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  • 1
    facet.materialart.
    Unknown
    Glyoxal- and formaldehyde slant column densities measured during the cruise MSM58/2 over the Atlantic Ocean using MAX-DOAS (2019)
    PANGAEA
    In:  Supplement to: Behrens, Lisa K; Hilboll, Andreas; Richter, Andreas; Peters, Enno; Alvarado, Leonardo M A; Kalisz Hedegaard, Anna Beata; Wittrock, Folkard; Burrows, John Philipp; Vrekoussis, Mihalis (2019): Detection of outflow of formaldehyde and glyoxal from the African continent to the Atlantic Ocean with a MAX-DOAS instrument. Atmospheric Chemistry and Physics, 19(15), 10257-10278, https://doi.org/10.5194/acp-19-10257-2019
    Details
    Publication Date: 2023-03-07
    Description: Trace gas maps retrieved from satellite measurements show enhanced levels of the atmospheric volatile organic compounds formaldehyde (HCHO) and glyoxal (CHOCHO) over the Atlantic Ocean. To validate the spatial distribution of this continental outflow, ship-based measurements were taken during the Continental Outflow of Pollutants towards the MArine tRoposphere (COPMAR) project. A Multi-AXis Differential Optical Absorption Spectrometer (MAX-DOAS) was operated aboard the research vessel (RV) Maria S. Merian during cruise MSM58/2. This cruise was conducted in October 2016 from Ponta Delgada (Azores) to Cape Town (South Africa), crossing between Cabo Verde and the African continent. The instrument was continuously scanning the horizon, looking towards the African continent. Enhanced levels of HCHO and CHOCHO were found in the area of expected outflow during this cruise. The observed spatial gradients of HCHO and CHOCHO along the cruise track agree with the spatial distributions from satellite measurements and the Model for OZone and Related chemical Tracers version 4 (MOZART-4) model simulations. The continental outflow from the African continent is observed in an elevated layer, higher than 1000 m, and probably originates from biogenic emissions or biomass burning according to FLEXible PARTicle dispersion model (FLEXPART) emission sensitivities.
    Type: Dataset
    Format: application/zip, 2 datasets
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 2
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    Unknown
    Phytoplankton pigment concentrations and phytoplankton groups measured on water samples collected from various expeditions in the Atlantic Ocean from 71°S to 84°N (2023)
    PANGAEA
    Details
    Publication Date: 2024-02-14
    Description: This data set composes a large amount of quality controlled in situ measurements of major pigments based on HPLC collected from various expeditions across the Atlantic Ocean spanning from 71°S to 84°N, including 11 expeditions with RV Polarstern from the North Atlantic to the Arctic Fram Strait: PS74, PSS76, PS78, PS80, PS85, PS93.2 (https://doi.org/10.1594/PANGAEA.894872), PS99.1 (https://doi.org/10.1594/PANGAEA.905502), PS99.2 ( https://doi.org/10.1594/PANGAEA.894874), PS106 (https://doi.org/10.1594/PANGAEA.899284), PS107 (https://doi.org/10.1594/PANGAEA.894860), PS121 (https://doi.org/10.1594/PANGAEA.941011), four expeditions (two with RV Polarstern and two Atlantic Meridional Transect expeditions with RRS James Clark Ross and RRS Discovery) in the trans-Atlantic Ocean: PS113 ( https://doi.org/10.1594/PANGAEA.911061), PS120, AMT28 and AMT29, and one expedition with RV Polarstern in the Southern Ocean: PS103 (https://doi.org/10.1594/PANGAEA.898941). Chlorophyll a concentration (Chl-a) of six phytoplankton functions groups (PFTs) derived from these pigments have been also included. This published data set has contributed to validate satellite PFT products available on the EU funded Copernicus Marine Service (CMEMS, https://marine.copernicus.eu/), which are derived from multi-sensor ocean colour reflectance data and sea surface temperature using an empirical orthogonal function based approach (Xi et al. 2020; 2021). Description on in situ PFT Chl-a determination from pigment data: PFT Chl-a in this data set were derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017) with retuned coefficients by Alvarado et al (2021), that was originally developed by Vidussi et al. (2001), adapted in Uitz et al. (2006) and further refined by Hirata et al. (2011) and Brewin et al. (2015). The values of retuned DPA weighting coefficients for PFT Chl-a determination are: 1.56 for fucoxanthin, 1.53 for peridinin, 0.89 for 19'-hexanoyloxyfucoxanthin, 0.44 for 19'-butanoyloxyfucoxanthin, 1.94 for alloxanthin, 2.63 for total chlorophyll b, and 0.99 for zeaxanthin. The coefficient retuning was based on an updated global HPLC pigment data base for the open ocean (water depth 〉200 m), which was compiled based on the previously published data sets spanning from 1988 to 2012 described in Losa et al. (2017), with updates in Xi et al. (2021) and Álvarez et al. (2022), by adding other newly available HPLC pigment data collected between 2012 and 2018 mainly from SeaBASS (https://seabass.gsfc.nasa.gov/), PANGAEA, British Oceanographic Data Centre (BODC, https://www.bodc.ac.uk/), and Australian Open Access to Ocean Data (AODN, https://portal.aodn.org.au/) (as of February 2020, see Table 1 attached in the 'Additional metadata' for more details on the data sources).
