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Real time Power Supply for e-fuels - PtL-Produktion bei variabler Stromverfügbarkeit

1Hz resolution aircraft measurements of wind and temperature during the ACLOUD campaign in 2017

During the ACLOUD (Arctic Cloud Observations Using airborne measurements during polar Day) campaign conducted in May/June 2017 meteorological data (temperature, horizontal wind components, air pressure) have been measured using instrumentation that was installed at the nosebooms of both aircraft Polar 5 and Polar 6. This dataset presents the 1Hz resolution data. The high temporal resolution data (at 100 Hz) with all wind components are available here: doi:10.1594/PANGAEA.900880). For each flight the data are given as functions of time and position (including height above ground) along the flight tracks. Listed in this repository are all flights beginning with the test flight in Bremen and the Ferryflights to Longyearbyen. All other measurement flights started and ended in Longyearbyen. Each file represents an entire flight starting well before the first movement of the plane and ending after the final parking position has been reached after landing. The wind measurement is only valid during flight and the full accuracy is only achieved during straight level flight sections. The absolute accuracy of the wind components is 0.2m/s for straight and level flights sections. For further informations on the data processing and accuracy of the turbulence measurement refer to Hartmann et al. (2018, doi:10.5194/amt-11-4567-2018). For further information on the ACLOUD campaign we refer to Wendisch et al. (2018, doi:10.1175/BAMS-D-18-0072.1). -- All data are given as decimal values at 1Hz in columns in this order and meaning: UTC - UTC-time in seconds (since midnight) h - height in metres based on WGS84 lon - longitude in degress based on WS84 lat - latitude in degress based on WS84 p - static pressure in hpa gs - ground speed in m/s pitch - pitch angle in degrees roll - roll angle in degrees rh - relative humidity from Vaisala at noseboom T - temperature from PT100, corrected for adiabatic heating u - west-east component of wind speed in m/s, positive towards east v - south-north component of wind speed in m/s, positive towards north tas - true air speed in m/s

Wetterdaten - Projekt iResilience 2021-III

Sensorwetterdaten des nordwärts-Projektes iResilience des Quartal III des Jahres 2021. Die Sensorwetterdaten des gesamten Projektes stehen in einer Datei als Alternativer Export unter Exporte zur Verfügung. Projektzeitraum vom 01.10.20 - 30.09.22.Was ist iResilience? iResilience ist ein Forschungsprojekt, das vom Bundesministerium für Bildung und Forschung in der Initiative Zukunftsstadt unter dem Titel Soziale Innovationen und intelligente Stadtinfrastrukturen für die resiliente Stadt der Zukunft über den Zeitraum von drei Jahren gefördert wird. Ein interdisziplinäres Team aus Forschung und Praxis begleitet den Prozess. TEAMDer Forschungsverbund stellt sich vorDas iResilience-Team setzt sich aus einem interdisziplinären Team aus Wissenschaft und Praxis zusammen. Die Koordination des Gesamtvorhabens liegt bei der Sozialforschungsstelle der Technischen Universität Dortmund (sfs). Co-Koordinator für die Aktivitäten in Köln ist das Deutsche Institut für Urbanistik (Difu) in Köln. Das iResilience-Projektteam unterstützt die Pilotquartiere mit Fachwissen und Methoden, moderiert und dokumentiert Veranstaltungen und andere Formate und fördert die Kommunikation zwischen den aktiven Akteuren. Gefördert vom Bundesministerium für Bildung und Forschung.

Wetterdaten - Projekt iResilience 2022-II

Sensorwetterdaten des nordwärts-Projektes iResilience des Quartal II des Jahres 2022. Die Sensorwetterdaten des gesamten Projektes stehen in einer Datei als Alternativer Export unter Exporte zur Verfügung. Projektzeitraum vom 01.10.20 - 30.09.22.Was ist iResilience? iResilience ist ein Forschungsprojekt, das vom Bundesministerium für Bildung und Forschung in der Initiative Zukunftsstadt unter dem Titel Soziale Innovationen und intelligente Stadtinfrastrukturen für die resiliente Stadt der Zukunft über den Zeitraum von drei Jahren gefördert wird. Ein interdisziplinäres Team aus Forschung und Praxis begleitet den Prozess. TEAMDer Forschungsverbund stellt sich vorDas iResilience-Team setzt sich aus einem interdisziplinären Team aus Wissenschaft und Praxis zusammen. Die Koordination des Gesamtvorhabens liegt bei der Sozialforschungsstelle der Technischen Universität Dortmund (sfs). Co-Koordinator für die Aktivitäten in Köln ist das Deutsche Institut für Urbanistik (Difu) in Köln. Das iResilience-Projektteam unterstützt die Pilotquartiere mit Fachwissen und Methoden, moderiert und dokumentiert Veranstaltungen und andere Formate und fördert die Kommunikation zwischen den aktiven Akteuren. Gefördert vom Bundesministerium für Bildung und Forschung.

