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# 1
Addis Ababa University, Institute of Geophysics, Space Science and Astronomy (Ethiopia) • Altay-Sayan Branch of Geophysical Survey of Siberian Branch of Russian Academy of Sciences (Russia) • Bureau Central de Magnétisme Terrestre, BCMT (France) • Beijing Ming Tombs Geomagnetic Observatory Center, Institute of Geology and Geophysics, Chinese Academy of Sciences (China) • Bogaziçi University, Kandilli Observatory and Earthquake Research Institute (Turkey) • (et. al.)
Abstract: Definitive digital one-minute values of the Earth's mangetic field recorded during 2013 at INTERMAGNET observatories around the world. This is the 23rd annual publication in the series. Some national data institutions may have related DOIs that describe subsets of the data. These DOIs are shown under "Related DOIs to be quoted". For more information on the technical standards please refer to the INTERMAGNET Technical Manual and the Technical note TN6 "INTERMAGNET Definitive One-second Data Standard".
Geomagnetic data is recorded and quality controlled at the institutions responsible for each observatory. Before becoming a member of INTERMAGNET, institutes must make a detailed submission for each observatory that is to join. This submission is verified by a committee in INTERMAGNET before the observatory is admitted. Only data from INTERMAGNET members is published by INTERMAGNET. Each annual definitive data set is checked for quality by a team of data checkers in INTERMAGNET before the data is admitted to the series for that year.
# 2
Matzka, Jürgen • Stolle, Claudia • Kervalishvili, Guram • Rauberg, Jan • Yamazaki, Yosuke
Abstract: Purpose and design of the Hp indices, test dataset The geomagnetic Hp indices are developed as part of the SWAMI project ( funded by the European Union’s H2020 research and innovation program. They are designed to resemble the geomagnetic Kp index, but have a higher temporal resolution of 90, 60 and 30 minutes. Whereas the Kp index is a measure of energy input from the solar wind during a 3-hour interval, the Hp indices aim at being a similar measure for the energy input, but over shorter intervals. The geomagnetic Hp indices can be provided back to 1995. Their derivation procedure is similar, but not identical, to the Kp index. Hp values range from 0 to 9 (like Kp), and have mean occurrence rates that are comparable to those of the Kp index. However, users have to appreciate that the Hp indices are not identical to the Kp index of the corresponding time interval. Therefore, it is to be expected that they represent the energy input from the solar wind slightly differently than when using the Kp index. Disclaimer to users of the Hp indices test data set Please carefully test and validate all your model output and services for which you use the Hp indices (including the ap90, ap60, ap30) as input parameter. This is especially true when these models and services were originally derived or parameterized with the Kp index. Which files to use? We provide a number of test data files with different time resolutions. By default, we recommend to use the 1-hourly Kp-like Hp60 index (e.g. data file Hp60_2003.dat) or ap-like ap60 index (e.g. ap60_2003.dat). Hp test dataset description The Hp test dataset consists of 24 files. It is accompanied by the presentation given on the index at the IUGG General Assembly 2019 in Montreal (Stolle et al., 2019). For each year 2003, 2004, 2005 and 2017, there exist annual files for 90, 60 and 30 minutes time resolution) in 2 different formats (Hp and ap). In the format 'Hp' the Hp values are given as 0, 0.7, 1, 1.3, 1.7, 2, 2.3, ... 8.7, 9. In the format 'ap', the Hp values are mapped onto ap values in the same fashion as Kp values are mapped to ap values. The index is provided with an hourly resolution (Hp60 and ap60), and also with a 30-minute (Hp30 and ap30) and 90-minute version (Hp90 and ap90). The years 2003 (Halloween storm in October and November), 2005 (frequent geomagnetic storms) and 2017 (geomagnetic storm in September) were chosen for the occurrence of strong geomagnetic activity. The files are ASCII and have 7 header lines. The data is blank separated and fixed length. The 7th header line indicates the start time (in UTC) of the index interval. For Hp90 there are 16 intervals per day, for Hp60 there are 24 intervals per day, for Hp30 there are 48 intervals per day. Every line with data contains the index values for one day and starts with the date (year-month-day) in the format YYYY-MM-DD. The index values for each interval are written below the start time of the 7th header line. Missing data is indicated by -1. For more information on the Kp and ap index, please refer to and to Siebert and Meyer (1996). For more information on the Hp indices test dataset, please refer also to the presentation (Stolle et al., 2019) which can be downloaded from the FTP server.
# 3
Gaucher1, Emmanuel • Maurer, Vincent • Grunberg, Marc
Abstract: This report describes the passive seismic data acquired by the TOPASE network deployed over Rittershoffen geothermal field (Alsace, France). The monitoring period extends from March 2013 to November 2014, which includes the stimulation of the first well of the doublet, the drilling of the second well and well tests. These data were acquired using 31 Earth Data Loggers PR6-24 and MARK-SERCEL L-4C-3D 1 Hz seismometers of the Geophysical Instrument Pool Potsdam (GIPP), which were provided to the KIT-AGW-Geothermal research division.