    Keywords: 19-Butanoyloxyfucoxanthin; 19-Hexanoyloxyfucoxanthin; AC3; Alloxanthin; AMT28; AMT28_10-33; AMT28_1-1; AMT28_11-36; AMT28_12-41; AMT28_13-44; AMT28_14-48; AMT28_15-50; AMT28_16-57; AMT28_17-58; AMT28_18-64; AMT28_19-66; AMT28_20-71; AMT28_21-73; AMT28_22-78; AMT28_23-80; AMT28_2-4; AMT28_24-85; AMT28_25-87; AMT28_27-93; AMT28_28-95; AMT28_29-100; AMT28_30-101; AMT28_31-105; AMT28_32-111; AMT28_33-112; AMT28_34-117; AMT28_35-120; AMT28_36-124; AMT28_37-126; AMT28_3-8; AMT28_38-133; AMT28_40-137; AMT28_4-11; AMT28_41-142; AMT28_43-147; AMT28_44-150; AMT28_45-155; AMT28_46-158; AMT28_47-164; AMT28_48-166; AMT28_49-174; AMT28_50-176; AMT28_51-181; AMT28_5-13; AMT28_52-183; AMT28_53-188; AMT28_54-190; AMT28_55-198; AMT28_56-199; AMT28_57-204; AMT28_58-206; AMT28_59-210; AMT28_59-212; AMT28_61-218; AMT28_6-17; AMT28_62-220; AMT28_63-226; AMT28_64-227; AMT28_65-232; AMT28_66-234; AMT28_7-21; AMT28_8-24; AMT28_9-28; AMT29; AMT29_AA; AMT29_AB; AMT29_AC; AMT29_AD; AMT29_AE; AMT29_AF; AMT29_AG; AMT29_AH; AMT29_AI; AMT29_AJ; AMT29_AK; AMT29_AL; AMT29_AM; AMT29_AN; AMT29_AO; AMT29_AP; AMT29_AQ; AMT29_AR; AMT29_AS; AMT29_AV; AMT29_AX; AMT29_BC; AMT29_BD; AMT29_BE; AMT29_BF; AMT29_BG; AMT29_BH; AMT29_BI; AMT29_BJ; AMT29_BK; AMT29_BL; AMT29_BM; AMT29_BN; AMT29_BO; AMT29_BP; AMT29_BQ; AMT29_BR; AMT29_BS; AMT29_BT; AMT29_BU; AMT29_BV; AMT29_BW; AMT29_BX; AMT29_BY; AMT29_BZ; AMT29_CA; AMT29_CB; AMT29_CC; AMT29_CD; AMT29_CE; AMT29_CF; AMT29_CG; AMT29_CH; AMT29_CJ; AMT29_CK; AMT29_CL; AMT29_CM; AMT29_CN; AMT29_CO; AMT29_CP; AMT29_CQ; AMT29_CR; AMT29_CS; AMT29_CT; AMT29_CTD_001; AMT29_CTD_002; AMT29_CTD_003; AMT29_CTD_004; AMT29_CTD_005; AMT29_CTD_006; AMT29_CTD_007; AMT29_CTD_008; AMT29_CTD_009; AMT29_CTD_010; AMT29_CTD_011; AMT29_CTD_013; AMT29_CTD_015; AMT29_CTD_016; AMT29_CTD_017; AMT29_CTD_018; AMT29_CTD_019; AMT29_CTD_020; AMT29_CTD_021; AMT29_CTD_022; AMT29_CTD_024; AMT29_CTD_025; AMT29_CTD_026; AMT29_CTD_027; AMT29_CTD_028; AMT29_CTD_029; AMT29_CTD_030; AMT29_CTD_031; AMT29_CTD_032; AMT29_CTD_034; AMT29_CTD_035; AMT29_CTD_036; AMT29_CTD_037; AMT29_CTD_038; AMT29_CTD_039; AMT29_CTD_041; AMT29_CTD_042; AMT29_CTD_043; AMT29_CTD_044; AMT29_CTD_045; AMT29_CTD_046; AMT29_CTD_047; AMT29_CTD_048; AMT29_CTD_049; AMT29_CTD_050; AMT29_CTD_051; AMT29_CTD_052; AMT29_CTD_053; AMT29_CTD_054; AMT29_CTD_055; AMT29_CU; AMT29_CV; AMT29_CW; AMT29_CX; AMT29_CY; AMT29_CZ; AMT29_DA; AMT29_DB; AMT29_DC; AMT29_DD; AMT29_DE; AMT29_DF; AMT29_DG; AMT29_DH; AMT29_DI; AMT29_DJ; AMT29_DK; AMT29_DL; AMT29_DM; AMT29_DN; AMT29_DO; AMT29_DP; AMT29_DQ; AMT29_DR; AMT29_DS; AMT29_DT; AMT29_DU; AMT29_DV; AMT29_DZ; AMT29_EB; AMT29_EC; AMT29_EE; AMT29_EF; AMT29_EG; AMT29_EI; AMT29_EK; AMT29_EL; AMT29_EM; AMT29_EO; AMT29_EQ; AMT29_ER; AMT29_ES; AMT29_ET; AMT29_EV; ANT-XXXII/2; ANT-XXXIII/4; Arctic Amplification; Arctic