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1,121 results for “weathering”
Renewable Generation Profiles and Weather Correlated Loads Used in CEC EPC-19-056 Final Report
<p>This repository contains 8760 hour time series data for load and renewable generation utilized in the <br> CEC EPC-19-056 Final Report: Assessing the Value of LDES in California. The details of how these profiles were constructed can be found in Appendix B of the report.</p> <p>The load balancing authorities and renewable resources modeled in this study are consistent with those found in the 2019-20 CPUC IRP Inputs and Assumptions, which can be found here: https://www.cpuc.ca.gov/-/media/cpuc-website/divisions/energy-division/documents/integrated-resource-plan-and-long-term-procurement-plan-irp-ltpp/2019-2020-irp-events-and-materials/inputs--assumptions-2019-2020-cpuc-irp_20191106.pdf</p>
R SCRIPT: INFLUENCE OF ENVIRONMENTAL FACTORS ON THE POWER PRODUCED BY PHOTOVOLTAIC PANELS ARTIFICIALLY WEATHERED
<p>The files describe the r-script code and data base used in the article titled: "INFLUENCE OF ENVIRONMENTAL FACTORS ON THE POWER PRODUCED BY PHOTOVOLTAIC PANELS ARTIFICIALLY WEATHERED"</p>
After the sun: A nanoscale comparison of the surface chemical composition of UV and soil weathered plastics data
<p>The present folder contains the raw data as well as the R script used in the following publication : <em>After the sun: A nanoscale comparison of the surface chemical composition of UV and soil weathered plastics. </em>To extract the data:</p> <p> </p> <ol> <li>Download the .zip file and extract all the files it contains in a folder, without changing the hierarchy</li> <li>Open the Rproject by double-clicking on it</li> <li>Run the script and explore the outputs in the <em>Script_01_output </em>folder</li> </ol> <p>The data extracted from the HDF5 files before any processing are saved as .csv files in the <em>CSV </em>folder.</p>
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands
<p>This repository contains CLM5 model soil nitrogen (N2O, NO and NH3) output and metadata, and soil N<sub>2</sub>O fluxes from the Energy Farm ERW field trials used in the paper "Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands" (https://gmd.copernicus.org/preprints/gmd-2023-47/).</p>
Weather Data Cutouts for the Wasserstoffatlas (Hydrogen Map)
<p>The cutouts are required to calculate the solar PV and wind onshore feed-in profiles of the <a href="http://wasserstoffatlas.de">Wasserstoffatlas</a>.</p> <p>The provided <strong>cutouts </strong>are spatiotemporal subsets of the European weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul> <p><strong>CMSAF SARAH-2</strong></p> <ul> <li>Pfeifroth, Uwe; Kothe, Steffen; Müller, Richard; Trentmann, Jörg; Hollmann, Rainer; Fuchs, Petra; Werscheck, Martin (2017): Surface Radiation Data Set - Heliosat (SARAH) - Edition 2, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V002, <a href="https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002">https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002</a>.</li> <li><strong>Terms of Use:</strong> <a href="https://www.eumetsat.int/cs/idcplg?IdcService=GET_FILE&dDocName=pdf_leg_data_policy&allowInterrupt=1&noSaveAs=1&RevisionSelectionMethod=LatestReleased">https://www.eumetsat.int/cs/idcplg?IdcService=GET_FILE&dDocName=pdf_leg_data_policy&allowInterrupt=1&noSaveAs=1&RevisionSelectionMethod=LatestReleased</a></li> </ul>
IWIN: The Isfjorden Weather Information Network (August 2021 - June 2023)
<p>The Isfjorden Weather Information Network provides standard meteorological near-surface measurements from the Isfjorden region in Svalbard. The network includes weather stations permanently installed on lighthouses around the fjord and onboard small tourist cruise ships trafficking the fjord from the spring to the autumn. A defined (fixed-end) data set covering the period August 2021 - June 2023 is available here, while new observations become available in near real-time via the data portals of MET Norway (https://doi.org/10.21343/ebrw-w846; https://thredds.met.no/thredds/unis-obs/unis-obs.html).</p>
Supporting Dataset for "Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system"
<p>This archive supports the submission of "Reducing a tropical cyclone weak intensity bias in a global numerical weather prediction system" to Monthly Weather Review. It contains model configurations, the software used to create ensemble perturbations, the software used to compute the diagnostics discussed in the text, and the software use to plot figures.</p> <p>After downloading, the contents can be extracted using:</p> <blockquote> <p>tar -xzf idealtc1_archive-1.tgz</p> </blockquote>
GPR data collected near the Chepeta Weather Station and the DUST-1 sampler, Uinta Mountains, Utah
Ground penetrating radar data collected on September 9, 2021 in the Uinta Mountains at the Chepeta Remote Automated Weather Station (RAWS) and the DUST-1 passive dust sampler. Data were collected with a GSSI SIR-4000 control unit and a 350HS antenna connected to an Emlid Reach RS2 GPS receiver. Data files have been distance normalized and field-applied range gains have been removed before being exported in .sgy format. Files were also exported in .kml format for viewing the transect locations in Google Earth. Two long transects (780 feet each) were collected. The "West" transect passed to the west of the Chepta RAWS; the "East" transect passed to the east. The transects started at different points along the northern lip of the summit upland and came together at a common point at their southern ends. Marks were made in the data file every 60 feet while surveying; these marks were used to distance normalize the results. The system collected 334 scans/second with 512 samples/scan while surveying. Two 30-foot perpendicular transects were also surveyed (north to south, and west to east) with their intersection adjacent to a soil pit excavated to a depth of 92 cm. The location of the soil pit was noted in each transect with a mark near 16 feet. Data were used to evaluate spatial variations in the thickness of regolith overlying the bedrock beneath this gently sloping summit flat.