Nachwuchsgruppen Klima, Umwelt und Gesundheit: Auswirkungen von Wetter auf die menschliche Gesundheit (HeWeCon)

Steigerung von Kontrolle und Effizienz in regionalen Energiesystemen durch Quantensensoren und maschinelles Lernen, Teilvorhaben: Optimierung von Zink-Luft-Batterien zur Stromnetzstabilisierung

Real time Power Supply for e-fuels - PtL-Produktion bei variabler Stromverfügbarkeit, Real time Power Supply for e-fuels - PtL-Produktion bei variabler Stromverfügbarkeit (RePoSe)

Meteorological data of Time Series Station Spiekeroog for 2020

The Time Series Station Spiekeroog (TSS) was setup in 2002, in the tidal inlet between the East Frisian Islands of Langeoog and Spiekeroog in the Southern German Bight, at position 53°45′01.0″ N, 007°40′16.3″ E. The aim was to ensure the continuous measurement of physical, biological, chemical and meteorological parameters, even under extreme weather conditions such as storms, ice, and storm surges. The TSS was financed as part of the Deutsche Forschungsgemeinschaft (DFG) research unit BioGeoChemistry of Tidal Flats and the Ministry for Science and Culture of the Land of Lower Saxony (MWK). Here, air temperature, air pressure, relative humidity, wind speed and wind direction were measured in the year 2020. All raw data were revised and corrected for steps as range, outliers and stationarity checks. A detailed description of the Time Series Station Spiekeroog, its structure and instrumentation can be found in Zielinski et al. (2022) and in Reuter et al. (2009).

Atmospheric methane and carbon dioxide concentrations measured during ALKOR cruise AL575, 29 June 2022 – 12 July 2022, Kiel – Kiel

Atmospheric carbon dioxide (CO2) and methane (CH4) concentrations were monitored during AL575 cruise (29.6.-12.7.2022) using a cavity ring down spectrometer (Picarro G2301-f CRDS) and GEOMARs 'Atmospheric Intake System' (AIS). The Picarro/AIS system was installed in the wet laboratory (starboard, main deck) of RV ALKOR and was connected by an aluminium tube to an air inlet nozzle placed at the bow of RV ALKOR. The air was pumped via the AIS intake tube with a flow rate of 2.67 litre per minute to the Picarro analyser. The atmospheric CH4, CO2, and water vapour concentrations were determined in real time at 2-3 Hz sampling rate. A time offset of 87 seconds between the air intake nozzle at the bow (6 m above sea surface) and the actual gas measurement at the Picarro was determined and the calculated flow rate considered, when correlating the gas data with navigation and weather data provided by the WERUM dship repository (https://dship.geomar.de). The weather data is published separately at doi:10.1594/PANGAEA.983833 .

Graduiertenkolleg (GRK) 1565: Complex Terrain and Ecological Heterogeneity - Evaluating ecosystem services in production versus water yield and water quality in mountainous landscapes (TERRECO), Evaluation of WRF 3.2 with observed climate data in Haean (WP 1-01)

In this research we analyzed nocturnal temperature inversions in Haean Basin. Inversions are important phenomena for understanding meteorological and hydrological character of the basin region. Three automatic weather station data and tethered balloon soundings were used to analyze inversion strength, depth, and occurrence of inversions. Stronger and deep inversion was found during early summer while weaker but frequent inversions occurred during late September and early October. A significant influence of fog layer was found. The fog layer acts as a break during a cooling process. The fog appears usually in early mornings. During our experiment, average potential temperature change at the surface was -1.08 K/h without fog presence. When the fog appeared six hours average decreased to -0.23K/h. The most deep and strongest inversion of the studied period was 0.19 °C/m temperature gradient.

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