Ocean; ARK-XXIV/1; ARK-XXIV/2; ARK-XXIX/2.2; ARK-XXV/1; ARK-XXV/2; ARK-XXVI/1; ARK-XXVII/1; ARK-XXVII/2; ARK-XXVIII/2; ARK-XXX/1.1; ARK-XXX/1.2; ARK-XXXI/1.1,PASCAL; ARK-XXXI/1.2; ARK-XXXI/2; AWI_BioOce; Barents Sea; Biological Oceanography @ AWI; Campaign; Canarias Sea; chlorophyll; Chlorophyll a; Chlorophyll a, Diatoms; Chlorophyll a, Dinoflagellata; Chlorophyll a, Green algae; Chlorophyll a, Haptophyta; Chlorophyll a, Prochlorococcus; Chlorophyll a, Prokaryotes; Chlorophyll a + Divinyl chlorophyll a + Chlorophyllide a; Chlorophyll b + Divinyl chlorophyll b + Chlorophyllide b; Chlorophyllide a; CT; CTD, towed system; CTD/Rosette; CTD/Rosette with Underwater Vision Profiler; CTD001; CTD002; CTD003; CTD004; CTD005; CTD006; CTD007; CTD008; CTD009; CTD010; CTD011; CTD012; CTD013; CTD014; CTD015; CTD016; CTD017; CTD018; CTD019; CTD020; CTD021; CTD022; CTD023; CTD024; CTD025; CTD026; CTD027; CTD028; CTD029; CTD030; CTD031; CTD032; CTD033; CTD034; CTD035; CTD036; CTD037; CTD038; CTD039; CTD040; CTD041; CTD042; CTD043; CTD044; CTD045; CTD046; CTD047; CTD048; CTD049; CTD050; CTD051; CTD052; CTD053; CTD054; CTD055; CTD056; CTD057; CTD058; CTD059; CTD060; CTD061; CTD062; CTD063; CTD-Acoustic Doppler Current Profiler; CTD-ADCP; CTD-RO; CTD-RO_UVP; CTD-twoyo; DATE/TIME; DEPTH, water; Diagnostic Pigment Analysis (DPA); Discovery (2013); Divinyl chlorophyll a; DPA; DY110; EG_I; EG_II; EG_III; EG_IV; Event label; Exploitation of Sentinel-5-P for Ocean Colour Products; FRAM; FRontiers in Arctic marine Monitoring; Fucoxanthin; Global Long-term Observations of Phytoplankton Functional Types from Space; GLOPHYTS; Hand net; HG_I; HG_II; HG_III; HG_IV; HG_IX; HG_V; HG_VI; HG_VIII; HGIV; High Performance Liquid Chromatography (HPLC); HN; HPLC; ICE; Ice station; James Clark Ross; JR18001; Kb0; LATITUDE; Lazarev Sea; LONGITUDE; N3; N4; N5; North Greenland Sea; North Sea; Norwegian Sea; ORDINAL NUMBER; Peridinin; phytoplankton functional types; pigments; Polarstern; PORTWIMS; Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation; PS103; PS103_0_Underway-3; PS103_1-1; PS103_11-1; PS103_15-1; PS103_22-5; PS103_23-5; PS103_2-4; PS103_27-2; PS103_29-3; PS103_3-1; PS103_31-2; PS103_34-6; PS103_39-3; PS103_40-3; PS103_4-1; PS103_43-4; PS103_45-3; PS103_48-1; PS103_5-2; PS103_59-2; PS103_6-6; PS103_67-1; PS103_8-3; PS103_9-1; PS106_18-2; PS106_21-2; PS106_27-6; PS106_28-2; PS106_31-2; PS106_32-2; PS106_45-1; PS106_50-1; PS106_ZODIAK_170527; PS106_ZODIAK_170529; PS106_ZODIAK_170531; PS106_ZODIAK_170601; PS106_ZODIAK_170607; PS106_ZODIAK_170608; PS106_ZODIAK_170617; PS106_ZODIAK_170618; PS106_ZODIAK_170619; PS106_ZODIAK_170624; PS106_ZODIAK_170625; PS106_ZODIAK_170626; PS106_ZODIAK_170627; PS106_ZODIAK_170629; PS106_ZODIAK_170630; PS106_ZODIAK_170701; PS106_ZODIAK_170702; PS106_ZODIAK_170703; PS106_ZODIAK_170705; PS106_ZODIAK_170706; PS106_ZODIAK_170708; PS106_ZODIAK_170709; PS106_ZODIAK_170710; PS106_ZODIAK_170711; PS106_ZODIAK_170713; PS106_ZODIAK_170714; PS106_ZODIAK_170715; PS106/1; PS106/2; PS107; PS107_0_underway-9; PS107_10-4; PS107_12-3; PS107_14-1; PS107_16-3; PS107_18-3; PS107_19-1; PS107_20-8; PS107_21-1; PS107_22-6; PS107_24-1; PS107_28-1; PS107_29-1; PS107_33-6; PS107_34-5; PS107_36-1; PS107_37-1; PS107_40-2; PS107_40-3; PS107_40-4; PS107_40-5; PS107_40-6; PS107_48-1; PS107_6-8; PS107_7-1; PS107_8-1; PS113; PS113_0_underway-5; PS113_11-2; PS113_1-2; PS113_13-2; PS113_14-2; PS113_15-1; PS113_17-2; PS113_18-2; PS113_20-1; PS113_21-1; PS113_22-2; PS113_23-2; PS113_25-1; PS113_26-2; PS113_27-1; PS113_28-1; PS113_29-2; PS113_30-2; PS113_31-1; PS113_3-2; PS113_33-1; PS113_5-2; PS113_6-2; PS113_7-2; PS113_9-2; PS120; PS120_0_underway-10; PS120_11-3; PS120_15-3; PS120_19-3; PS120_20-1; PS120_21-3; PS120_24-3; PS120_3-1; PS120_5-3; PS120_8-3; PS121; PS121_0_Underway-65; PS121_1-2; PS121_12-2; PS121_15-1; PS121_16-5; PS121_24-2; PS121_25-2; PS121_27-2; PS121_28-4; PS121_29-1; PS121_32-2; PS121_33-2; PS121_34-1; PS121_35-3; PS121_36-1; PS121_38-1; PS121_39-1; PS121_40-3; PS121_43-7; PS121_44-3; PS121_45-1; PS121_52-2; PS121_52-6; PS121_5-3; PS121_7-3; PS74; PS74/104-1; PS74/107-1; PS74/108-1; PS74/112-1; PS74/119-1; PS74/120-1; PS74/127-1; PS74/128-1; PS74/132-1; PS74/133-1; PS74/134-1; PS74/1-track; PS74/2-track; PS76; PS76/001-1; PS76/002-1; PS76/005-1; PS76/007-2; PS76/009-1; PS76/017-1; PS76/020-1; PS76/025-1; PS76/026-1; PS76/030-1; PS76/034-3; PS76/039-1; PS76/041-1; PS76/044-1; PS76/049-1; PS76/051-1; PS76/057-1; PS76/058-1; PS76/062-1; PS76/064-1; PS76/068-1; PS76/072-1; PS76/080-1; PS76/082-1; PS76/094-1; PS76/098-1; PS76/102-1; PS76/109-3; PS76/110-1; PS76/111-1; PS76/120-2; PS76/121-1; PS76/122-1; PS76/124-3; PS76/129-1; PS76/132-1; PS76/134-1; PS76/135-1; PS76/136-1; PS76/138-1; PS76/139-1; PS76/157-1; PS76/159-2; PS76/166-1; PS76/167-1; PS76/170-2; PS76/173-1; PS76/174-1; PS76/175-1; PS76/176-1; PS76/178-1; PS76/179-3; PS76/181-1; PS76/182-1; PS76/184-1; PS76/185-1; PS76/194-1; PS76/200-1; PS76/201-1; PS76/203-1; PS76/204-1; PS76/208-5; PS76/210-2; PS76/211-1; PS76/216-1; PS76/220-1; PS76/223-1; PS76/224-1; PS76/227-3; PS76/229-1; PS76/231-1; PS76/233-1; PS76/235-
    Type: Dataset
    Format: text/tab-separated-values, 37522 data points
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  • 3
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    Formaldehyde (HCHO) slant column densities measured during the cruise MSM58/2 over the Atlantic Ocean using MAX-DOAS (2019)
    PANGAEA
    Details
    Publication Date: 2024-02-02
    Keywords: Atlantic Ocean; Atmosphere; CT; Formaldehyde; Formaldehyde, slant column density; Formaldehyde, vertical column density; Formaldehyde air mass factor; HCHO; LATITUDE; LONGITUDE; Maria S. Merian; MAX-DOAS; MSM58/2; MSM58/2-track; Multi-AXis Differential Optical Absorption Spectrometer (MAX-DOAS); Time in days; Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 209 data points
