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  • PANGAEA  (60)
  • Oxford University Press  (2)
  • American Physical Society
  • 2020-2024  (62)
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  • 1
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    Evidence for radial anisotropy in the lower crust of the Apennines from Bayesian ambient noise tomography in Europe (2021)
    Oxford University Press
    Details
    Publikationsdatum: 2024-02-07
    Beschreibung: Probing seismic anisotropy of the lithosphere provides valuable clues on the fabric of rocks. We present a 3-D probabilistic model of shear wave velocity and radial anisotropy of the crust and uppermost mantle of Europe, focusing on the mountain belts of the Alps and Apennines. The model is built from Love and Rayleigh dispersion curves in the period range 5–149 s. Data are extracted from seismic ambient noise recorded at 1521 broad-band stations, including the AlpArray network. The dispersion curves are first combined in a linearized least squares inversion to obtain 2-D maps of group velocity at each period. Love and Rayleigh maps are then jointly inverted at depth for shear wave velocity and radial anisotropy using a Bayesian Monte Carlo scheme that accounts for the trade-off between radial anisotropy and horizontal layering. The isotropic part of our model is consistent with previous studies. However, our anisotropy maps differ from previous large scale studies that suggested the presence of significant radial anisotropy everywhere in the European crust and shallow upper mantle. We observe instead that radial anisotropy is mostly localized beneath the Apennines while most of the remaining European crust and shallow upper mantle is isotropic. We attribute this difference to trade-offs between radial anisotropy and thin (hectometric) layering in previous studies based on least-squares inversions and long period data (〉30 s). In contrast, our approach involves a massive data set of short period measurements and a Bayesian inversion that accounts for thin layering. The positive radial anisotropy (VSH 〉 VSV) observed in the lower crust of the Apennines cannot result from thin layering. We rather attribute it to ductile horizontal flow in response to the recent and present-day extension in the region.
    Materialart: Article , PeerReviewed
    Format: text
    Format: archive
    Standort Signatur Erwartet Verfügbarkeit
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    ARTIKEL (OCEANREP)
  • 2
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    Shear wave splitting in the Alpine region (2021)
    Oxford University Press
    Details
    Publikationsdatum: 2024-02-07
    Beschreibung: To constrain seismic anisotropy under and around the Alps in Europe, we study SKS shear wave splitting from the region densely covered by the AlpArray seismic network. We apply a technique based on measuring the splitting intensity, constraining well both the fast orientation and the splitting delay. Four years of teleseismic earthquake data were processed, from 723 temporary and permanent broad-band stations of the AlpArray deployment including ocean-bottom seismometers, providing a spatial coverage that is unprecedented. The technique is applied automatically (without human intervention), and it thus provides a reproducible image of anisotropic structure in and around the Alpine region. As in earlier studies, we observe a coherent rotation of fast axes in the western part of the Alpine chain, and a region of homogeneous fast orientation in the Central Alps. The spatial variation of splitting delay times is particularly interesting though. On one hand, there is a clear positive correlation with Alpine topography, suggesting that part of the seismic anisotropy (deformation) is caused by the Alpine orogeny. On the other hand, anisotropic strength around the mountain chain shows a distinct contrast between the Western and Eastern Alps. This difference is best explained by the more active mantle flow around the Western Alps. The new observational constraints, especially the splitting delay, provide new information on Alpine geodynamics.
    Materialart: Article , PeerReviewed
    Format: text
    Format: archive
    Standort Signatur Erwartet Verfügbarkeit
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    ARTIKEL (OCEANREP)
  • 3
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    Seawater and sediment data from offshore wind farms in the German Bight collected during the cruise AT275 (2022)
    PANGAEA
    Details
    Publikationsdatum: 2024-03-05
    Beschreibung: Offshore wind energy is a steadily growing sector contributing to the worldwide energy production. The impact of these offshore constructions on the marine environment, however, remains unclear in many aspects. In fact, little is known about potential emissions from corrosion protection systems such as organic coatings or galvanic anodes composed of Al and Zn alloys, used to protect offshore structures. In order to assess potential chemical emissions from offshore wind farms and their impact on the marine environment water and sediment samples were taken in and around offshore wind farms of the German Bight between 06.03.2019 and 24.03.2019.
