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# 1
Rabbel, W. • Thorwart, M.
Abstract: The Villarrica Volcano is one of the most active volcanoes in South America and is located in a major tourism region. A dense seismological network is used to investigate the seismic characteristics of the volcano and its seismic structure tomographically with high spatial resolution. The network was in operation for 2 week from 01.03.2012 to 14.03.2012. It consisted of 30 3-component and 45 1-component short period seismographs covering an area of 2000 km*2. The covered area has a diameter of 45 km and includes the volcanic building.
# 2
Pick, Leonie
Abstract: The software package “ClassifyStorms” performs a classification of geomagnetic storms according to their interplanetary driving mechanisms based exclusively on magnetometer measurements from ground. In the present version two such driver classes are considered for storms dating back to 1930. Class 0 contains storms driven by Corotating or Stream Interaction Regions (C/SIRs) and Class 1 contains storms driven by Interplanetary Coronal Mass Ejections (ICMEs). The properties and geomagnetic responses of these two solar wind structures are reviewed, e.g., by Kilpua et al. (2017, http://doi.org/10.1007/s11214-017-0411-3). The classification task is executed by a supervised binary logistic regression model in the framework of python's scikit-learn library. The model is validated mathematically and physically by checking the driver occurrence statistics in dependence on the solar cycle phase and storm intensity. A detailed description of the classification model is given in Pick et al. (2019) to which this software is supplementary material. Under "Files" you can download ClassifyStorms_1.0.0.zip, which contains "StormsClassified.csv". This table lists the Date (Year-Month-Day) and Time (Hour:Minutes:Seconds) of 7546 classified geomagnetic storms together with the predicted interplanetary driver class label (0 or 1) and its probability (between 0 and 1). The directory also includes an ASCII file ("ReferenceEvents.txt"), which lists the "reference" events, i.e., a compilation of three published lists with identified ICMEs and C/SIRs (Jian et al., 2006a, 2006b, 2011; Shen et al., 2017; Turner et al., 2009). If you want to execute the code yourself, run the jupyter notebook “ClassifyStorms.ipynb” (https://jupyter.org/). The notebook accesses the python modules “Imports.py”, “Modules.py” and “Plots.py” as well as the input data set “Input.nc”, an xarray Dataset (http://xarray.pydata.org/en/stable) saved in NetCDF format (https://www.unidata.ucar.edu/software/netcdf). The latter contains a temporally smoothed version of the long-term stable Hourly Magnetospheric Currents index (HMC index, http://doi.org/10.5880/GFZ.2.3.2018.006) and the underlying geomagnetic observatory measurements. The files provided here correspond to the software version discussed in the corresponding paper (see above). An up-to-date version of the software is available on GitLab via https://gitext.gfz-potsdam.de/lpick/ClassifyStorms. The “Readme.md” file provides all information needed to run or modify “ClassifyStorms” from the GitLab source.
# 3
Neuper, Malte • Ehret, Uwe
Abstract: The dataset consist of time series of hourly rain rates and mean radar reflectivity factor (herein after referred to as reflectivity) near the ground, 100 meter and 1500 meter above the ground at six locations in the Attert catchment in Luxembourg. The time series cover a time span of 4 years (from the 1st of October 2012 tor the 30th of September 2016). The dataset was derived from drop size measurements we conducted at six stations with six laser optical disdrometers and two micro rain radars (MRR) within the CAOS Project (DFG Research Group: From Catchments as Organized Systems to Models based on Functional Units (FOR 1598). The time series of rain rates and radar reflectivity factors (reflectivities) were calculated (derived) via the 3.5th and 6th statistical moments of the drop size distributions using the particular raw data of drop sizes and fall velocities. The primary