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  • 4
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    Glyoxal (CHOCHO) slant column densities measured during the cruise MSM58/2 over the Atlantic Ocean using MAX-DOAS (2019)
    PANGAEA
    Details
    Publication Date: 2024-02-02
    Keywords: Atlantic Ocean; Atmosphere; CHOCHO; CT; Glyoxal; Glyoxal, slant column density; Glyoxal, vertical column density; Glyoxal air mass factor; LATITUDE; LONGITUDE; Maria S. Merian; MAX-DOAS; MSM58/2; MSM58/2-track; Multi-AXis Differential Optical Absorption Spectrometer (MAX-DOAS); Time in days; Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 193 data points
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  • 5
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    Mean spectral diffuse attenuation coefficients averaged for 320 nm to 338.5 nm (UVAB), 356.5 nm to 390 nm (UVA) and 390 nm to 423 nm (blue) in the Atlantic Ocean from Sentinel-5P instrument TROPOMI (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-20
    Description: This data set contains the mean diffuse attenuation coefficient of the downwelling plane irradiance over the first optical depth and over three different wavelength regions: 312.5 - 338 nm (Kd-UVAB), 356.5 - 390 nm (Kd-UVA), and 390 - 423 nm (KD-blue) as retrieved from the Sentinel-5P TROPOMI sensor from 11 May to 9 June 2018 in the Atlantic Ocean. The retrieval for the products is based on Differential Optical Absorption Spectroscopy (DOAS) extended to the ocean domain (PhytoDOAS). The spectral integrated Kd are derived from the Vibrational Raman Scattering (VRS) signal of the ocean which is retrieved by DOAS fits in three different fit windows. Kd-UVAB corresponds to DOAS VRS fits in the wavelength regions of 349.5 - 382 nm, Kd-UVA to 405 - 450 nm, and Kd-blue to 450 - 493 nm. VRS fit factors in the blue fit window (450 - 493 nm) were offset-corrected (an offset of 0.186 was added to the VRS fit factor of all processed S5P ground pixels). Derived Kd-blue are otherwise unrealistically high. The offset was determined with the help of Kd data at 490 nm from the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A. Fit results from the DOAS retrieval are converted into physical quantities using look-up-tables which were established with coupled atmosphere-ocean radiative transfer modeling using the software SCIATRAN version 4.0.8 (Rozanov et al. 2017, https://www.iup.uni-bremen.de/sciatran/). Only TROPOMI data with a cloud fraction smaller 0.01 were processed by the algorithm. Output data within the Atlantic Ocean (55°N-55°S, 70°W-10°E) were gridded daily into 0.083° latitudinal/longitudinal bins. Details on the algorithm can be found in the related publication by Oelker et al. (2022).
