API src

Found 423 results.

Similar terms

s/meereisdicke/Meereisdecke/gi

HATPRO microwave radiometer measurements of the surface emissions from onboard the RV POLARSTERN during the ATWAICE expedition PS131 to the Arctic in summer 2022

The data set contains daily files of microwave radiation measurements by the HATPRO microwave radiometer (see Rose et al., 2015, doi:doi:10.1016/j.atmosres.2004.12.005) during a surface-observation mode onboard the RV POLARSTERN during cruise PS131 (ATWAICE expedition, see Kanzow, 2023, doi:10.57738/BzPM_0770_2023). The instrument was installed at about 22 m height at the top deck on starboard site. Via a mirror construction the radiometers were observing the surface, i.e, ocean and sea ice, at a viewing angle of about 53° off nadir for 15 min each hour. The actual viewing angle could vary by a few degrees because of ship motion. Also, on six occasions the mirror position was changed to observe at different viewing angles. The data covers the range July 7, 2022 to August 12, 2022. The radiation measurements are given as brightness temperatures in seven K band channels (22.24 - 31.4 GHz), predominantly-vertical polarization at the viewing angle, and seven V band (51.26 - 58 GHz) channels, predominantly-horizontal polarization at the viewing angle. During the cruise the instrument was calibrated with liquid nitrogen on July 7 and July 30. The data is processed with the mwr_pro software (doi:10.5281/zenodo.7973552). Quality flags characterizing the instrument and retrieval performance are set and described. The brightness temperatures are provided for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. The sanity_receiver_band1, sanity_receiver_band2 and rain_flag are rather strict and data quality might still be satisfying despite the flag being set. However, we recommend to exclude data where visual_inspection_filter_band_1, visual_inspection_filter_band_2 or visual_inspection_filter_band_3 are set.

Vergangene und zukünftige Entwicklung der Eismassen auf Svalbard - Klimaantrieb und Telekonnektionen

Der Klimawandel ist eine der Hauptherausforderungen für die Menschheit im 21. Jahrhundert. Seine Auswirkungen sind vielschichtig wobei der anwachsende Massenverlust von Gletschern außerhalb der großen Eisschilde sowie deren bedeutender Beitrag zum Meeresspiegelanstieg zu den am stärksten hervorstechenden zählt. Diesbezüglich sind die Gletscher und Eiskappen der Arktis aufgrund ihres großen Volumens und ihrer großen Oberfläche, die als Kontaktfläche zum Klima- und Ozeanantrieb und damit zum Klimawandel selber fungiert, von besonderer Bedeutung. Da die Arktis darüber hinaus diejenige Region der Erde mit dem höchsten, prognostizierten, zukünftigen Temperaturanstieg ist, wird erwartet, daß sich die Bedeutung der arktischen Eismassen für den Meeresspiegelanstieg auch in Zukunft fortsetzt oder sogar noch steigern wird.Die großen Gletscher der Nordpolarregion umgeben den arktischen Ozean in ähnlichen Breitenlagen, weisen aber in jüngster Zeit ein inhomogenes Verhalten auf. Diese Tatsache legt eine räumliche Variabilität der klimatischen und ozeanischen Antriebsmechanismen der Gletschermassenbilanz innerhalb der zirkumarktischen Regionen nahe und offenbart damit die Diversität der Einflüsse des Klimawandels. Bezüglich der Variabilität der Antriebsmechanismen weist Svalbard in der Arktis eine einzigartige Lage auf. Es liegt an der Grenze zwischen kalten, polaren Luftmassen und Ozeanwassern und den Einflüssen des Westspitzbergenstroms, welcher der hauptsächliche Warmwasserlieferant für das arktische Umweltsystem ist. Darum verspricht das Erforschen der Reaktionen der Gletscher auf Svalbard auf die Veränderlichkeit des Klima- und Ozeanantriebs bedeutende Einblicke in die komplexe Kausalkette zwischen Klimawandel, der Variabilität der Klima- und Ozeanbedingungen in der Arktis und der Reaktion der arktischen Landeismassen. Das Ziel des Projektes ist es eine zuverlässige Abschätzung der räumlichen und zeitlichen Variabilität der klimatischen Massenbilanz aller Gletscher und Eiskappen auf Svalbard zu erreichen und diese mit dem Klima- und Ozeanantrieb in Verbindung zu setzen. Dazu wird ein räumlich verteiltes, von statistisch downgescalten Klimadaten angetriebenes Model zur Berechnung der klimatischen Massenbilanz aufgesetzt. Die Massenbilanz aller Gletscherflächen auf Svalbard wird für den Zeitraum 1948-2013 modelliert und die zeitlich variablen Felder von Ablation, Akkumulation, wiedergefrorenem Schmelzwasser und klimatischer Massenbilanz für anschließende geostatistische Studien genutzt. Diese Studien werden potentielle Einflüsse der raumzeitlichen Variabilität von großräumigen Mustern des Luftdrucks, der Meereisbedeckung und der Meeresoberflächentemperatur auf die Variabilität der Gletschermassenbilanz auf Svalbard identifizieren und analysieren. Auch Telekonnektionen zu fernen Modi der atmosphärischen Zirkulation werden durch Studien bezüglich der potentiellen Einflüsse verschiedener atmosphärischer Zirkulationsindizes in die Betrachtungen einbezogen.

