131 documents found in 278ms
# 1
Mills, Steven • Williams, Jack
Abstract: This code (nwrap.ijm) can be used to generate an 'unrolled' circumferential image of a tomographic drill-core scan, such as an X-ray Computed Tomography (CT) scan. The resulting image is analogous to those produced by a DMT CoreScan system®. By comparing such images to geographically references borehole televiewer data, it may be used to reorientate drill-core back into geographic space (Williams et al. submitted). This code should be installed and run as a plugin on ImageJ/Fiji. Full instructions are given in the code and in the Appendix A of Williams et al. (submitted). Examples of unrolled CT scans can be found at Williams et al (2017, http://doi.org/10.5880/ICDP.5052.004).
# 2
Brunke, Heinz-Peter
Abstract: This data publication includes a matlab software package as described in Brunke (2017). In addition to the Matlab software, we provide three test dataset from the Niemegk magnetic observatories (NGK). We present a numerical method, allowing for the evaluation of an arbitrary number (minimum 5 as there are 5 independent parameters) of telescope orientations. The traditional measuring schema uses a fixed number of eight orientations (Jankowski et al, 1996). Our method provides D, I and Z base values and calculated uncertitudes of them. A general approach has significant advantages. Additional measurements may by seamlessly incorporate for higher accuracy. Individual erroneous readings are identified and can be discarded without invalidating the entire data set, a-priory information can be incorporated. We expect the general method to ease requirements also for automated DI-flux measurements. The method can reveal certain properties of the DI-theodolite, which are not captured by the conventional method. Based on the alternative evaluation method, a new faster and less error prone measuring schema is presented. It avoids the need to calculate the magnetic meridian prior to the inclination measurements. Measurements in the vicinity of the magnetic equator become possible with theodolites without zenith ocular.
# 3
Eggert, Daniel • Sips, Mike • Dransch, Doris
Abstract: gms-vis is a web-based implementation of our visual-analytics approach for assessing remote-sensing data. It is implemented based on the GWT framework. Once deployed through a webserver it acts as the user interface for the GeoMultiSens (GMS) platform. Within the interface users can intuitively define spatial, temporal as well as quality constraints, for remote sensing scenes. A heatmap enables the user to assess the spatial distribution of selected scenes, while a time histogram allows the user to assess their temporal distribution. Finally, users can specify a workflow which will be executed by the GeoMultiSens platform. Though gms-vis is part of the GeoMultiSens platform, it is relatively self-contained and can be attached to different analysis frameworks and platforms with reasonable effort.
# 4
Albrecht, Torsten
Abstract: This dataset contains PISM simulation results (http://www.pism-docs.org) of the Antarctic Ice Sheet based on code release pik-holocene-gl-rebound: http://doi.org/10.5281/zenodo.1199066 . With the help of added python scripts, Fig. 3 and other model related extended data figures can be reproduced as in the journal publication: *Kingslake, Scherer, Albrecht et al.* **Nature**, forthcoming.
PISM is the open-source Parallel Ice Sheet Model developed mainly at UAF, USA and PIK, Germany.Plottings scripts for figures in 'plot_scripts' access the uploaded PISM results (netCDF data) and save them to 'final_figures'. The bash script 'preprocessing.sh' downloads and converts forcing input data for the plots based on https://github.com/pism/pism-ais. See README.md for information on experiment (ensemble numbers) and information on access of input data.
# 5
Rawald, Tobias • Sips, Mike • Dransch, Doris
Abstract: PyRQA is a tool to conduct recurrence quantification analysis (RQA) and to create recurrence plots in a massively parallel manner using the OpenCL framework. It is designed to process very long time series consisting of hundreds of thousands of data points efficiently.
# 6
Dietze, Michael
Abstract: Environmental seismoloy is a scientific field that studies the seismic signals, emitted by Earth surface processes. This R package eseis provides all relevant functions to read/write seismic data files, prepare, analyse and visualise seismic data, and generate reports of the processing history. eseis contains a growing set of function to handle the complete workflow of environmental seismology, i.e., the scientific field that studies the seismic signals that are emitted by Earth surface processes. The package supports reading the two most common seismic data formats, general functions for preparational and analytical signal processing aswell as specified functions for handling signals generated by Earth surface processes. Finally, graphical plot functions are provided, too. The software package contains 51 functions and two example data sets (eseis-supplementary_material.zip). It makes use of a series of dependency packages described in the DESCRIPTION file of the package.
# 7
Eggert, Daniel • Köthur, Patrick • Dransch, Doris
Abstract: The processing of Persistent Scatterer Interferometry (PSI) data and the estimation of displacement is a nonlinear and user-driven procedure that can introduce large errors for noisy backscatter points. Results may differ significantly depending on chosen thresholds, filter settings, constraints and final interpretation. Thus the identification of valid PS with rather low errors in the SAR data is a crucial step in the PSI workflow. PSI-Explorer is a scientific prototype of our visual-analytics (VA) approach supporting this important task. The prototype is written in Java and operates on Matlab files.
