Other language confidence: 0.9979339083116571
This dataset provides a comprehensive, high-resolution global record of monthly Terrestrial Water Storage Anomalies (TWSA) from April 2002 to December 2022. It was generated to address the spatial resolution limitations of raw satellite gravimetry observations from the GRACE and GRACE-FO missions, offering a product suitable for regional and basin-scale hydrological analysis. The dataset was simulated using a novel deep learning framework. This model spatially downscales the low-resolution (~300 km) JPL GRACE/GRACE-FO Mascon solutions (Watkins et al., 2015; Wiese et al., 2016; Landerer et al., 2020; Wiese et al., 2023) to a high resolution of 50 km (0.5-degree grid). The core of the methodology is a dynamic soft-constrained paradigm, where the model is simultaneously guided by the observational accuracy of GRACE/GRACE-FO data and the high-resolution spatial patterns from the WaterGAP Global Hydrology Model (Müller Schmied et al., 2023) and ERA5 reanalysis data (Hersbach et al., 2023). The influence of these constraints is dynamically weighted at each training step based on the evolving correlation between the model's prediction and the high-resolution inputs, ensuring an optimal simulation of observational integrity and high-resolution detail.
The REHEATFUNQ Python package helps to work with the (residual) scatter of surface heat flow even in small regions. REHEATFUNQ uses a stochastic model for regional aggregate heat flow distributions (RAHFD), that is, the collected set of heat flow measurements within a region marginalized to the heat flow dimension. The stochastic model is used in a Bayesian analysis that (1) yields a posterior estimate of the RAHFD which captures the range of heat flow within the analysis region, and (2) quantifies the magnitude of a surface heat flow anomaly within the region, for instance through the generating frictional power. The stochastic model underlying REHEATFUNQ views heat flow data, uniformly sampled across the region of interest, as a random variable. A gamma distribution is used as a model for this random variable and information from the global data set of Lucazeau (2019) is introduced by means of a conjugate prior (Miller, 1980). The detailed science behind the model is described in Ziebarth et al. (202X). The analysis by Ziebarth et al. (202X) can be reproduced through the Jupyter notebooks contained in the subdirectory “jupyter/REHEATFUNQ/”. The location specified in the map below covers the region to which REHEAFUNQ is applied in this analysis. REHEATFUNQ is a Python package that uses a compiled Cython/C++ backend. Compiling REHEATFUNQ requires the Meson build system and a number of scientific libraries and Python packages (and their dependencies) that are listed in the documentation. A Docker image “reheatfunq” is provided as an alternative means of installation. The Docker image comes in two flavors, specified in “Dockerfile” and “Dockerfile-stable”. The former is based on the current “python:slim” image and downloads further dependencies through the Debian package manager, leading to a short image generation time. The latter bootstraps the REHEATFUNQ dependencies from source, aiming to create a reproducible model. To do so, “Dockerfile-stable” depends on the sources contained in “vendor-1.3.3.tar.xz”. If you plan to build the stable image, download both “REHEATFUNQ-1.3.3.tar.gz” and “vendor-1.3.3.tar.xz”, and see the README contained in the latter. Later versions of the “REHEATFUNQ” archive are compatible with the latest “vendor” archive. A quickstart introduction and the API documentation can be found in the linked documentation.