    Keywords: AC3; Arctic Amplification; AtlanticOcean; Atlantic Ocean; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); blue radiation; Differential Optical Absorption Spectroscopy; diffuse attenuation coefficient; Exploitation of Sentinel-5-P for Ocean Colour Products; FRAM; FRontiers in Arctic marine Monitoring; optical satellite data; S5POC; SAT; Satellite remote sensing; UV radiation
    Type: Dataset
    Format: text/tab-separated-values, 3 data points
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    DATA PANGAEA
  • 6
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    Deep Neural Networks for Earthquake Detection and Source Region Estimation in North-Central Venezuela (2020)
    Seismological Society of America
    Details
    Publication Date: 2020-06-09
    Description: Reliable earthquake detection algorithms are necessary to properly analyze and catalog the continuously growing seismic records. We report the results of applying a deep convolutional neural network, called UPC-UCV (Universitat Politecnica de Catalunya - Universidad Central de Venezuela), over single-station three-channel signal windows for P-wave earthquake detection and source region estimation in north-central Venezuela. The analysis is performed on a new dataset of handpicked arrivals of P waves from local events, named CARABOBO, built and made public for reproducibility and benchmarking purposes. The CARABOBO dataset consists of three-channel continuous data recorded by the broadband stations of the Venezuelan Foundation for Seismological Research in the region of 9.5°–11.5°N and 67.0°–69.0°W during the time period from April 2018 to April 2019. During this period, 949 earthquakes were recorded in that area, corresponding to earthquakes with magnitudes in the range from Mw 1.1 to 5.2. To estimate the epicentral source region of a detected event, the proposed network employs geographical distribution of the CARABOBO dataset into K clusters as a basis. This geographical partitioning is automatically performed by the k-means algorithm, and the optimality of the K-values for our dataset has been assessed using the elbow (K=5) and silhouette (K=3) methods. For target seismicity, the proposed network achieves 95.27% detection accuracy and 93.36% source region estimation accuracy, when using K=5 geographic clusters. The location accuracy slightly increases to 95.68% in the case of K=3 geographic partitions. The detection capability of this network has also been tested on the OKLAHOMA dataset, which compiles more than 2000 local earthquakes that occurred in this U.S. state. Without any modification, the proposed network yields excellent detection results when trained and evaluated on that dataset (98.21% accuracy; ConvNetQuake, fine-tuned for this dataset, achieves a 97.32% accuracy), corresponding to a totally different geographical region.
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
    Published by Seismological Society of America
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  • 7
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    Enhanced trans-Himalaya pollution transport to the Tibetan Plateau by cut-off low systems (2017)
    Copernicus
    Details
    Publication Date: 2017-02-28
    Description: Long-range transport followed by deposition of black carbon on glaciers of Tibet is one of the key issues of climate research as it induces changes on radiative forcing and subsequently impacting the melting of glaciers. The transport mechanism, however, is not well understood. In this study, we use short-lived reactive aromatics as proxies to diagnose transport of pollutants to Tibet. In situ observations of short-lived reactive aromatics across the Tibetan Plateau are analyzed using a regional chemistry and transport model. The model performance using the current emission inventories over the region is poor due to problems in the inventories and model transport. Top-down emissions constrained by satellite observations of glyoxal are a factor of 2–6 higher than the a priori emissions over the industrialized Indo-Gangetic Plain. Using the top-down emissions, agreement between model simulations and surface observations of aromatics improves. We find enhancements of reactive aromatics over Tibet by a factor of 6 on average due to rapid transport from India and nearby regions during the presence of a high-altitude cut-off low system. Our results suggest that the cut-off low system is a major pathway for long-range transport of pollutants such as black carbon. The modeling analysis reveals that even the state-of-the-science high-resolution reanalysis cannot simulate this cut-off low system accurately, which probably explains in part the underestimation of black carbon deposition over Tibet in previous modeling studies. Another model deficiency of underestimating pollution transport from the south is due to the complexity of terrain, leading to enhanced transport. It is therefore challenging for coarse-resolution global climate models to properly represent the effects of long-range transport of pollutants on the Tibetan environment and the subsequent consequence for regional climate forcing.