    Schlagwort(e): Helmholtz-Zentrum Hereon; Hereon
    Materialart: Dataset
    Format: application/zip, 2 datasets
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN PANGAEA
  • 4
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    Nitrous oxide fluxes from permafrost regions (2020)
    PANGAEA
    Details
    Publikationsdatum: 2024-02-24
    Beschreibung: This dataset is a synthesis of published nitrous oxide (N2O) fluxes from permafrost-affected soils in Arctic, Antarctic, and Alpine permafrost regions. The data includes mean N2O flux rates measured under field (in situ) conditions and in intact plant-soil systems (mesocosms) under near-field conditions. The dataset further includes explanatory environmental parameters such as meteorological data, soil physical-chemical properties, as well as site and experimental information. Data has been synthesized from published studies (see 'Further details'), and in some cases the authors of published studies have been contacted for additional site-level information. The dataset includes studies published until 2019. We encourage linking additional N2O flux data from unpublished and future studies with similar metadata structure to this dataset, to produce a comprehensive, findable database for N2O fluxes from permafrost regions.
    Schlagwort(e): Abisko_N2O; Alexandra_Fjord_N2O; Ammonium; Analytical method; Antarctica; Ardley_Island_N2O; Area/locality; Boniface_River_N2O; Canada; Cape_Bounty_N2O; Carbon/Nitrogen ratio; China; Churchill_N2O; Country; Daring_Lake_N2O; Daxing-an_Mountains_N2O; Day; Denmark; Density, active layer, bulk; Disturbance Type; Dome_Desert_N2O; Eagle_Plains_N2O; Eboling_Mountains_N2O; Ecosystem; Event label; Expedition_Fjord_N2O; Experimental treatment; Fenghuo_Mountains_N2O; Fildes_Peninsula_N2O; Finland; Garwood_Valley_N2O; Geermu_N2O; Great_Hing-an_Mountains_N2O; Haibei_N2O; Hemeroby/disturbance; Inner_Mongolia_N2O; Kilpisjaervi_N2O; LATITUDE; Location; LONGITUDE; Luanhaizi_N2O; Month; Nagqu_N2O; Nitrate; Nitrogen, soil; Nitrous oxide, flux, in mass nitrous oxide; Niwot_Ridge_N2O; Norway; Number of measurements; Number of measurement seasons; Number of points; Ny-Alesund_N2O; Okse_Bay_N2O; Organic carbon, soil; Original unit; Original value; Patterson_River_N2O; Permafrost extent; pH, soil; Precipitation, annual mean; Presence/absence; Publication of data; Reference of data; Replicates; Russia; Sample code/label; Seida_I_N2O; Seida_II_N2O; Site; Sodankylae_N2O; Soil moisture; Soil organic matter; Soil water content, gravimetric; Soil water content, volumetric; Sweden; Temperature, air; Temperature, air, annual mean; Temperature, soil; Thaw depth of active layer, maximum; Thaw depth of active layer, mean; Time in minutes; Truelove_Lowland_N2O; Tura_N2O; Type of chamber; Type of study; United States of America; Utsjoki_N2O; Vegetation type; Water filled pore space; Water filled pore space, calculated; Water holding capacity; Wudaoliang_N2O; Yakutsk_N2O; Year of observation; Yukon_Delta_N2O; Zackenberg_N2O; Zone
    Materialart: Dataset
    Format: text/tab-separated-values, 10302 data points
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN PANGAEA
  • 5
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    DLR in-situ cloud measurements during MOSAiC-ACA Arctic airborne campaign (2022)
    PANGAEA
    Details
    Publikationsdatum: 2024-05-17
    Beschreibung: During MOSAiC-ACA field campaign in late summer 2020 the Basler BT-67 research aircraft Polar 5 based in Spitzbergen (78.24 N, 15.49 E) was equiped with an advanced in-situ cloud payload by the DLR including a combination of the Cloud Droplet Probe, Cloud Imaging Probe and Precipitation Imaging Probe. The data sets provides data from all DLR particle measurement instruments including micropysical cloud properties like particle size distribution, total particle number concentration, effective diameter, median volume diameter and an estimated cloud/liquid/ice water content. In combination the dataset includes all particle sizes from 2.8 - 6400.0µm in diameter. In addition to the particle measurement systems the Nevzorov probe provides bulk measurements of the liquid and total water content. These cloud measurements were mainly conducted in low and midlevel clouds in the Fram Strait over sea ice and the open ocean. This measurement campaign is embedded in the Transregional Collaborative Research Centre TR 172 (ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3.
