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  • PANGAEA  (60)
  • Wiley  (4)
  • American Meteorological Society  (1)
  • American Physical Society
  • 2020-2024  (65)
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Year
  • 1
    Publication Date: 2024-02-07
    Description: We present the results of P-to-S receiver function analysis to improve the 3D image of the sedimentary layer, the upper crust, and lower crust in the Pannonian Basin area. The Pannonian Basin hosts deep sedimentary depocentres superimposed on a complex basement structure and it is surrounded by mountain belts. We processed waveforms from 221 three-component broadband seismological stations. As a result of the dense station coverage, we were able to achieve so far unprecedented spatial resolution in determining the velocity structure of the crust. We applied a three-fold quality control process; the first two being applied to the observed waveforms and the third to the calculated radial receiver functions. This work is the first comprehensive receiver function study of the entire region. To prepare the inversions, we performed station-wise H-Vp/Vs grid search, as well as Common Conversion Point migration. Our main focus was then the S-wave velocity structure of the area, which we determined by the Neighborhood Algorithm inversion method at each station, where data were sub-divided into back-azimuthal bundles based on similar Ps delay times. The 1D, nonlinear inversions provided the depth of the discontinuities, shear-wave velocities and Vp/Vs ratios of each layer per bundle, and we calculated uncertainty values for each of these parameters. We then developed a 3D interpolation method based on natural neighbor interpolation to obtain the 3D crustal structure from the local inversion results. We present the sedimentary thickness map, the first Conrad depth map and an improved, detailed Moho map, as well as the first upper and lower crustal thickness maps obtained from receiver function analysis. The velocity jump across the Conrad discontinuity is estimated at less than 0.2 km/s over most of the investigated area. We also compare the new Moho map from our approach to simple grid search results and prior knowledge from other techniques. Our Moho depth map presents local variations in the investigated area: the crust-mantle boundary is at 20–26 km beneath the sedimentary basins, while it is situated deeper below the Apuseni Mountains, Transdanubian and North Hungarian Ranges (28–33 km), and it is the deepest beneath the Eastern Alps and the Southern Carpathians (40–45 km). These values reflect well the Neogene evolution of the region, such as crustal thinning of the Pannonian Basin and orogenic thickening in the neighboring mountain belts.
    Type: Article , PeerReviewed
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  • 2
    Publication Date: 2024-02-23
    Description: The understanding of silicate weathering and its role as a sink for atmospheric CO 2 is important to get a better insight into how the Earth shifts from warm to cool climates. The lithium isotope composition (δ 7 Li) of marine carbonates can be used as a proxy to track the past chemical weathering of silicates. A high‐resolution δ 7 Li record would be helpful to evaluate the role of silicate weathering during the late Cretaceous climate cooling. Here, we assess chalk as a potential archive for reconstructing Late Cretaceous seawater Li isotope composition by comparing Maastrichtian chalk from Northern Germany (Hemmoor, Kronsmoor) to a Quaternary coccolith ooze from the Manihiki Plateau (Pacific Ocean) as a lithological analog to modern conditions. We observe a negative offset of 3.9 ± 0.6‰ for the coccolith ooze relative to the modern seawater Li isotope composition (+31.1 ± 0.3‰; 2SE; n = 54), a value that falls in the range of published offsets for modern core‐top samples and for brachiopod calcite. Further, the negative offset between the Li isotope compositions of Manihiki coccolith ooze and modern planktonic foraminifera is 2.3 ± 0.6‰. Although chalk represents a diagenetically altered modification of pelagic nannofossil ooze, manifested by changes in the composition of trace elements, we observe a consistent offset of Li isotope data between Maastrichtian chalk and Maastrichtian planktonic foraminiferal data (−1.4 ± 0. 5‰) that lies within the uncertainty of modern values. We therefore suggest that chalk can be used as a reliable archive for δ 7 Li reconstructions. Key Points Chalk is a reliable archive for the Li isotope composition of seawater Coccolith ooze has a negative offset of 3.9 ± 0.6‰ from modern seawater for Li isotope ratios The estimated mean value for the late Maastrichtian seawater Li isotope composition is +27.5 ± 1.0‰
    Type: Article , PeerReviewed
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  • 3
    Publication Date: 2024-03-05
    Description: 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.
    Keywords: Helmholtz-Zentrum Hereon; Hereon
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 4
    Publication Date: 2024-02-24
    Description: 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.
    Keywords: 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
    Type: Dataset
    Format: text/tab-separated-values, 10302 data points
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  • 5
    Publication Date: 2024-05-17
    Description: 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.
    Keywords: 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
    Type: Dataset
    Format: text/tab-separated-values, 40 data points
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  • 6
    Publication Date: 2024-06-25
    Description: 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.
    Keywords: 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
    Type: Dataset
    Format: text/tab-separated-values, 17568 data points
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  • 7
    Publication Date: 2024-06-25
    Description: 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).
    Keywords: 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
    Type: Dataset
    Format: text/tab-separated-values, 52 data points
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  • 8
    Publication Date: 2024-06-25
    Description: 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.
    Keywords: 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
    Type: Dataset
    Format: text/tab-separated-values, 12 data points
    Location Call Number Expected Availability
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  • 9
    Publication Date: 2024-06-25
    Description: 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.
    Keywords: 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
    Type: Dataset
    Format: text/tab-separated-values, 6 data points
    Location Call Number Expected Availability
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  • 10
    Publication Date: 2024-06-25
    Description: 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.
    Keywords: 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
    Type: Dataset
    Format: text/tab-separated-values, 5499 data points
    Location Call Number Expected Availability
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