# 4
Uhlig, David • von Blanckenburg, Friedhelm
Abstract: The data herein were used to assess the importance of geogenic-derived nutrients on long-term forest ecosystem nutrition in two mountainous temperate forest ecosystems in southern Germany (Conventwald/Black Forest and Mitterfels/Bavarian Forest). Presented are element concentrations of various forest ecosystem compartments along with the soil pH, chemical depletion fractions (CDF), mass transfer coefficients (τ_(X_i)^X), radiogenic Sr isotope ratio (87Sr/86Sr) of soil and saprolite as well as in situ 10Be concentrations of bedload sediment. Element concentrations measured by X-ray fluorescence (XRF) are provided for drilling core samples (depth: 20 m, site Conventwald (CON), and 30 m, site Mitterfels (MIT)) including unweathered parent bedrock (paragneiss) and regolith comprising soil, saprolite and weathered bedrock but also for bedload sediment. Element concentrations were also measured by ICP-OES to determine the element composition of the soil´s and saprolite´s water-soluble, easily exchangeable, carbonate and organic-bound fraction. In addition, ICP-OES derived element concentrations are reported for plant tissues such as needles, leaves, and stem wood comprising heartwood (dead part of wood) and sapwood (living part of wood) of the two tree species European beech (Fagus sylvatica) and Norway spruce (Picea abies). Along with the chemical composition of soil and saprolite calculated weathering indices such as the chemical depletion fraction (CDF) and the mass transfer coefficient (τ_(X_i)^X) are reported for regolith and bedrock. Further, the dataset contains phosphorus (P) concentrations measured by ICP-OES and UV spectrometry from various P fractions obtained by sequential extractions following the Hedley fractionation method. Additionally, the pH of soil and saprolite measured by a pH meter as well as the radiogenic Sr isotope ratio, namely 87Sr/86Sr measured by MC-ICP-MS for bulk bedrock and regolith are reported in the dataset. Finally, to estimate the landscapes lowering rate (total denudation) in situ 10Be concentrations were measured by accelerator mass spectrometry (AMS) on bedload sediment at the outlet of the catchment. The data presented here stem from sampling campaigns described in Uhlig et al. (2019) to which they are supplementary material to. Samples were mainly processed in the Helmholtz Laboratory for the Geochemistry of the Earth Surface (HELGES) and the GFZ section of Inorganic and Isotope Geochemistry (XRF analyses), the University of Bonn (P Hedley fractionation), and the University of Cologne - Centre for Accelerator Mass Spectrometry (AMS) (10Be measurements). This dataset represents the supplementary material to Uhlig et al. (2019). Tables (including data quality control) supplementary to the article are provided in pdf and xls formats. In addition, data measured in the course of the study is given in machine readable ASCII files. All samples are indexed with an International Geo Sample Number (IGSN). Sample metadata can be viewed by adding the IGSN to the “” URL (e.g.
# 5
Loibl, David
Abstract: All data in this archive were processed using the Open Source SAR Investigation System (OSARIS; v. 0.7.2. Processing was conducted on the Cirrus Cluster at the Climate Geography department, Humboldt-Universität zu Berlin. Input data were ESA Sentinel-1 IW SLC files.
With the advent of the two Sentinel-1 (S1) satellites, Synthetic Aperture Radar (SAR) data with high temporal and spatial resolution are freely available. This provides a promising framework to facilitate detailed investigations of surface instabilities and movements on large scales with high temporal resolution, but also poses substantial processing challenges because of storage and computation requirements. Here we present OSARIS, the ‘Open Source SAR Investigation System’, as a framework to process large stacks of S1 data on High-Performance Computing (HPC) clusters. Based on GMTSAR, shell scripts, and the workload manager Slurm, OSARIS provides an open and modular framework combining parallelization of high-performance C programs, flexibility of processing schemes, convenient configuration, and generation of geocoded stacks of analysis-ready base data, including amplitude, phase, coherence, and unwrapped interferograms. Time series analyses can be conducted by applying automated modules to the data stacks. Here, a demonstration dataset is presented that was generated using OSARIS in a case study from the northwestern Tien Shan, Central Asia. After merging of slices, a total of 80 scene pairs were processed from 174 total S1 input scenes. This archive contains full time series in original resolution of ~31 m for selected OSARIS interferometric processing results, i.e. amplitude, coherence, connected components, interferometric phase, line-of-sight displacement, sums of forward plus reverse pair unwrapped interferograms, and 'Unstable Coherence Metric'. In addition, results from the coherence-based 'Stable Ground Point Identification' module and coherence statistics for time series of selected subregions and landforms discussed in the associated publication are included. Wall clock processing time for the case study (area ~9,000 km²) was ~12h:04m on a machine with 400 cores and two TB RAM. In total, ~12d:10h:44m were saved through parallelization. OSARIS thus facilitates efficient S1-based region-wide investigations of surface movement events over multiple years.