Schwerpunktprogramm (SPP) 1158: Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; Bereich Infrastruktur - Antarktisforschung mit vergleichenden Untersuchungen in arktischen Eisgebieten, Zirkum-Antarktische Auftrittsfrequenz von Meereis-Rinnen und regionale Verteilung aus Satellitendaten

Ziel dieses Projektvorhabens ist es, einen Einblick in die räumliche und zeitliche Variabilität des Auftretens von Meereisrinnen im Antarktischen Meereis während der Wintermonate zu erhalten. Meereis-Rinnen zeichnen sich dadurch aus, dass es in ihrem Einflussbereich zu einem starken Austausch von Wärme, Feuchte und Impuls zwischen dem relativ warmen Ozean und der kalten Atmosphäre kommt. In Meereis-Rinnen bildet sich demnach neues, dünnes Eis und trägt damit zur Meereis-Massenbilanz bei. Wir beabsichtigen auf einer Methode aufzubauen, die entwickelt wurde, um Eisrinnen in der Arktis automatisch aus Thermal-Infrarot Satellitendaten zu identifizieren. Diese Methode muss für eine Anwendung auf Satellitendaten der Antarktis neu implementiert und erweitert werden. In diesem Rahmen gilt es auch, hemisphärische Besonderheiten in den Meereiseigenschaften und atmosphärischen Einflüssen zu berücksichtigen. Darum werden Anpassungen im ursprünglichen Algorithmus mit Hilfe detaillierter Fallstudien vorzunehmen sein. Als Ergebnis erwarten wir umfangreiche Erkenntnisse darüber, wann und wo Meereis-Rinnen gehäuft in der Antarktis auftreten, und wie diese Auftrittsmuster durch atmosphärische und ozeanische Antriebe gesteuert werden.

ARK 2018: AWI airborne ultra-wideband microwave radar data at and upstream of 79°N Glacier, northeast Greenland Ice Sheet (RESURV 79 Project)

This dataset contains airborne radar data acquired using the AWI ultra-wideband microwave radar (UWBM) during the Arctic season of 2018. The profiles extend across the Greenland Ice Sheet over and upstream of 79°N Glacier (Nioghalvfjerdsbræ; northeast Greenland). Furthermore, one flight extends over sea ice northeast of the Greenland Ice Sheet. The data are available as netCDF files (including waveforms and metadata), KML files of the profile line locations, and quicklook images of the radargrams. For every profile we provide four radar products (img_01, img_02, img_03, img_04), which correspond to the four polarizations (VV, VH, HH, HV).