    Schlagwort(e): AC; AC3; Aircraft; Arctic; Arctic Amplification; Binary Object; Binary Object (File Size); CDP; CIP; Cloud droplet probe; Cloud imaging probe; Cloud Microphysics; clouds; Date/Time of event; Date/Time of event 2; Event label; Fram Strait; In-situ; In-Situ Measurements; Latitude of event; Longitude of event; mixed-phase clouds; MOSAiC; MOSAiC20192020; MOSAiC-ACA; Multidisciplinary drifting Observatory for the Study of Arctic Climate; NEVZ; Nevzorov probe; P5_223_MOSAiC_ACA_2020_2008310301; P5_223_MOSAiC_ACA_2020_2009020501; P5_223_MOSAiC_ACA_2020_2009040601; P5_223_MOSAiC_ACA_2020_2009070701; P5_223_MOSAiC_ACA_2020_2009080801; P5_223_MOSAiC_ACA_2020_2009100901; P5_223_MOSAiC_ACA_2020_2009111001; P5_223_MOSAiC_ACA_2020_2009131101; P5-223_MOSAiC_ACA_2020; Particle measurement system; PIP; PMS; POLAR 5; Precipitation imaging probe; Svalbard
    Materialart: Dataset
    Format: text/tab-separated-values, 40 data points
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN PANGAEA
  • 6
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    Metal distribution and Sr and Pb stable isotope ratios for sediment samples of Atair cruise AT275 (2023)
    PANGAEA
    Details
    Publikationsdatum: 2024-06-25
    Beschreibung: Offshore wind energy is a steadily growing sector contributing to the worldwide energy production. The impact of these offshore constructions on the marine environment, however, remains unclear in many aspects. In fact, little is known about potential emissions from corrosion protection systems such as organic coatings or galvanic anodes composed of Al and Zn alloys, used to protect offshore structures. In order to assess potential chemical emissions from offshore wind farms and their impact on the marine environment water and sediment samples were taken in and around offshore wind farms of the German Bight between 06.03.2019 and 24.03.2019 within the context of the Hereon-BSH project OffChEm. The surface sediment samples were taken by a box grab, homogenized, freeze-dried and wet-sieved to gain the 〈20 µm grain size fraction. The 〈20 µm grain size fraction was acid digested and measured by ICP-MS/MS for their (trace) metal mass fractions. The Sr and Pb isotope ratios were measured by MC ICP-MS after an automated matrix separation with a prepFAST MCTM system.
    Schlagwort(e): Aluminium; Aluminium, limit of detection; Aluminium, limit of quantification; Aluminium, uncertainty; Arsenic; Arsenic, limit of detection; Arsenic, limit of quantification; Arsenic, uncertainty; AT275; AT275_Stat_S_097_HELW5; Atair; Atair275; Atair275_11; Atair275_12; Atair275_13; Atair275_14; Atair275_17; Atair275_18; Atair275_19; Atair275_2; Atair275_20; Atair275_21; Atair275_22; Atair275_23; Atair275_24; Atair275_25; Atair275_26; Atair275_27; Atair275_28; Atair275_29; Atair275_30; Atair275_31; Atair275_32; Atair275_33; Atair275_34; Atair275_35; Atair275_36; Atair275_39; Atair275_4; Atair275_40; Atair275_41; Atair275_42; Atair275_43; Atair275_44; Atair275_45; Atair275_46; Atair275_47; Atair275_48; Atair275_49; Atair275_5; Atair275_52; Atair275_53; Atair275_54; Atair275_55; Atair275_56; Atair275_57; Atair275_58; Atair275_60; Atair275_61; Atair275_62; Atair275_64; Atair275_65; Atair275_67; Atair275_68; Atair275_69; Atair275_7; Atair275_70; Atair275_71; Atair275_72; Atair275_73; Atair275_75; Atair275_78; Atair275_79; Atair275_8; Atair275_80; Atair275_81; Atair275_82; Atair275_83; Atair275_84; Atair275_85; Atair275_86; Atair275_87; Atair275_88; Atair275_89; Atair275_9; Atair275_91; Atair275_92; Atair275_93; Atair275_94; Atair275_95; Atair275_96; Atair275_97; Barium; Barium, limit of detection; Barium, limit of quantification; Barium, uncertainty; Beryllium; Beryllium, limit of detection; Beryllium, limit of quantification; Beryllium, uncertainty; Bismuth; Bismuth, limit of detection; Bismuth, limit of quantification; Bismuth, uncertainty; Cadmium; Cadmium, limit of detection; Cadmium, limit