Other language confidence: 0.9979339083116571
The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b).Version 1.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2016. Data sources of W5E5 are version 1.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2019), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003).Variables (with short names and units in brackets) included in the W5E5 dataset are Near Surface Relative Humidity (hurs, %), Near Surface Specific Humidity (huss, kg kg-1), Precipitation (pr, kg m-2 s-1), Snowfall Flux (prsn, kg m-2 s-1), Surface Air Pressure (ps, Pa), Sea Level Pressure (psl, Pa), Surface Downwelling Longwave Radiation (rlds, W m-2), Surface Downwelling Shortwave Radiation (rsds, W m-2), Near Surface Wind Speed (sfcWind, m s-1), Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and WFDE5-ERA5 Mask (mask, 1).W5E5 is a merged dataset. It combines WFDE5 data over land with ERA5 data over the ocean. The mask used for the merge is included in the dataset. The mask is equal to 1 over land and equal to 0 over the ocean.Over land, orog is the surface altitude used for elevation corrections in WFDE5. For all other variables already included in WFDE5 (huss, prsn, ps, rlds, rsds, sfcWind, tas), W5E5 data over land are equal to the daily mean values of the corresponding hourly WFDE5 data. W5E5 hurs over land is the daily mean of hourly hurs computed from hourly WFDE5 huss, ps, and tas using the equations of Buck (1981) as described in Weedon et al. (2010). W5E5 pr over land is the daily mean of the sum of hourly WFDE5 rainfall and snowfall. Note that W5E5 pr and prsn over land are based on WFDE5 rainfall and snowfall bias-adjusted using GPCC monthly precipitation totals. W5E5 psl over land is the daily mean of hourly psl computed from hourly WFDE5 orog, ps, and tas according to psl = ps * exp((g * orog) / (r * tas)), where g is gravity, and r is the specific gas constant of dry air. Lastly, W5E5 tasmax and tasmin over land are the daily maximum and minimum, respectively, of hourly WFDE5 tas.Over the ocean, W5E5 data are based on temporally (from hourly to daily resolution) and spatially (from 0.25° to 0.5° horizontal resolution) aggregated ERA5 data. The spatial aggregation using first-order conservative remapping was always done after the temporal aggregation. For tasmax and tasmin, hourly tas values were aggregated to daily maximum and minimum values, respectively. For all other variables, hourly values were aggregated to daily mean values. Variables unavailable in ERA5 (huss, hurs, sfcWind, orog) were first derived from available variables at hourly temporal and 0.25° horizontal resolution and then aggregated like all other variables. huss and hurs were derived from Near Surface Dewpoint Temperature, ps, and tas using the equations of Buck (1981) as described in Buck (2010). sfcWind was derived from Eastward Near-Surface Wind (uas) and Northward Near-Surface Wind (vas) according to sfcWind = sqrt(uas * uas + vas * vas). orog is equal to surface geopotential divided by gravity. Lastly, pr and prsn were bias-adjusted such that monthly W5E5 precipitation totals match GPCP version 2.3 values over the ocean. Monthly rescaling factors used for this purpose were computed following the scale-selective rescaling procedure described by Balsamo et al. (2010).
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 the SedDB database 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 ("Region", "Sub-region", "Location", which relate to location at continental to city/river scale) or with the sample types (terrestrial samples – “aerosols”, “soil sediments”, “river sediments”, “rocks” - or marine samples –“marine sediment” or “trap sample”). Thirdly, samples were discriminated according to their deposition age, which allowed to compute average values for specific time intervals (see attached table "Age_determination_Sediment_Cores_V2.txt"). A first version of the database was published in September 2018 and presented data for the African sector. A second version was published in April 2019, in which the dataset has been extended to reach a global extent. The dataset will be further updated bi-annually to increase the geographical resolution and/or add other type of samples.This dataset consists of two tab separated tables: "Dataset_Nd_Sr_isotopes_V2.txt" and "Age_determination_Sediment_Cores_V2.txt". "Dataset_Nd_Sr_isotopes_V2.txt" contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. "Age_determination_Sediment_Cores_V2.txt" contains all background information concerning the determination of the isotopic signature of specific time intervals (depth interval, number of samples, mean and standard deviation). Column headers are explained in respective metadata comma-separated files. A full reference list is provided in the file “References_Database_Nd_Sr_isotopes_V2.rtf”. Finally, R code for mapping the data and running statistical analyses is also available for this dataset (Blanchet, 2018b, 2018a).
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 (see supplemental references).The database has been built by incorporating data from the literature and the SedDB database 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 ("Region", "Sub-region", "Location", which relate to location at continental to city/river scale) or with the sample types (terrestrial samples – “aerosols”, “soil sediments”, “river sediments” - or marine samples –“marine sediment” or “trap sample”). Thirdly, samples were discriminated according to their deposition age, which allowed to compute average values for specific time intervals (see attached table "Age_determination_Sediment_Cores.csv"). The dataset will be updated bi-annually and might be extended to reach a global geographical extent and/or add other type of samples.This dataset contains two csv tables: "Dataset_Nd_Sr_isotopes.csv" and "Age_determination_Sediment_Cores.csv". "Dataset_Nd_Sr_isotopes.csv" contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. "Age_determination_Sediment_Cores.csv" contains all background information concerning the determination of the isotopic signature of specific time intervals (depth interval, number of samples, mean and standard deviation). Column headers are explained in respective metadata comma-separated files. A human readable data description is provided in portable document format, as well. Finally, R code for mapping the data and running statistical analyses is also available for this dataset (see supplemental references).
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 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.
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).
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 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).
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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