Arctic PASSION - High Resolution Synthetic Aperture Radar based Risk Index Outcome (AP-RIO)

The Risk Index Outcome (RIO) is a critical component of the Polar Operational Limit Assessment Risk Indexing System (POLARIS) developed by the International Maritime Organization (IMO, 2016). RIO evaluates the operational risks for ships navigating in ice-infested waters by evaluating ice conditions and offers a quantifiable measure of risk that aids in decision-making for safe navigation in polar regions based on ship ice class, sea ice type/stage of development (SOD) and sea ice concentration (SIC). The DMI-led Automated Sea Ice Products (DMI-ASIP; Wulf et al., 2024, dataset) provides daily maps of SOD and SIC based on Sentinel-1 SAR imagery, AMSR-2 Passive Microwave and Ice Charts from the Greenland and Canadian Ice Services, combined with novel AI retrieval and processing techniques. In the framework of EU funded Arctic PASSION project, we produced 10 years of satellite observation based weekly RIO maps referred as the Arctic PASSION-RIO (AP-RIO) by leveraging DMI-ASIP datasets. The AP-RIO dataset will provide weekly risk assessment maps for the given ship classes and will support the establishment of a 10 year climatology thereby enabling the assessment of RIO variability in the years covered by the input DMI-ASIP products. The AP-RIO dataset will enhance the safety and efficiency of maritime operations in the polar seas, providing a robust reference for evaluating normal and extreme ice conditions. AP-RIO is produced in the framework of the Arctic PASSION project (European Union's Horizon 2020 research and innovation program under grant agreement No. 101003472) and supported by the DMI-ASIP development team. Algorithm and Processing Scheme: SIC and SOD from ASIP are processed (by taking the mean and mode respectively) into a weekly field based on the daily files for that week. This is done for the time period of 3 Oct. 2014 - 3 Oct. 2024. The weekly SOD is used to find the Risk Value (RV) by looking at the lookup table (Dybkjær et al. 2025a). Risk Index Outcome (RIO) values are computed for each pixel in the field based on the RIO formula (RIO = SIC x RV) using the SIC from ASIP and the found RV. The meaning of the computed RIO values can be interpreted using the table in (Dybkjær et al. 2025b). The RIO field is finally saved to weekly NetCDF files.

Temperature measurements from SIMBA-type sea ice mass balance buoy 2022T97

Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2022T97 (original name NPOL_0803) installed on drifting sea ice in the Arctic Ocean during the expedition Kronprins Haakon AO22 in 2022. Data is available between 2022-08-06 10:38:00 and 2022-11-22 03:02:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project Arctic Passion.

Heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T57: 30 s after the start of the heating cycle

Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2019T57 (original name FMI05-08) installed on drifting sea ice in the Arctic Ocean during the expedition Polarstern PS122 (MOSAiC) in 2019/20. Data is available between 2019-10-07 03:00:00 and 2020-01-18 02:00:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project FMI.

Heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T57: 120 s after the start of the heating cycle

Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2019T57 (original name FMI05-08) installed on drifting sea ice in the Arctic Ocean during the expedition Polarstern PS122 (MOSAiC) in 2019/20. Data is available between 2019-10-07 03:00:00 and 2020-01-18 02:00:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project FMI.

Temperature measurements from SIMBA-type sea ice mass balance buoy 2019T59

Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2019T59 (original name FMI05-10) installed on drifting sea ice in the Central Arctic Ocean during the expedition Polarstern PS122 (MOSAiC) in 2019/20. Data is available between 2019-10-07 07:30:00 and 2020-08-13 19:00:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project FMI.

Temperature measurements from SIMBA-type sea ice mass balance buoy 2019T57

Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2019T57 (original name FMI05-08) installed on drifting sea ice in the Arctic Ocean during the expedition Polarstern PS122 (MOSAiC) in 2019/20. Data is available between 2019-10-07 03:00:00 and 2020-01-18 02:00:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project FMI.

1 2 3 4 5 … 41 42 43