of quantification; Cadmium, uncertainty; Caesium; Caesium, limit of detection; Caesium, limit of quantification; Caesium, uncertainty; Calcium; Calcium, limit of detection; Calcium, limit of quantification; Calcium, uncertainty; Cerium; Cerium, limit of detection; Cerium, limit of quantification; Cerium, uncertainty; Chromium; Chromium, limit of detection; Chromium, limit of quantification; Chromium, uncertainty; Cobalt; Cobalt, limit of detection; Cobalt, limit of quantification; Cobalt, uncertainty; DEPTH, sediment/rock; Dysprosium; Dysprosium, limit of detection; Dysprosium, limit of quantification; Dysprosium, uncertainty; Element analysis grain size fraction 〈 20 microns via ICP-MS (total digest); Erbium; Erbium, limit of detection; Erbium, limit of quantification; Erbium, uncertainty; Europium; Europium, limit of detection; Europium, limit of quantification; Europium, uncertainty; Event label; Gadolinium; Gadolinium, limit of detection; Gadolinium, limit of quantification; Gadolinium, uncertainty; Gallium; Gallium, limit of detection; Gallium, limit of quantification; Gallium, uncertainty; Germanium; Germanium, limit of detection; Germanium, limit of quantification; Germanium, uncertainty; Helmholtz-Zentrum Hereon; Hereon; Holmium; Holmium, limit of detection; Holmium, limit of quantification; Holmium, uncertainty; Indium; Indium, limit of detection; Indium, limit of quantification; Indium, uncertainty; International Generic Sample Number; Iron; Iron, limit of detection; Iron, limit of quantification; Iron, uncertainty; Lanthanum; Lanthanum, limit of detection; Lanthanum, limit of quantification; Lanthanum, uncertainty; Lead; Lead, limit of detection; Lead, limit of quantification; Lead, uncertainty; Lead-206/Lead-204 ratio; Lead-206/Lead-204 ratio, uncertainty; Lead-207/Lead-204 ratio; Lead-207/Lead-204 ratio, uncertainty; Lead-207/Lead-206 ratio; Lead-207/Lead-206 ratio, uncertainty; Lead-208/Lead-204 ratio; Lead-208/Lead-204 ratio, uncertainty; Lead-208/Lead-206 ratio; Lead-208/Lead-206 ratio, uncertainty; Lead-208/Lead-207 ratio; Lead-208/Lead-207 ratio, uncertainty; Lithium; Lithium, limit of detection; Lithium, limit of quantification; Lithium, uncertainty; Lutetium; Lutetium, limit of detection; Lutetium, limit of quantification; Lutetium, uncertainty; Magnesium, limit of detection; Magnesium, limit of quantification; Magnesium, uncertainty; Manganese; Manganese, limit of detection; Manganese, limit of quantification; Manganese, uncertainty; Mercury; Mercury, limit of detection; Mercury, limit of quantification; Mercury, uncertainty; Molybdenum; Molybdenum, limit of detection; Molybdenum, limit of quantification; Molybdenum, uncertainty; MULT; Multi-collector ICP-MS (MC-ICP-MS), Nu Plasma II, Wrexham, UK; External intra-elemental calibration using NIST SRM 981; Multi-collector ICP-MS (MC-ICP-MS), Nu Plasma II, Wrexham, UK; External intra-elemental calibration using NIST SRM 987; Multiple investigations; Neodymium; Neodymium, limit of detection; Neodymium, limit of quantification; Neodymium, uncertainty; Nickel; Nickel, limit of detection; Nickel, limit of quantification; Nickel, uncertainty; Niobium; Niobium, limit of detection; Niobium, limit of quantification; Niobium, uncertainty; North Sea; Phosphorus; Phosphorus, limit of detection; Phosphorus, limit of quantification; Phosphorus, uncertainty; Potassium; Potassium, limit of detection; Potassium, limit of quantification; Potassium, uncertainty; Praseodymium; Praseodymium, limit of detection; Praseodymium, limit of quantification; Praseodymium, uncertainty; Rubidium; Rubidium, limit of detection; Rubidium, limit of