This data publication contains seismic catalog developed by the analysis of seismicity recorded during hydraulic stimulation campaign performed in May 2020 in the 5.8-km deep OTN-2 well near Helsinki, Finland as part of the St1 Deep Heat project (Kwiatek et al., 2022). The original seismic data to develop the seismic catalog were acquired with the high-resolution seismic network composed of 22 geophones surrounding the project site. The centerpiece of the network was a 10-level borehole array of Geospace OMNI-2400 geophones (3C/15 Hz) sampled at 2 kHz placed in the OTN-3 well adjacent to the OTN-2 injection well, and located at 1.93 - 2.55 km depth, approx. 3km from injection intervals. Additional 12 stations at distances <10 km from project site formed the satellite network that was equipped with short-period 3C 4.5 Hz Sunfull PSH geophones, completing the seismic network. Near-real-time processing of induced seismicity data started on Jan 26, 2020, i.e. about 3 months prior to the onset of the injection, covering entire period of the stimulation campaign in May 2020. The monitoring stopped end of June 2020, about one month after the stimulation finished. The monitoring campaign resulted in initial industrial seismicity catalog containing 6,243 events that was refined and further extended (cf. Kwiatek et al., 2022). The final catalog associated with this data publication contains 6,318 earthquakes, including 197, 5427 and 694 events recorded before, during, and after stimulation campaign. The core catalog data contains origin time, local magnitude, (re)location and focal mechanism data.
The success of scientific projects increasingly depends on using data analysis tools and data in distributed IT infrastructures. Scientists need to use appropriate data analysis tools and data, extract patterns from data using appropriate computational resources, and interpret the extracted patterns. Data analysis tools and data reside on different machines because the volume of the data often demands specific resources for their storage and processing, and data analysis tools usually require specific computational resources and run-time environments. The data analytics software framework DASF, developed at the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de) and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/), provides a framework for scientists to conduct data analysis in distributed environments. The data analytics software framework DASF supports scientists to conduct data analysis in distributed IT infrastructures by sharing data analysis tools and data. For this purpose, DASF defines a remote procedure call (RPC) messaging protocol that uses a central message broker instance. Scientists can augment their tools and data with this protocol to share them with others. DASF supports many programming languages and platforms since the implementation of the protocol uses WebSockets. It provides two ready-to-use language bindings for the messaging protocol, one for Python and one for the Typescript programming language. In order to share a python method or class, users add an annotation in front of it. In addition, users need to specify the connection parameters of the message broker. The central message broker approach allows the method and the client calling the method to actively establish a connection, which enables using methods deployed behind firewalls. DASF uses Apache Pulsar (https://pulsar.apache.org/) as its underlying message broker. The Typescript bindings are primarily used in conjunction with web frontend components, which are also included in the DASF-Web library. They are designed to attach directly to the data returned by the exposed RPC methods. This supports the development of highly exploratory data analysis tools. DASF also provides a progress reporting API that enables users to monitor long-running remote procedure calls. One application using the framework is the Digital Earth Flood Event Explorer (https://git.geomar.de/digital-earth/flood-event-explorer). The Digital Earth Flood Event Explorer integrates several exploratory data analysis tools and remote procedures deployed at various Helmholtz centers across Germany.
Fatbox - Fault Analysis Toolbox is a python module for the extraction and analysis of faults (and fractures) in raster data. We often observer faults in 2-D or 3-D raster data (e.g. geological maps, numerical models or seismic volumes), yet the extraction of these structures still requires large amounts of our time. The aim of this module is to reduce this time by providing a set of functions, which can perform many of the steps required for the extraction and analysis of fault systems. The basic idea of the module is to describe fault systems as graphs (or networks) consisting of nodes and edges, which allows us to define faults as components, i.e. sets of nodes connected by edges, of a graph. Nodes, which are not connected through edges, thus belong to different components (faults).
FlotteKarte is a low-overhead plotting routine using Matplotlib, NumPy, and PyPROJ under the hood. The conceptual idea behind this package is that a map is fully defined through the 2D cartesian coordinates that result from applying the map projection to different geographical data. For displaying data on a two-dimensional canvas, Matplotlib is a powerful tool. Conversion between geographic and projected coordinates can easily be done using PyProj. The gap between these two powerful tools and a polished map lies in potential difficulties when translating spherical line topology to 2D cartesian space, and by introducing typical map decorations such as grids or ticks. FlotteKarte aims to fill this gap with a simple interface. FlotteKarte's philosophy is to work completely within the 2D projected coordinates, that is, very close to the projected data. If projected coordinates of data can be obtained, the data can be drawn directly on the underlying Matplotlib Axes. The Map class can then be used to add typical map decoration to that axes using information that it derives from the numerics of the PROJ projection.