Unravelling arthropod movement in natural landscapes: small-scale effects of body size and weather conditions
<p>This dataset was used to run the analyses for Logghe et al. (2024). Unravelling arthropod movement in natural landscapes: small-scale effects of body size and weather conditions. Journal of Animal Ecology, 93(9), 1365-1379. <a href="https://doi.org/10.1111/1365-2656.14161">https://doi.org/10.1111/1365-2656.14161</a></p> <p>"Arthropod_movement.csv" contains the results of experimental fieldwork, where movement speed and direction were recorded for individuals belonging to multiple arthropod species. Each row contains data on taxonomy, weather conditions, movement speed and movement direction of an individual measurement. Metadata is provided for the different columns.</p> <p>"Summary_observations.csv" gives an overview of the amount of individuals that were tested for each species. This dataset separates flying and cursorial movement, since several species were able to switch between these two modes.</p> <p><strong>Abstract</strong></p> <p><strong>Background: </strong>Movement is a crucial life history component and holds direct significance to population dynamics, thereby influencing population viability. For arthropods in general, larger species achieve greater dispersal distances and large scale movements are influenced by weather conditions. However, many aspects of arthropod movement behaviour remain relatively unexplored, especially on small spatial scales. Studies on this topic are scarce and often limited to a few specific species or laboratory conditions. Consequently, it remains uncertain whether the effects of body size and weather conditions can be generalized across a wide range of arthropod species in natural environments.</p> <p><strong>Methods: </strong>To help address this knowledge gap, we conducted a field study in two nature reserves in Belgium, focusing on both flying and cursorial arthropods. Over 200 different arthropod species were captured and released within a circular setup, allowing quantification of movement speed and direction. By analysing the relationship between these movement variables and morphological (body size) as well as environmental factors (temperature and wind), we aimed to gain insights into the mechanisms driving small-scale arthropod movement under natural conditions.</p> <p><strong>Results: </strong>For flying species, movement speed is positively correlated with both body size and (tail)wind speed. In contrast, movement speed of cursorial individuals was solely positively related with temperature. Notably, movement direction was biased towards the vegetated areas where the arthropods were originally caught, suggesting an internal drive to move towards suitable habitat. This tendency was particularly strong in larger flying individuals, in smaller cursorial species and under tailwind conditions. Furthermore, both flying and cursorial taxa were hindered from moving towards the habitat by strong upwind.</p> <p><strong>Conclusions: </strong>Body size can be used as a useful proxy for not only movement speed, but orientation capacity as well. This movement-size correlation is, at least at small temporal scales, conditional to the prevailing wind conditions.</p>
Digitisation of Weather Records of Seungjeongwon Ilgi: A Historical Weather Dynamics Dataset of the Korean Peninsula (1623-1910)
<p><strong>Introduction</strong></p> <p>This study has exploited the daily weather records of Seungjeongwon Ilgi from the NIKH database (http://sjw.history.go.kr/main.do). Seungjeongwon Ilgi is a daily record of the Seungjeongwon, the Royal Secretariat of the Joseon Dynasty of Korea. These diaries span from 1623 to 1910 and generally involve daily weather records in the entry header. Their observational site would be located in Seoul (N37°35′, E126°59′). We have exploited the weather records from the NIKH database and classified the daily weather using text mining method. We have also converted the report dates from the traditional lunisolar calendar to the Gregorian calendar, to better contextualise our data into the contemporary daily measurements.</p> <p><strong>Data</strong></p> <p>We provide different formats (csv, xlsx, json) to facilitate the usage of data. The main contents of data are listed as below.</p> <ul> <li><strong>ID</strong>: The unique identifier of a specific record in the metadata, which can also serve as the identifier to merge with external data in the NIKH digital database.</li> <li><strong>Traditional calendar</strong>: The original lunar dates in the NIKH digital database, which are listed in data format "YYYY-MM-DD". More specifically, "L0" implies the leap year and "L1" implies the common year.</li> <li><strong>Leap</strong>: The identifier of a leap year.</li> <li><strong>Gregorian calendar</strong>: The Gregorian calendar date that converted by the traditional calendar date.</li> <li><strong>Weather Text</strong>: The text that describe the weather conditions. Specifically, multiple weather descriptions of the same day have been put together.</li> <li><strong>Flag</strong>: The computed value that indicates different combinations of weather conditions.