quantification; Rubidium, uncertainty; S_002_AMWE4; S_004_AMWE3; S_005_AMWE7; S_007_AMWE5; S_008_AMWE6; S_009_AMWE15; S_011_AMWE19; S_012_AMWE20; S_013_AMWE21; S_014_AMWE22; S_017_NOST4; S_018_NOST1; S_019_NOST5; S_020_NOST6; S_021_NOST7; S_022_NOST3; S_023_NOST42; S_024_NOST43; S_025_NOST35; S_026_TI7; S_027_MEWI1; S_028_MEWI3; S_029_MEWI6; S_030_TI13; S_031_MEWI7; S_032_MEWI36; S_033_MEWI37; S_034_MEWI38; S_035_MEWI40; S_036_MEWI41; S_039_DOLW1; S_040_ALVE5; S_041_ALVE4; S_042_ALVE2; S_043_ALVE3; S_044_ALVE1; S_045_BKRI5; S_046_BKRI4; S_047_BKRI3; S_048_BKRI2; S_049_BKRI1; S_052_GOWI10; S_053_GOWI6; S_054_GOWI7; S_055_GOWI9; S_056_GOWI11; S_057_GOWI4; S_058_GOWI3; S_060_GOWI2; S_061_GOWI1; S_062_GOWI8; S_064_GOWI54; S_065_GOWI59; S_067_GOWI26; S_068_GOWI24; S_069_GOWI21; S_070_GOWI25; S_071_GOWI20; S_072_GOWI22; S_073_GOWI23; S_075_GOWI29; S_078_GOWI55; S_079_GOWI57; S_080_DOLW7; S_081_VEJA02; S_082_VEJA03; S_083_VEJA04; S_084_VEJA05; S_085_VEJA06; S_086_VEJA08; S_087_VEJA09; S_088_VEJA10; S_089_VEJA11; S_091_DOLW8; S_092_DOLW10; S_093_DOLW9; S_094_VEJA16; S_095_HELW1; S_096_HELW4; Samarium; Samarium, limit of detection; Samarium, limit of quantification; Samarium, uncertainty; Sample code/label; Sample method; Scandium; Scandium, limit of detection; Scandium, limit of quantification; Scandium, uncertainty; Selenium; Selenium, limit of detection; Selenium, limit of quantification; Selenium, uncertainty; Silver; Silver, limit of detection; Silver, limit of quantification; Silver, uncertainty; Sodium; Sodium, limit of detection; Sodium, limit of quantification; Sodium, uncertainty; Station label; Strontium; Strontium, limit of detection; Strontium, limit of quantification; Strontium, uncertainty; Strontium-87/Strontium-86 ratio; Strontium-87/Strontium-86 ratio, uncertainty; Tantalum; Tantalum, limit of detection; Tantalum, limit of quantification; Tantalum, uncertainty; Tellurium; Tellurium, limit of detection; Tellurium, limit of quantification; Tellurium, uncertainty; Terbium; Terbium, limit of detection; Terbium, limit of quantification; Terbium, uncertainty; Thallium; Thallium, limit of detection; Thallium, limit of quantification; Thallium, uncertainty; Thorium; Thorium, limit of detection; Thorium, limit of quantification; Thorium, uncertainty; Thulium; Thulium, limit of detection; Thulium, limit of quantification; Thulium, uncertainty; Titanium; Titanium, limit of detection; Titanium, limit of quantification; Titanium, uncertainty; Tungsten; Tungsten, limit of detection; Tungsten, limit of quantification; Tungsten, uncertainty; Uranium; Uranium, limit of detection; Uranium, limit of quantification; Uranium, uncertainty; Vanadium; Vanadium, limit of detection; Vanadium, limit
    Materialart: Dataset
    Format: text/tab-separated-values, 17568 data points
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN PANGAEA
  • 7
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    DLR in situ cloud measurements during HALO-(AC)³ Arctic airborne campaign (2023)
    PANGAEA
    Details
    Publikationsdatum: 2024-06-25
    Beschreibung: During the HALO-(AC)³ field campaign in spring 2022, the Basler BT-67 research aircraft Polar 6, based in Spitzbergen (78.24 N, 15.49 E), was equipped with an advanced in situ cloud payload by the DLR. This payload contained a combination of cloud instruments, including the Cloud Droplet Probe (CDP), the Cloud Imaging Probe (CIP), and the Precipitation Imaging Probe (PIP). The published data contain the particle size distributions measured by each particle measurement system. The respective instruments operate in different size ranges, and by combining their data, an additional data set is calculated that covers cloud particles in the size range from 2.8 µm to 6400 µm. Microphysical cloud properties such as cloud particle number concentration, liquid water content, ice water content, and effective diameter are derived from the given particle size distributions. The in situ cloud measurements focused on low and mid-level clouds in the Fram Strait, over the sea ice and the open ocean. The measurement campaign is embedded in the Transregional Collaborative Research Centre TR 172 (ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)³).