reason for the measurements was to improve radar based quantitative precipitation estimation in general and the conversion of the reflectivity Z (measured by operational weather radar) to a rain rate R at the ground via the so-called Z-R relation within a mesoscale catchment. GENERAL CONVENTIONS:• Time extent: 1.10.2012 00:00 – 30.09.2016 23:00 (35064 values)• Time reference: UTC • Time stamp: end• Time resolution: 1h• Time series are equidistant and gapless• Missing values: NaN• Delimiter: ; (semicolon)• decimal separator: . (point) STATION LOCATIONS:Name; Abbreviation; Latitude (WGS-84); Longitude(WGS-84); height a.s.l; InstrumentationOberpallen;OPA; 49.73201°; 5.84712°;287 m; disdrometer Useldange;USL; 49.76738°; 5.96756°; 280 m; disdrometer and MRR Ell;ELL; 49.76558°; 5.84401°; 290 m; disdrometer Post;POS; 49.75394°; 5.75481°; 345 m; disdrometer Petit-Nobressart;PIN; 49.77938°; 5.80526°; 374 m; disdrometer and MRR Hostert-Folschette;HOF; 49.81267°; 5.87008°; 435 m; disdrometer HEADER – VARIABLES DESCRIPTION:Name - description:Date-UTC – Date as yyyy-mm-dd HH:MM (4 digit year-2 digit month – 2 digit day 2 digit hour: 2 digit minute)Time Zone: UTC. Decade – tenner day of the year (that is 1st to 10th of January = 1 ; 11th to 20th of January = 2 ; 21th to 30th of January = 3 ; … 21st to 31st of December = 36.Month – Month of the year (1: January, 2: February, 3:March,…, 12: December).dBZ0_DIS_ELL – reflectivity at ground level (in dBZ) at the station Ell derived from disdrometer measurements.dBZ0_DIS_HOF – reflectivity at ground level (in dBZ) at the station Hostert-Folschette derived from disdrometer measurements.dBZ0_DIS_OPA – reflectivity at ground level (in dBZ) at the station Oberpallen derived from disdrometer measurements.dBZ0_DIS_PIN – reflectivity at ground level (in dBZ) at the station Petit-Nobressart derived from disdrometer measurements.dBZ0_DIS_POS – reflectivity at ground level (in dBZ) at the station Post derived from disdrometer measurements.dBZ0_DIS_USL – reflectivity at ground level (in dBZ) at the station Useldange derived from disdrometer measurements.dBZ100_MRR_PIN – reflectivity 100 m above ground (in dBZ) at the station Petit-Nobressart derived from MRR measurements.dBZ100_MRR_USL – reflectivity 100 m above ground (in dBZ) at the station Useldange derived from MRR measurements.dBZ1500_MRR_PIN – reflectivity 1500 m above ground (in dBZ) at the station Petit-Nobressart derived from MRR measurements.dBZ1500_MRR_USL – reflectivity 1500 m above ground (in dBZ) at the station Useldange derived from MRR measurements.RR0_DIS_ELL – rain rate at ground level (in mm/h) at the station Ell derived from disdrometer measurements.RR0_DIS_HOF – rain rate at ground level (in mm/h) at the station Hostert-Folschette derived from disdrometer measurements.RR0_DIS_OPA – rain rate at ground level (in mm/h) at the station Oberpallen derived from disdrometer measurements.RR0_DIS_PIN– rain rate at ground level (in mm/h) at the station Petit-Nobressart derived from disdrometer measurements.RR0_DIS_POS – rain rate at ground level (in mm/h) at the station Post derived from disdrometer measurements.RR0_DIS_USL – rain rate at ground level (in mm/h) at the station Useldange derived from disdrometer measurements.RR100_MRR_PIN – rain rate 100 m above ground (in mm/h) at the station Petit-Nobressart derived from MRR measurements.RR100_MRR_USL – rain rate 100 m above ground (in mm/h) at the station Useldange derived from MRR measurements.RR1500_MRR_PIN – rain rate 1500 m above ground (in mm/h) at the station Petit-Nobressart derived from MRR measurements.RR1500_MRR_USL – rain rate 1500 m above ground (in mm/h) at the station Useldange derived from MRR measurements. The instruments were maintained and cleaned monthly. The data was quality checked. Cases with solid precipitation were excluded using the output form the Pasivel² present weather sensor software, which especially was needed since disdrometer data was contaminated by cobwebs. But since the present weather analyzer classified these (due to their slow movement within the wind) as snow, these then could easily be eliminated.