PDToolbox is a collection of methods helpful for doing probability distribution computations in Python. The aim of the PDToolbox Python module is to provide a set of features, based on simple probability distributions, that are not available from the scipy.stats module. This includes fast batch computations of (weighted) maximum likelihood estimates, computation of critical empirical distribution statistics, and more niche probability distributions or related code in the pdtoolbox.special module. The module contains code that is described in (ADD citations of the two articles).
VERSION HISTORY:- On June 26, 2018 all files were republished due to the incorporation of additional observational data covering years 2014 to 2016. Prior to that date, the dataset only covered years 1979 to 2013. Data for all years prior to 2014 are identical in this and the original version of the dataset.DATA DESCRIPTION: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).
The database presented here contains radiogenic neodymium and strontium isotope ratios measured on both terrestrial and marine sediments. It was compiled to help assessing sediment provenance and transport processes for various time intervals. This can be achieved by either mapping sediment isotopic signature and/or fingerprinting source areas using statistical tools (e.g. Blanchet, 2018b, 2018a). The database has been built by incorporating data from the literature and various databases and data compilations, and harmonizing the metadata, especially units and geographical coordinates. The original data were processed in three steps. Firstly, a specific attention has been devoted to provide geographical coordinates to each sample in order to be able to map the data. When available, the original geographical coordinates from the reference (generally DMS coordinates, with different precision standard) were transferred into the decimal degrees system. When coordinates were not provided, an approximate location was derived from available information in the original publication. Secondly, all samples were assigned a set of standardized criteria that help splitting the dataset in specific categories. We defined categories associated with the sample location, the type of sample, the sedimentary fraction measured, or the deposition age (as given in the original publication). This dataset consists of one spreadsheet: "Dataset_Nd_Sr_isotopes_V3.txt", which contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. A full reference list is provided in the file “References_Database_Nd_Sr_isotopes_V3.pdf”. R code for mapping the data and running statistical analyses is also available for this dataset (Blanchet, 2018b, 2018a).
The success of scientific projects increasingly depends on using data analysis tools and data in distributed IT infrastructures. Scientists need to use appropriate data analysis tools and data, extract patterns from data using appropriate computational resources, and interpret the extracted patterns. Data analysis tools and data reside on different machines because the volume of the data often demands specific resources for their storage and processing, and data analysis tools usually require specific computational resources and run-time environments. The data analytics software framework DASF, developed at the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de) and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/), provides a framework for scientists to conduct data analysis in distributed environments. The data analytics software framework DASF supports scientists to conduct data analysis in distributed IT infrastructures by sharing data analysis tools and data. For this purpose, DASF defines a remote procedure call (RCP) messaging protocol that uses a central message broker instance. Scientists can augment their tools and data with this protocol to share them with others. DASF supports many programming languages and platforms since the implementation of the protocol uses WebSockets. It provides two ready-to-use language bindings for the messaging protocol, one for Python and one for the Typescript programming language. In order to share a python method or class, users add an annotation in front of it. In addition, users need to specify the connection parameters of the message broker. The central message broker approach allows the method and the client calling the method to actively establish a connection, which enables using methods deployed behind firewalls. DASF uses Apache Pulsar (https://pulsar.apache.org/) as its underlying message broker. The Typescript bindings are primarily used in conjunction with web frontend components, which are also included in the DASF-Web library. They are designed to attach directly to the data returned by the exposed RCP methods. This supports the development of highly exploratory data analysis tools. DASF also provides a progress reporting API that enables users to monitor long-running remote procedure calls. One application using the framework is the Digital Earth Flood Event Explorer (https://git.geomar.de/digital-earth/flood-event-explorer). The Digital Earth Flood Event Explorer integrates several exploratory data analysis tools and remote procedures deployed at various Helmholtz centers across Germany.
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