    Schlagwort(e): AC; AC3; Aircraft; Arctic; Arctic Amplification; CDP; CIP; Cloud droplet probe; Cloud imaging probe; Date/Time of event; Event label; HALO - (AC)3; HALO-AC3_20220320_P6_RF01; HALO-AC3_20220322_P6_RF02; HALO-AC3_20220324_P6_RF03; HALO-AC3_20220326_P6_RF04; HALO-AC3_20220328_P6_RF05; HALO-AC3_20220329_P6_RF06; HALO-AC3_20220330_P6_RF07; HALO-AC3_20220401_P6_RF08; HALO-AC3_20220404_P6_RF09; HALO-AC3_20220405_P6_RF10; HALO-AC3_20220408_P6_RF11; HALO-AC3_20220409_P6_RF12; HALO-AC3_20220410_P6_RF13; netCDF file; P6_231_HALO_2022_2203200401; P6_231_HALO_2022_2203220501; P6_231_HALO_2022_2203240601; P6_231_HALO_2022_2203260702; P6_231_HALO_2022_2203280801; P6_231_HALO_2022_2203290901; P6_231_HALO_2022_2203301001; P6_231_HALO_2022_2204011101; P6_231_HALO_2022_2204041201; P6_231_HALO_2022_2204051301; P6_231_HALO_2022_2204081401; P6_231_HALO_2022_2204091501; P6_231_HALO_2022_2204101601; P6-231_HALO_2022; PIP; POLAR 6; Precipitation imaging probe
    Materialart: Dataset
    Format: text/tab-separated-values, 52 data points
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN PANGAEA
  • 8
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    Unbekannt
    Backscatter Cloud Probe with Polarization Detection (2023)
    PANGAEA
    Details
    Publikationsdatum: 2024-06-25
    Beschreibung: This dataset contains the processed and raw data collected with the Backscatter Cloud Probe with Polarization Detection during the HALO-AC³ campaign in March and April 2022 with the Polar 6 Aircraft out of Longyearbyen, Svalbard. The dataset contains two kinds of data. Data to which no inversion procedure has been applied and data to which the inversion procedure has been applied. The inversion procedure is applied to account for an uneven intensity of the laser beam across the sample area and resulting undersizing effects. The inversion procedure has been discussed in Lucke et al. (2023) (doi.org/10.4271/2023-01-1485) and Beswick et al. (2014) (doi.org/10.5194/amt-7-1443-2014). All quantities which carry the suffix inv are based on the inverted data, all other properties are not. It should be noted, that the necessity of the inversion procedure remains unclear (see the previously mentioned publications). The inversion procedure could only be applied when more than 2000 particles were present over a 5 second interval. When this was not the case, the inverted data are 9999.999. The inverted data are therefore also computed from a 5s rolling average. The measurements of the BCPD are likely severely influenced by inertial separation effects, due to the proximity of the BCPD sample area to the fuselage (approx. 3cm). When ice particles are present, shattering occurs on the fuselage and artificially increases the ice number concentration. The number of ice and liquid particles listed in this data set can be useful for assessing the presence of ice and liquid particles. To estimate the number of liquid and ice particles more than 100 particles are required over a 5s interval. When this is not the case, the data are 9999.999. The number of ice and liquid particles were computed as rolling averages over 5s intervals. The sample area in case no inversion procedure is applied is 0.273 square millimeters.
    Schlagwort(e): AC; Aircraft; Arctic; Backscatter Cloud Probe with Polarization Detection; BCPD; Date/Time of event; Event label; HALO - (AC)3; HALO-(AC)³; HALO-AC3_20220320_P6_RF01; HALO-AC3_20220322_P6_RF02; HALO-AC3_20220326_P6_RF04; HALO-AC3_20220328_P6_RF05; HALO-AC3_20220329_P6_RF06; HALO-AC3_20220330_P6_RF07; HALO-AC3_20220401_P6_RF08; HALO-AC3_20220404_P6_RF09; HALO-AC3_20220405_P6_RF10; HALO-AC3_20220408_P6_RF11; HALO-AC3_20220409_P6_RF12; HALO-AC3_20220410_P6_RF13; mixed-phase clouds; netCDF file; netCDF file (File Size); P6_231_HALO_2022_2203200401; P6_231_HALO_2022_2203220501; P6_231_HALO_2022_2203260702; P6_231_HALO_2022_2203280801; P6_231_HALO_2022_2203290901; P6_231_HALO_2022_2203301001; P6_231_HALO_2022_2204011101; P6_231_HALO_2022_2204041201; P6_231_HALO_2022_2204051301; P6_231_HALO_2022_2204081401; P6_231_HALO_2022_2204091501; P6_231_HALO_2022_2204101601; P6-231_HALO_2022; Particle size distributions; Phase differentiation; POLAR 6; Polarimetric Radar Observations meet Atmospheric Modelling (PROM) - Fusion of Radar Polarimetry and Numerical Atmospheric Modelling Towards an Improved Understanding of Cloud and Precipitation Processes; SPP2115_PROM; Svalbard
    Materialart: Dataset
    Format: text/tab-separated-values, 12 data points
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN PANGAEA
  • 9
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    Nevzorov LWC and TWC data from the HALO-AC3 campaign in March and April 2022 (2024)
    PANGAEA
    Details
    Publikationsdatum: 2024-06-25
    Beschreibung: Liquid water content and total water content from the Nevzorov probe, collected during the HALO-AC³ campaign out of Longyearbyen, Svalbard in April 2022. The dataset contains measurements from the three collector sensors of the Nevzorov probe. These are the cylindrical LWC sensor, the 8 mm TWC cone and the 12 mm TWC cone (for a description of the probe see doi:10.1175/1520-0426(1998)015〈1495:TNAHWL〉2.0.CO;2, doi:10.5194/egusphere-2022-647 ). Furthermore, corrected LWC and TWC values are contained in the dataset. These values are best estimates of LWC and TWC. They are computed by solving a system of equations and they consider collection efficiencies, the different latent heats of water and ice and the sensitivity of the LWC sensor to ice particles. For a description of the computation see Lucke et al. (2023) (doi:10.4271/2023-01-1485). However, for this data, the 12 mm cone was not included in the computation, as its data were deemed to be too unreliable in conditions where droplet diameters are low. NaNs are represented as 9999.999 in the dataset. The dataset only contains research flight 8 - 13. For the previous flights a problem with the probe existed and no data was recorded.