DISDROMETER:We deployed six second generation OTT Particle Size and Velocity (PARSIVEL², see Löffler-Mang and Joss, 2000 - https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2) optical disdrometers in the study area to measure drop size distributions at ground level in 1-minute resolution. Two were located at the same sites as the MRRs (Useldange and Petit-Nobressart). The others were placed such as to both capture the hydroclimatic variations in the study area and to cover it as uniformly as possible. We applied a quality control to the raw data as described by Friedrich et al. (2013 - https://doi.org/10.1175/JTECH-D-12-00254.1 and https://doi.org/10.1175/MWR-D-12-00116.1 ), converted the filtered data to drop-size concentrations per unit air volume to make them comparable to weather radar and MRR data, then converted them to reflectivity and rain rate using the 3.5th and 6th statistical moments of the drop size distributions and finally took 1-hour averages and sums thereof. MRR: From two vertical pointing K-band METEK micro rain radars (MRR) (Löffler-Mang et al. 1999 - https://doi.org/10.1175/1520-0426(1999)016<0379:OTPOAL>2.0.CO;2 and Peters et al. 2002 - Rain observations with a vertically looking Micro Rain Radar (MRR). Boreal Env. Res. 7: 353–362, 2002) measurements located at the sites Useldange and Petit-Nobressart drop size spectra were retrieved at 1500 meter and 100 meter above ground . We operated the MRR's at 100 vertical meters and 10 seconds temporal resolution, but for reasons of storage and processing efficiency did all further processing on 1-minute aggregations thereof. The raw Doppler spectra were transformed to drop size distributions via the drop size – fall velocity relation given in Atlas et al (1973 - https://doi.org/10.1029/RG011i001p00001). From the drop size distributions the rain rate and the reflectivity were calculated using the 3.5th and the 6th statistical moments of the drop size distributions. In doing so we assumed the vertical velocity of the air to be negligible.
# 4
Bentz, Stephan • Martínez-Garzón, Patricia • Kwiatek, Grzegorz • Dresen, Georg • Bohnhoff, Marco
Abstract: Preparatory mechanisms accompanying or leading to nucleation of larger earthquakes have been observed at both laboratory and field scale, but the precise conditions favoring extended nucleation processes are still largely unknown. In particular, it remains a matter of debate why earthquakes often occur spontaneously without noticeable precursors as opposed to an extended failure process accompanied by foreshocks. In this study, we have generated new high-resolution seismicity catalogs framing the occurrence of 20 ML > 2.5 earthquakes at The Geysers geothermal field in California. To this end, a seismicity catalog of the 11 days framing each large event was created. We selected 20 sequences in total from different tectonic settings within the field that sample the entire reservoir depth range and temporal periods with high or low injection rates. Seismic activity and magnitude frequency distributions displayed by the different earthquake sequences are correlated with their location within the reservoir. Sequences located in the northwestern part of the reservoir show overall increased seismic activity and low b-values, while the southeastern part is dominated by decreased seismic activity and higher b-values. Periods of high injection coincide with high b-values, and vice versa. These observations potentially reflect differential stresses and damage varying across the field. About 50 % of analyzed sequences exhibit no change in seismicity rate in response to the large main event. Instead, we find complex waveforms at the onset of the main earthquake, suggesting that small ruptures spontaneously grow into or trigger larger events.
Earthquake catalogs belonging to each of the 20 mainshock sequences. The catalogue is provided in tabular form (ASCII). The file names indate the NCEDC ID of the EQ Sequence mainshock (NCEDC = Northern California earthquake datacenter, Enhanced Geothermal Systems Earthquake Catalog, https://ncedc.org/egs/catalog-search.html). Definition of columns in the data tables (also in the header of the data): - X,Y are in local cartesian coordinates (m) [unlocated events are indicated by NaN]- Z (m) is in depth, negative values point downwards- Coordinate origin in Lat;Lon: 38.8042;-122.7822- dt refers to matlab serial time (i.e. the number of days from January 0, 0000, fractions of days are given as decimal digits)- Ml is local magnitude
# 5
Ge, Zhiyuan • Rosenau, Matthias • Warsitzka, Michael • Rudolf, Michael • Gawthorpe, Robert
Abstract: This data set includes the results of digital image correlation of three experiments on gravitational tectonics at passive margins performed at the Helmholtz Laboratory for Tectonic Modelling (HelTec) of the GFZ German Research Centre for Geosciences in Potsdam in the framework of EPOS transnational access activities in 2018. Detailed descriptions of the experiments and monitoring techniques can be found in Ge et al. (submitted) to which this data set is supplement. The DIC analysis yields quantitative deformation information of the experiment surfaces by means of 3D surface displacements from which strain has been calculated. The data presented here are visualized as surface displacement maps, strain maps and strain evolution maps.
# 6
Brugger, Julia • Hofmann, Matthias • Petri, Stefan • Feulner, Georg
Abstract: In "On the sensitivity of the Devonian climate to continental configuration, vegetation cover, orbital configuration, CO_2 concentration and insolation" we study the sensitivity of the Devonian (419 to 359 million years ago) to several parameters using a coupled climate model. The data presented here is the model output the results of this manuscript are based on. Additionally, the figures of the publication and scripts (Python and Yorick) to analyse the model output and generate the figures are contained. The model output is provided in different netcdf files. The structure of the model output is explained in a readme file. The data is generated using the coupled ocean-atmosphere model CLIMBER3alpha which models climate globally on a 3.75°x3.75° (ocean) and 22.5° (longitude) x 7.5° (latitude) (atmosphere) grid. More information about the model can be found in the manuscript.