</li> <li><strong>Volume</strong>: The volume of text in the original record.</li> <li><strong>Herbal Volume</strong>: The volume of text in the herbal record.</li> <li><strong>Sunny</strong>: A dummy variable that represents whether the weather description contains the expression of sunny.</li> <li><strong>Cloudy</strong>: A dummy variable that represents whether the weather description contains the expression of cloudy.</li> <li><strong>Rainy</strong>: A dummy variable that represents whether the weather description contains the expression of rainy.</li> <li><strong>Snow</strong>: A dummy variable that represents whether the weather description contains the expression of snow.</li> <li><strong>Wind</strong>: A dummy variable that represents whether the weather description contains the expression of wind.</li> </ul> <p><strong>Import Data</strong></p> <pre><code class="language-python"># Python # CSV file import pandas as pd data=pd.read_csv('~/SJWilgi_Seoul_Weather_YR1623_1910.csv',encoding="utf-8") # JSON file data=pd.read_json('~/SJWilgi_Seoul_Weather_YR1623_1910.json',encoding="utf-8") # Excel file data=pd.read_excel('~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx') # Excel file</code></pre> <pre><code class="language-bash"># R # CSV file library(readr) data<- read_csv("~/SJWilgi_Seoul_Weather_YR1623_1910.csv") # Excel file library(readxl) data <- read_excel("~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx")</code></pre> <p> </p>
Mechanisms leading to exceptional niobium concentration during lateritic weathering: the key role of secondary oxides
<p>This dataset supports the publication entitled: 'Mechanisms leading to exceptional niobium concentration during lateritic weathering: the key role of secondary oxides' submitted to <em>Chemical Geology</em> journal.</p> <p>Data include:</p> <p>- EPMA data: 'Fe_ox_EPMA', ...</p> <p>- XRD data: 'SL_02.dat', ...</p> <p>- XAS data on bulk samples 'XAS_SL_12_meascan3'...</p> <p>- EXAFS data: 'EXAFS_nbbrookite_R_space.csv', ...</p> <p><strong>SL02: Fragmented laterite/SL04: Fragmented laterite/SL12: Upper purple laterite/SL14: manganiferous laterite/SL23: lower purple laterite/SL31: brown laterite/SL_CARB: Core siderite carbonatite</strong></p>
Direct evidence for atmospheric carbon dioxide removal via enhanced weathering in cropland soil: Supporting data
<p>Datasets (climate, alkalinity, moisture sensors) associated with the manuscript "Direct evidence for atmospheric carbon dioxide removal via enhanced weathering in cropland soil."</p>
Huge ensembles part I design of ensemble weather forecasts with spherical Fourier neural operators; Huge ensembles part II properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators
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Data from: Impacts of weathered microplastic ingestion on gastrointestinal microbial communities and health endpoints in fathead minnows (Pimephales promelas)
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Differential responses to weather and land-cover conditions explain spatial variation in winter abundance trends in a migratory bird of conservation concern
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Sex, body size, and winter weather explain migration strategies in a partial migrant population of American Kestrels (Falco sparverius)
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Early-season biomass and weather enable robust cereal rye cover crop biomass predictions
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Supplementing enhanced weathering with organic amendments accelerates the net climate benefit in rangeland soils
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Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model
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Big Spirit Lake Monitoring Buoy: High Frequency Oxygen, Temperature, Water Quality and Weather Data 2020-2021
High frequency weather and limnological data from the Big Spirit Lake monitoring buoy, Iowa, USA, from 2020 - 2021. The Big Spirit Lake (Global Lake Ecological Observatory Network—GLEON) instrumented monitoring buoy is operated by the Iowa Lakeside Laboratory (https://iowalakesidelab.org/). The buoy is equipped with a thermistor chain and dissolved oxygen probes the measure every meter from 1m to 6m in depth, a multi-parameter sonde at 1 meter depth, as well as a weather station and wave sensor (added in 2021) situated on the buoy. All data in this data package have been QAQC'd to remove obviously errant readings, highly suspicious readings, and artifacts of buoy maintenance. Because of this, as well as sensor errors and failure, not all data is available at every point each year. Measured parameters in the data set include water temperature, dissolved oxygen concentration, and dissolved oxygen saturation (measured every meter from 1m to 6m). Other measured parameters include pH, specific conductivity, chlorophyll, and blue-green algae in the surface water, as well as weather station data and wave sensor data. Sampling Frequency: Parameters are measured at 10 minute intervals, and most years include measurements from mid/late May through October.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.