Ziel des Projektes ist es die Menge und Verteilung von Schnee auf antarktischem Meereis sowie dessen physikalische Eigenschaften und deren zeitliche Variabilität zu quantifizieren. Die Entwicklung eines neuen und konsistenten Datenprodukts für Schnee auf antarktischem Meereis steht im Vordergrund des Projektes. Dieses soll die hohe Variabilität über unterschiedliche Größenskalen und Jahreszeiten abbilden. Mithilfe dieses Produktes sind wir dann in der Lage Fernerkundungsalgorithmen und Modellsimulationen zu verbessern und zu validieren. Schließlich wird unser Projekt das Gesamtverständnis der Massenbilanz und Dynamik antarktischen Meereises verbessern, und leistet so einen wichtigen Beitrag für die biologische und geochemische Erforschung des eisbedeckten Südozeans. Um diese Ziele zu erreichen, werden hochaufgelöste Modelle betrieben, die durch Feld- und Fernerkundungsdaten von antarktischem Schnee auf Meereis gestützt und geleitet werden. Im Rahmen einer neuen deutsch-schweizer Zusammenarbeit (D-A-CH Programm) werden die Meereisexpertisen aus Feldmessungen und Fernerkundung der deutschen Partner mit der Schneeexpertise aus Feldmessungen und Modellierung der Schweizer Partner kombiniert. Die Projektpartner verfügen über detaillierte Schneemessungen mehrerer erfolgreicher Feldkampagnen auf antarktischem Meereis, die durch autonome Messungen ergänzt werden. Daten der Satelliten AMSR-2, SMOS und CryoSat-2 sind verfügbar und werden genutzt, um neue Algorithmen für die Bestimmung von Schneeeigenschaften auf Meereis zu entwickeln. Diese Algorithmen und daraus resultierende Datensätze werden durch Beobachtungen validiert und verbessert. Durch die Kopplung der numerischen Schneemodelle SNOWPACK und MEMLS werden Schneedicke, -temperatur, -dichte und Mikrowellenemissivität simuliert. Das Projekt ist darauf ausgelegt drei junge Wissenschaftler für Ihre Arbeit in der Meereisforschung zu finanzieren. Zwei erfahrene Post-Doktoranden sind vorgesehen. Beide haben bereits ähnliche Methoden und Datensätze im Rahmen ihren Doktorarbeiten bearbeitet. Ein Doktorand wird dieses Projekt zur Promotion nutzen.

Deutscher Wetterdienst Open Data Server

The Deutscher Wetterdienst (DWD) Open Data Server is the official open data portal of Germany's national meteorological service. Established under a legal mandate from the German Weather Service Act, its primary purpose is to provide free public access to a vast and diverse collection of weather, climate, and environmental spatial data. The repository serves a wide range of users, from researchers and developers to businesses and the general public, by offering data essential for applications in science, technology, and public information. The scope of data is comprehensive and includes: Weather Data: Numerical model forecasts, radar data, and current measurements/observations. Climate Data: A large archive of historical and processed climate data, accessible via the integrated Climate Data Center (CDC) subtree. Specialized Formats: Many datasets are also available through OGC-compatible web services (WMS, WFS) for integration into Geographic Information Systems (GIS). The platform operates on the principle of free provision, though it notes that service and availability levels for the open server are not guaranteed. For critical business or operational processes requiring higher data integrity, dedicated service agreements are available. The repository is actively maintained, with news feeds and change logs to inform users of updates and extensions to the data offerings.

Monthly maximum duration of dull days

Maps of monthly maximum duration of dull days derived from satellite and in-situ observations ('satellite weather') on a 0.25x0.25 degree grid (near real time product), provided by WMO Regional Climate Centre (RCC) on Climate Monitoring

GTS Bulletin: FCRO32 LROM - Forecast (details are described in the abstract)

The FCRO32 TTAAii Data Designators decode as: T1 (F): Forecast T1T2 (FC): Aerodrome (VT < 12 hours) A1A2 (RO): Romania (The bulletin collects reports from stations: LRAR;ARAD INT ;LRBM;TAUTII MAGHERAUS ;LRCL;CLUJ-NAPOCA INT ;LROD;ORADEA INT ;LRSM;SATU MARE ;LRTM;TRANSILVANIA TARGU MURES INT;)

GTS Bulletin: SARO32 LROM - Surface data (details are described in the abstract)

The SARO32 TTAAii Data Designators decode as: T1 (S): Surface data T1T2 (SA): Aviation routine reports A1A2 (RO): Romania (The bulletin collects reports from stations: LRAR;ARAD INT ;LRBM;TAUTII MAGHERAUS ;LRCL;CLUJ-NAPOCA INT ;LROD;ORADEA INT ;LRSM;SATU MARE ;LRTM;TRANSILVANIA TARGU MURES INT;)

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