# 7
Clubb, Fiona • Bookhagen, Bodo • Rheinwalt, Aljoscha
Abstract: This software package contains code for performing agglomerative hierarchical clustering on river long profiles extracted from topographic data. The software requires initial topographic analysis to extract river profiles based on the Edinburgh Land Surface Topographic Tools package. Detailed documentation and tutorials for installation and running the code can be found at https://lsdtopotools.github.io/LSDTT_documentation/. The package written in Python and based on the scipy cluster package. The development version of the code can be found on GitHub (https://github.com/UP-RS-ESP/river-clusters) along with full instructions on how to install and run the code.
# 8
Heidbach, Oliver • Rajabi, Mojtaba • Reiter, Karsten • Ziegler, Moritz • WSM Team
Abstract: The World Stress Map (WSM) database is a global compilation of information on the crustal present-day stress field. It is a collaborative project between academia and industry that aims to characterize the stress pattern and to understand the stress sources. It commenced in 1986 as a project of the International Lithosphere Program under the leadership of Mary-Lou Zoback. From 1995-2008 it was a project of the Heidelberg Academy of Sciences and Humanities headed first by Karl Fuchs and then by Friedemann Wenzel. Since 2009 the WSM is maintained at the GFZ German Research Centre for Geosciences and since 2012 the WSM is a member of the ICSU World Data System. All stress information is analysed and compiled in a standardized format and quality-ranked for reliability and comparability on a global scale. The WSM database release 2016 contains 42,870 data records within the upper 40 km of the Earth’s crust. The data are provided in three formats: Excel-file (wsm2016.xlsx), comma separated fields (wsm2016.csv) and with a zipped google Earth input file (wsm2016_google.zip). Data records with reliable A-C quality are displayed in the World Stress Map (doi:10.5880/WSM.2016.002). Further detailed information on the WSM quality ranking scheme, guidelines for the various stress indicators, and software for stress map generation and the stress pattern analysis is available at www.world-stress-map.org. VERSION HISTORY:Version 1.1. (15 June 2019): updated version of the zip-compressed Google Earth .kml (wsm2016_google.zip) with a new URL of the server.
# 9
Gütschow, Johannes • Jeffery, Louise • Gieseke, Robert
Abstract: This is an updated version of Gütschow et al. (2018, http://doi.org/10.5880/pik.2018.003). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its previous updates. For a detailed description of the changes please consult the CHANGELOG included in the data description document. The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2016, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Agriculture is available. Version 2.0 of the PRIMAP-hist dataset does not include emissions from Land use, land use change and forestry (LULUCF). List of datasets included in this data publication:(1) PRIMAP-hist_v2.0_11-Dec-2018.csv: With numerical extrapolation of all time series to 2016. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v2.0_11-Dec-2018.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v2.0_data-format-description: including CHANGELOG(4) PRIMAP-hist_v2.0_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder) When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources. SOURCES:- Global CO2 emissions from cement production v2: Andrew (2018)- BP Statistical Review of World Energy: BP (2018)- CDIAC: Boden et al. (2017)- EDGAR version 4.3.2: JRC and PBL (2017), Janssens-Maenhout et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2018)- RCP historical data: Meinshausen et al. (2011)- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2018)- UNFCCC Biennal Update Reports: UNFCCC (2018)- UNFCCC Common Reporting Format (CRF): UNFCCC (2017), UNFCCC (2018), Jeffery et al. (2018) Full references are available in the data description document.
Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations.
# 10
INTERMAGNET • 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) • (et. al.)
Abstract: Definitive digital values of the Earth's mangetic field recorded during 2013 at INTERMAGNET observatories around the world. Data includes minute, hourly and daily vector values, along with observatory baseline values for quality control. Annual means are also included. All data is included on the single downloadable archive file (gzipped tar format) available from this landing page. 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 data formats used in this publication and the technical standards used to create the data, please refer to the INTERMAGNET Technical Manual (http://www.intermagnet.org/publication-software/technicalsoft-eng.php) 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.
The International Real-time Magnetic Observatory Network (INTERMAGNET) is the global network of observatories, monitoring the Earth's magnetic field. The INTERMAGNET programme exists to establish a global network of cooperating digital magnetic observatories, adopting modern standard specifications for measuring and recording equipment, in order to facilitate data exchange and the production of geomagnetic products in close to real time. INTERMAGNET also coordinates the publication of quality-controlled, definitive geomagnetic data from its affiliated observatories.
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