    Print ISSN: 1680-7316
    Electronic ISSN: 1680-7324
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 8
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    The velocity structure of the Cariaco sedimentary basin, northeastern Venezuela, from refraction seismic data and possible relationship to earthquake hazard (2005)
    Elsevier
    Details
    Publication Date: 2005-01-01
    Print ISSN: 0895-9811
    Electronic ISSN: 1873-0647
    Topics: Geosciences
    Published by Elsevier
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    Unexpected long-range transport of glyoxal and formaldehyde observed from the Copernicus Sentinel-5 Precursor satellite during the 2018 Canadian wildfires (2019)
    Copernicus
    Details
    Publication Date: 2019-08-29
    Description: Glyoxal (CHO.CHO) and formaldehyde (HCHO) are intermediate products in the oxidation of the majority of volatile organic compounds (VOC). CHO.CHO is also a precursor of secondary organic aerosol (SOA) formation in the atmosphere. These VOCs are released from biogenic, anthropogenic, and pyrogenic sources. CHO.CHO and HCHO tropospheric lifetimes are short during the daytime and at mid-latitudes (few hours), as they are rapidly removed from the atmosphere by their photolysis, oxidation by OH, and uptake on particles/deposition. During nighttime or at high latitudes, lifetime can be prolonged to many hours or even days. Previous studies demonstrated that CHO.CHO and HCHO can be retrieved from space-borne observations using the DOAS method. In this study, we present CHO.CHO and HCHO columns retrieved from measurements of the TROPOMI instrument, launched recently on the Sentinel-5 Precursor (S5P) platform in October 2017. Strongly elevated amounts of CHO.CHO and HCHO are observed during the fire season in British Columbia Canada, where a large number of fires occurred in August 2018. CHO.CHO and HCHO plumes from individual fire hot-spots are observed in air masses travelling over distances of up to 1500 km, i.e. much longer than expected for the short atmospheric lifetime of CHO.CHO and HCHO. However, the temporal evolution of the plume differs for both species. Comparison with Lagrangian-based FLEXPART simulations for particles with different lifetimes shows that effective lifetimes of 20 hours and more are needed to explain the observations, indicating that CHO.CHO and HCHO are either efficiently recycled during transport or, continuously formed from the oxidation of longer-lived precursors present in the plume.
    Electronic ISSN: 1680-7375
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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    Enhanced Trans-Himalaya Pollution Transport to the Tibetan Plateau by the Cut-off Low System (2016)
    Copernicus
    Details
    Publication Date: 2016-09-28
    Description: Long-range transport and subsequent deposition of black carbon on glaciers of Tibet is one of the key issues of climate research inducing changes on radiative forcing and subsequently impacting on the melting of glaciers. The transport mechanism, however, is not well understood. In this study, we use short-lived reactive aromatics as proxies to diagnose transport of pollutants to Tibet. In situ observations of short-lived reactive aromatics across the Tibetan Plateau are analyzed using a regional chemistry and transport model. The model performance using the current emission inventories over the region is poor due to problems in the inventories and model transport. Top-down emissions constrained by satellite observations of glyoxal (CHOCHO) are a factor of 2–6 higher than the a priori emissions over the industrialized Indo-Gangetic Plain. Using the top-down emissions, agreement between model simulations and surface observations of aromatics improves. We find enhancements of reactive aromatics over Tibet by a factor of 6 on average due to rapid transport from India and nearby regions during the presence of a high-altitude cut-off low system. Our results suggest that the cut-off low system is a major pathway for long-range transport of pollutants such as black carbon. The modeling analysis reveals that even the state-of-the-science high-resolution reanalysis cannot simulate this cut-off low system accurately, which probably explains in part the underestimation of black carbon deposition over Tibet in previous modeling studies. Furthermore, another model deficiency of underestimating pollution transport from the south is due to the complexity of terrain, leading to enhanced transport. It is therefore challenging for coarse-resolution global climate models to properly represent the effects of long-range transport of pollutants on the Tibetan environment and the subsequent consequence for regional climate forcing.
    Electronic ISSN: 1680-7375
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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