# 8
Hempel, Sabrina • Frieler, Katja • Warszawski, Lila • Schewe, Jacob • Piontek, Franziska
Abstract: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) is a community-driven modelling effort bringing together impact models across sectors and scales to create consistent and comprehensive projections of the impacts of different levels of global warming. This entry holds the input data of the ISIMIP Fast Track Initiative consisting of bias corrected daily data for from the following five CMIP5 Global Climate Models (GCMs): GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M. Bias corrections has been processed by Sabrina Hempel at PIK and is described in "A trend-preserving bias correction -- the ISIMIP approach" by Hempel et al. (2013)The input data section of the ESGF project referenced in this entry holds the initial version of the bias-corrected GCM input data and was used to force impact models in the ISIMIP Fast Track phase. It should only be used for the ISIMIP2 catch-up experiments for sectors that were already part of the Fast Track phase. For all other purposes, i.e. future runs for new ISIMIP 2 sectors and modeling exercises with no relation to ISIMIP, the corrected and extended version published under the ISIMIP 2 ESGF project should be used. It overcomes several limitations in adjusting the daily variability (denoted as ISIe in Hempel et al., 2013). Data access links are provided to the PIK node of the Earth System Grid Federation (ESGF, https://esg.pik-potsdam.de/). There is currently no directly linked data available, please take a look at the input data of the ISIMIP Fast Track Initiative via https://esg.pik-potsdam.de/search/isimip-ft/. For technical support please have a look at the ESGF FAQ (http://esgf.github.io/esgf-swt/index.html) and the tutorials (https://www.earthsystemcog.org/projects/cog/tutorials_web).
Statistical bias correction is commonly applied within climate impact modeling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Thereby the climate signal is modified in a way not necessarily preserving the trend of the original climate model data. Here, we present the bias correction method that was developed within ISIMIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISIMIP is designed to synthesize impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. However, bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied bias correction method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISIMIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (WaterMIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean.
# 9
Deng, Zhiguo • Fritsche, Mathias • Nischan, Thomas • Bradke, Markus
Abstract: The German Research Center for Geosciences (GFZ) is providing ultra rapid multi-GNSS orbit-, clock- and EOP-product series (EOP = Earth Orientation Parameters). The Orbit/Clock product covers the following Global Navaigation Satellite Systems (GNSS)- GAL (Galileo) / Europe- GPS (GPS) / USA- GLO (Glonass) / Russian Federation- BDS (Beidou) / PR China- QZS (QZSS) / Japan All products are estimated using the latest version of GFZ's automated EPOS-8 GNSS processing environment and using global hourly RINEX observation data of the International GNSS Service (IGS). The orbit/clock product is provided:- in the SP3-c data format,- every 3 hours with a nominal latency of 2 hours after the last observation,- with a 48 hour sliding window orbit duration (1) the first 24 hours are estimated based on real GNSS RINEX observation data, (2) the second 24 hours consist of an orbit/clock prediction,- the orbit positions epoch interval is of 15 minutes. The EOP product is provided:- in the IGS ERP data format,- every 3 hours with a nominal latency of 2 hours after last observation,- with one estimated 24 hour EOP record based on real GNSS RINEX observation data,- with one predicted 24 hour EOP record. For recent (latest) products used for routine applications a registration via mgnss@gfz-potsdam.de is needed to get special access. Products with an age older than 2 days are available without restrictions. For the used data formats see Kouba and Mierault (2010, https://igscb.jpl.nasa.gov/igscb/data/format/erp.txt) for the description of the EOP Product Series and Hilla (2010, https://igscb.jpl.nasa.gov/igscb/data/format/sp3c.txt) for the description of the Orbit-/ Clock format SP3-c). The time series are provided in weekly folders, beginning with 27 May 2015 (GPS Week 1846).
# 10
Lange, Stefan
Abstract: The EWEMBI dataset was compiled to support the bias correction of climate input data for the impact assessments carried out in phase 2b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b; Frieler et al., 2017), which will contribute to the 2018 IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways. The EWEMBI data cover the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2013. Data sources of EWEMBI are ERA-Interim reanalysis data (ERAI; Dee et al., 2011), WATCH forcing data methodology applied to ERA-Interim reanalysis data (WFDEI; Weedon et al., 2014), eartH2Observe forcing data (E2OBS; Calton et al., 2016) and NASA/GEWEX Surface Radiation Budget data (SRB; Stackhouse Jr. et al., 2011). The SRB data were used to bias-correct E2OBS shortwave and longwave radiation (Lange, 2018). Variables included in the EWEMBI dataset are Near Surface Relative Humidity, Near Surface Specific Humidity, Precipitation, Snowfall Flux, Surface Air Pressure, Surface Downwelling Longwave Radiation, Surface Downwelling Shortwave Radiation, Near Surface Wind Speed, Near-Surface Air Temperature, Daily Maximum Near Surface Air Temperature, Daily Minimum Near Surface Air Temperature, Eastward Near-Surface Wind and Northward Near-Surface Wind. For data sources, units and short names of all variables see Frieler et al. (2017, Table 1).
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