    Schlagwort(e): AC; Aircraft; Arctic; Arctic Amplification; Date/Time of event; Event label; HALO - (AC)3; HALO-(AC)³; HALO-AC3_20220401_P6_RF08; HALO-AC3_20220404_P6_RF09; HALO-AC3_20220405_P6_RF10; HALO-AC3_20220408_P6_RF11; HALO-AC3_20220409_P6_RF12; HALO-AC3_20220410_P6_RF13; ice water content; IWC; liquid water content; LWC; mixed-phase clouds; netCDF file; netCDF file (File Size); NEVZ; Nevzorov probe; P6_231_HALO_2022_2204011101; P6_231_HALO_2022_2204041201; P6_231_HALO_2022_2204051301; P6_231_HALO_2022_2204081401; P6_231_HALO_2022_2204091501; P6_231_HALO_2022_2204101601; P6-231_HALO_2022; Polar 6; POLAR 6; Polarimetric Radar Observations meet Atmospheric Modelling (PROM) - Fusion of Radar Polarimetry and Numerical Atmospheric Modelling Towards an Improved Understanding of Cloud and Precipitation Processes; SPP2115_PROM; Svalbard; total water content; TWC
    Materialart: Dataset
    Format: text/tab-separated-values, 6 data points
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN PANGAEA
  • 10
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    Trace metal distribution for water samples of Atair cruise AT275 (2023)
    PANGAEA
    Details
    Publikationsdatum: 2024-06-25
    Beschreibung: Offshore wind energy is a steadily growing sector contributing to the worldwide energy production. The impact of these offshore constructions on the marine environment, however, remains unclear in many aspects. In fact, little is known about potential emissions from corrosion protection systems such as organic coatings or galvanic anodes composed of Al and Zn alloys, used to protect offshore structures. In order to assess potential chemical emissions from offshore wind farms and their impact on the marine environment water and sediment samples were taken in and around offshore wind farms of the German Bight between 06.03.2019 and 24.03.2019 within the context of the Hereon-BSH project OffChEm. The water samples were taken in metal-free GO-FLO sampling bottles, filtered over 〈0.45 µm polycarbonate filters into pre-cleaned LDPE bottles and acidified with nitric acid. The filtrates were then measured for their (trace) metal concentrations with ICP-MS/MS coupled online to a seaFAST preconcentration and matrix removal system.
