The data are the numerical modeling results to investigate plume-induced subduction initation on which the figures of the paper "Plume-induced subduction initiation: single- or multi-slab subduction?" by Baes, Sobolev, Gerya and Brune are based. Detailed description on how they are obtained is given in that article (Baes et al., 2020). The naming of the files is based on the number of figures in the paper. Each zipped file contains input files (init.t3c and mode.t3c) and output files (*.vtr).
(1) The compressed archive 'RCCM_Soft_Win.zip' includes the self-contained, executable IDL Virtual Machine software package that allows processing MISR RCCM data without requiring an IDL license. Users who do have access to an IDL license are encouraged to obtain the necessary source codes from the GitHub web site https://github.com/mmverstraete (Verstraete et al., 2019, https://doi.org/10.5281/zenodo.3240018) and to incorporate those functions in their own custom programs. (2) The document 'RCCM_Soft_Win.pdf' provides the User Manual to install and use the software package 'RCCM_Soft_Win.zip' on a PC running under the MS Windows operating system. In addition, the authors provide the test input data archive 'RCCM_input_68050.zip', available from Verstraete et al., 2020, http://doi.org/10.5880/fidgeo.2020.004, to allow users to explore for themselves the various steps of this missing data replacement process in actual MISR RCCM files. Background information: The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on- board the NASA Terra platform, launched on 18 December 1999. It features 9 cameras pointing at various angles along the track of the platform, each measuring the amount of solar radiation reflected by the Earth in 4 spectral bands. MISR started acquiring observations on 24 February 2000, and is still operating as of this writing, therefore providing almost 20 years of continuous global observations. One of the most basic data products generated by NASA after the initial pre-processing of MISR raw data is the Level 1B2 Georectified Radiance Product (GRP). This data product intermittently contains missing data that are often due to a temporary overload of the on- board computer. This process, which results in the dropping of lines of measurements while the computer resets itself, tends to occur especially when the MISR instrument is switched from the default Global Mode (GM) to the occasional Local Mode (LM) of operation, and conversely. As a result, those missing lines are unevenly distributed and tend to cluster around particular sites and dates.,This code is published under the MIT License. Copyright (c)  Michel Verstraete Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.,
Contributors:Porwollik, Vera, Rolinski, Susanne, Müller, Christoph
Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = Scenario Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. GLADIS - Water erosion: Nachtergaele et al. (2011) CHANGELOG for Version 1.1:improved calculation and mapping, for details see README.PDF
Global spherical harmonic paleomagnetic field model LSMOD.2 describes the magnetic field evolution from 50 to 30 ka BP based on published paleomagnetic sediment records and volcanic data. It is an update of LSMOD.1, with the only difference being a correction to the geographic locations of one of the underlying datasets. The time interval includes the Laschamp (~41 ka BP) and Mono Lake (~34 ka BP) excursions. The model is given with Fortran source code to obtain spherical harmonic magnetic field coefficients for individual epochs and to obtain time series of magnetic declination, inclination and field intensity from 49.95 to 30 ka BP for any location on Earth. For details see M. Korte, M. Brown, S. Panovska and I. Wardinski (2019): Robust characteristics of the Laschamp and Mono lake geomagnetic excursions: results from global field models. Submitted to Frontiers in Earth Sciences,File overview:
LSMOD.2 -- ASCII file containing the time-dependent model by a list of spline basis knot points and spherical harmonic coefficients for these knot points.LSfield.f -- Fortran source code to obtain time series predictions of declination, inclination and intensity from the model file.LScoefs.f -- Fortran source code to obtain the spherical harmonic coefficients for an individual age from the time-dependent model file.
The data are licenced under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0) and the Fortran Codes under the Apache License, Version 2.0.
The Fortran source code should work with any standard Fortran 77 or higher compiler. Each of the two program files can be compiled separately, all required subroutines are included in the files. The model file, LSMOD.1 or LSMOD.2, is read in by the executable program and has to be in the same directory. The programs work with interactive input, which will be requested when running the program.,
Radiance Light Trends is a GIS web application that is designed to quickly display information about radiance trends at a specific location (available online at https://lighttrends.lightpollutionmap.info). It uses data from two satellite systems, DMSP-OLS and VIIRS DNB, with data processing by NOAA. New VIIRS layers are added automatically as soon as NOAA makes them available to public.