# 6
Dobslaw, Henryk • Dill, Robert • Dahle, Christoph
Abstract: Spherical harmonic coefficients that represent the sum of the ATM (or GAA) and OCN (or GAB) coefficients during the specified timespan. These coefficients represent anomalous contributions of the non-tidal dynamic ocean to ocean bottom pressure, the non-tidal atmospheric surface pressure over the continents, the static contribution of atmospheric pressure to ocean bottom pressure, and the upper-air density anomalies above both the continents and the oceans. The anomalous signals are relative to the mean field from 2003-2014.
# 7
Dahle, Christoph • Flechtner, Frank • Murböck, Michael • Michalak, Grzegorz • Neumayer, Karl Hans • (et. al.)
Abstract: Spherical harmonic coefficients representing an estimate of Earth's mean gravity field during the specified timespan derived from GRACE-FO mission measurements. These coefficients represent the full magnitude of land hydrology, ice, and solid Earth processes. Further, they represent atmospheric and oceanic processes not captured in the accompanying GAC product.
# 8
Dahle, Christoph • Flechtner, Frank • Murböck, Michael • Michalak, Grzegorz • Neumayer, Hans • (et. al.)
Abstract: Spherical harmonic coefficients representing an estimate of Earth's mean gravity field during the specified timespan derived from GRACE mission measurements. These coefficients represent the full magnitude of land hydrology, ice, and solid Earth processes. Further, they represent atmospheric and oceanic processes not captured in the accompanying GAC product.
# 9
Reyer, Christopher • Silveyra Gonzalez, Ramiro • Dolos, Klara • Hartig, Florian • Hauf, Ylva • (et. al.)
Abstract: Current process-based vegetation models are complex scientific tools that require proper evaluation of the different processes included in the models to prove that the models can be used to integrate our understanding of forest ecosystems and project climate change impacts on forests. The PROFOUND database (PROFOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale. It has been designed to fulfill two objectives:- Allow for a thorough evaluation of complex, process-based vegetation models using multiple data streams covering a range of processes at different temporal scales- Allow for climate impact assessments by providing the latest climate scenario data. Therefore, the PROFOUND DB provides general a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data for 9 forest stands spread throughout Europe. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. The climate and nitrogen deposition data contains several datasets for the historic period and a wide range of future climate change scenarios following the Representative Emission Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). In addition, we also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND Database is available freely but we incite users to respect the data policies of the individual datasets as provided in the metadata of each data file. The database can also be accessed via the PROFOUND R-package, which provides basic functions to explore, plot and extract the data. The data (PROFOUND DB) are provided in two different versions (ProfoundData.sqlite, and documented by the following three documents: (1) PROFOUNDdatabase.pdf: describes the structure, organisation and content of the PROFOUND DB.(2) PROFOUNDsites.pdf: displays the main data of the PROFOUND DB for each of the 9 forest sites in tables and plots.(3) ProfoundData.pdf: explains how to use the PROFOUND R-Package "ProfoundData" to access the PROFOUND DB and provides example scripts on how to apply it.
# 10
Raab, Tobias • Reinsch, Thomas • Aldaz Cifuentes, Santiago • Henninges, Jan
Abstract: The data set is a supplement to the publication Raab, T., Reinsch, T., Aldaz Cifuentes, S. R., and Henninges, J. (2019). Real-Time Well Integrity Monitoring using Fiber-Optic Distributed Acoustic Sensing. SPE Journal. The data set contains fiber-optic and conventional logging data recorded for integrity investigations during different drilling stages of Well RN-34, Iceland.
AcknowledgementData was acquired within the framework of project IMAGE (Integrated Methods for Advanced Geothermal Exploration), funded by the EC Seventh Framework Programme under grant agreement No. 608553. This study has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 654497 (GeoWell project), 676564 (EPOS IP), and 691728 (DESTRESS). We would like to thank our partners from the GeoWell project for the excellent collaboration, constant support during data acquisition and analysis as well as the fruitful discussions over the past years. We are especially grateful to Árni Ragnarsson, Ingólfur Örn Þorbjornsson and Gunnar Skúlason Kaldal and their colleagues from ÍSOR as well as Guðmundur Ómar Friðleifsson and Ómar Sigurðsson and their colleagues from HS ORKA.We would like to thank Andi Clarke and his colleagues from Silixa Ltd. for their effort during data acquisition and analysis. At GFZ, we would like to thank David Bruhn, Ernst Huenges, Philippe Jousset, Christian Cunow, Jörg Schrötter, and Ronny Giese as well as all colleagues in section 4.8 Geoenergy who contributed to this project in one form or the other.
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