    Schlagwort(e): Aluminium; Aluminium, standard deviation; AT275; AT275_Stat_S_097_HELW5; Atair; Atair275; Atair275_11; Atair275_12; Atair275_13; Atair275_14; Atair275_15; Atair275_16; Atair275_17; Atair275_18; Atair275_19; Atair275_2; Atair275_20; Atair275_21; Atair275_22; Atair275_23; Atair275_24; Atair275_25; Atair275_26; Atair275_27; Atair275_28; Atair275_29; Atair275_30; Atair275_31; Atair275_32; Atair275_33; Atair275_34; Atair275_35; Atair275_36; Atair275_39; Atair275_4; Atair275_40; Atair275_41; Atair275_42; Atair275_43; Atair275_44; Atair275_45; Atair275_46; Atair275_47; Atair275_48; Atair275_49; Atair275_5; Atair275_52; Atair275_53; Atair275_54; Atair275_55; Atair275_56; Atair275_57; Atair275_58; Atair275_6; Atair275_60; Atair275_61; Atair275_64; Atair275_65; Atair275_67; Atair275_68; Atair275_69; Atair275_7; Atair275_70; Atair275_71; Atair275_72; Atair275_73; Atair275_75; Atair275_78; Atair275_79; Atair275_8; Atair275_80; Atair275_81; Atair275_82; Atair275_83; Atair275_84; Atair275_85; Atair275_86; Atair275_87; Atair275_88; Atair275_89; Atair275_9; Atair275_90; Atair275_91; Atair275_92; Atair275_93; Atair275_94; Atair275_95; Atair275_96; Atair275_97; Cadmium; Cadmium, standard deviation; Cerium; Cerium, standard deviation; Cobalt; Cobalt, standard deviation; Copper; Copper, standard deviation; Date/Time of event; DEPTH, water; Dysprosium; Dysprosium, standard deviation; Elevation of event; Erbium; Erbium, standard deviation; Europium; Europium, standard deviation; Event label; Gadolinium; Gadolinium, anthropogenic; Gadolinium, anthropogenic, uncertainty; Gadolinium, standard deviation; Gadolinium anomaly; Gadolinium anomaly, uncertainty; Gallium; Gallium, standard deviation; Helmholtz-Zentrum Hereon; Hereon; Holmium; Holmium, standard deviation; ICP-MS, Elemental Scientific, seaFAST; Indium; Indium, standard deviation; International Generic Sample Number; Iron; Iron, standard deviation; Lanthanum; Lanthanum, standard deviation; Latitude of event; Lead; Lead, standard deviation; Longitude of event; Lutetium; Lutetium, standard deviation; Manganese; Manganese, standard deviation; Molybdenum; Molybdenum, standard deviation; MULT; Multiple investigations; Neodymium; Neodymium, standard deviation; Nickel; Nickel, standard deviation; North Sea; Praseodymium; Praseodymium, standard deviation; Quality assessment; S_002_AMWE4; S_004_AMWE3; S_005_AMWE7; S_006_ANWE8; S_007_AMWE5; S_008_AMWE6; S_009_AMWE15; S_011_AMWE19; S_012_AMWE20; S_013_AMWE21; S_014_AMWE22; S_015_NOST4_WH; S_016_HELW1_WH; S_017_NOST4; S_018_NOST1; S_019_NOST5; S_020_NOST6; S_021_NOST7; S_022_NOST3; S_023_NOST42; S_024_NOST43; S_025_NOST35; S_026_TI7; S_027_MEWI1; S_028_MEWI3; S_029_MEWI6; S_030_TI13; S_031_MEWI7; S_032_MEWI36; S_033_MEWI37; S_034_MEWI38; S_035_MEWI40; S_036_MEWI41; S_039_DOLW1; S_040_ALVE5; S_041_ALVE4; S_042_ALVE2; S_043_ALVE3; S_044_ALVE1; S_045_BKRI5; S_046_BKRI4; S_047_BKRI3; S_048_BKRI2; S_049_BKRI1; S_052_GOWI10; S_053_GOWI6; S_054_GOWI7; S_055_GOWI9; S_056_GOWI11; S_057_GOWI4; S_058_GOWI3; S_060_GOWI2; S_061_GOWI1; S_064_GOWI54; S_065_GOWI59; S_067_GOWI26; S_068_GOWI24; S_069_GOWI21; S_070_GOWI25; S_071_GOWI20; S_072_GOWI22; S_073_GOWI23; S_075_GOWI29; S_078_GOWI55; S_079_GOWI57; S_080_DOLW7; S_081_VEJA02; S_082_VEJA03; S_083_VEJA04; S_084_VEJA05; S_085_VEJA06; S_086_VEJA08; S_087_VEJA09; S_088_VEJA10; S_089_VEJA11; S_090_VEJA12; S_091_DOLW8; S_092_DOLW10; S_093_DOLW9; S_094_VEJA16; S_095_HELW1; S_096_HELW4; Samarium; Samarium, standard deviation; Sample code/label; Station label; Terbium; Terbium, standard deviation; Thulium; Thulium, standard deviation; Tin; Tungsten; Tungsten, standard deviation; Uranium; Uranium, standard deviation; Vanadium; Vanadium, standard deviation; Ytterbium; Ytterbium, standard deviation; Yttrium; Yttrium, standard deviation; Zinc; Zinc, standard deviation
    Materialart: Dataset
    Format: text/tab-separated-values, 5499 data points
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN PANGAEA
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