The web application allows the user to examine changes in nighttime light emissions (nearly) worldwide, from 1992 up until last month. From 1992 to 2013, data comes from the Operational Linescan System of the Defense Meteorological Satellite Program (DMSP) satellites. From 2012 to the present, data comes from the Day/Night Band of the Visible Infrared Imaging Radiometer Suite instrument (VIIRS DNB). Due to significant differences in the instruments (as described by Miller et al., 2013), it is not possible to have a single record running from 1992 to today. A description of the VIIRS DNB night lights product used in this application was published by Elvidge et al. (2017), the data used in the app can be accessed from the NOAA Earth Observation Group (EOG) Website: https://ngdc.noaa.gov/eog/download.html
Contributors:Dreiling, Jennifer, Tilmann, Frederik
BayHunter is an open source Python tool to perform an McMC transdimensional Bayesian inversion of receiver functions and/ or surface wave dispersion. It is inverting for the velocity-depth structure, the number of layers and noise parameters (noise correlation and amplitude). The forward modeling codes are provided within the package, but are easily replaceable with own codes. It is also possible to add (completely different) data sets.
The BayWatch module can be used to live-stream the inversion while it is running: this makes it easy to see how each chain is exploring the parameter space, how the data fits and models change and in which direction the inversion progresses.
The scripts and workflow are supplementary material to "3D Modelling of Vertical Gravity Gradients and the delimitation of tectonic boundaries: The Caribbean oceanic domain as a case study" (Gómez-García et al., 2019). The codes include the calculation of the VGG response of a 3D lithospheric model, in spherical coordinates, using the software Tesseroids (Uieda, 2016). The "Readme_Workflow_2019_002.pdf" file provide very detail information about the structure of this repository, as well as the step-by-step for the scripts execution, and the list of the requiered software for the correct workflow performance. All the information provided here will allow the user to reproduce the results and figures of the main paper. Detailed information are also given in the associated README.
EMMA – End Member Modelling Analysis of grain-size data is a technique to unmix multimodal grain-size data sets, i.e., to decompose the data into the underlying grain-size distributions (loadings) and their contributions to each sample (scores). The R package EMMAgeo contains a series of functions to perform EMMA based on eigenspace decomposition. The data are rescaled and transformed to receive results in meaningful units, i.e., volume percentage. EMMA can be performed in a deterministic and two robust ways, the latter taking into account incomplete knowledge about model parameters. The model outputs can be interpreted in terms of sediment sources, transport pathways and transport regimes (loadings) as well as their relative importance throughout the sample space (scores).
The software package “ClassifyStorms” version 1.0.1 performs a classification of geomagnetic storms according to their interplanetary driving mechanisms based exclusively on magnetometer measurements from ground. In this 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-V1.0.1.zip, which contains the jupyter notebook “ClassifyStorms.ipynb” (https://jupyter.org/) and the python modules “Imports.py”, “Modules.py” and “Plots.py”. Check for an up-to-date release of the software on GitLab via https://gitext.gfz-potsdam.de/lpick/ClassifyStorms (under Project, Releases). The “Readme.md” file provides all information needed to run or modify “ClassifyStorms” from the GitLab source. The software depends on 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), which you can also download under “Files”. It contains 1. the HMC index: a three-hour running mean with weights [0.25,0.5,0.25] of the original Hourly Magnetospheric Currents index (HMC index, http://doi.org/10.5880/GFZ.2.3.2018.006). 2. the geomagnetic observatory data: vector geomagnetic disturbances from 34 mid-latitude observatories during 1900-2015 in the Cartesian Centered Dipole coordinate system. The original observatory data was downloaded from the WDC for Geomagnetism, Edinburgh (http://www.wdc.bgs.ac.uk/) and processed as described in section 2.1 of Pick et al. (2019). 3. the “reference” geomagnetic storms: universal time hours of 868 geomagnetic storm peaks together with their interplanetary drivers (class labels 0 or 1, see above) as described in section 2.2 of Pick et al., 2019. These events are taken from published lists (Jian et al., 2006a, 2006b, 2011; Shen et al., 2017; Turner et al., 2009), which are gathered in the separate ASCII file “ReferenceEvents.txt” (under “Files”) for a quick overview. 4. additional quantities for plotting: time series of Kp (since 1932) and Dst (since 1957) geomagnetic indices from the WDC for Geomagnetism, Kyoto (http://wdc.kugi.kyoto-u.ac.jp/wdc/Sec3.html) as well as the yearly mean total sunspot number from WDC-SILSO, Royal Observatory of Belgium, Brussels (http://sidc.be/silso/datafiles). The output of ClassifyStorms is "StormsClassified.csv" (under “Files”). 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 the corresponding probability (between 0 and 1). Version history:20 Sep 2019: Version 1.0.1: Correction of plotting mistake in Figure m / Figure S4 (see gitlab repository for details)
Contributors:Matuschek, Hannes, Mauerberger, Stefan
This library implements several functions to convert points and fields between several reference coordinate systems (e.g., GEO, SM and MAG) and different representations (carthesian, spherical) within each reference system. The aim of this library is to collect all functions needed to perform coordinate system transformations in a consistent and encapsulated way.