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1,121 results for “weathering”
Hourly wind profile measurements at Beijing weather station for the year 2020
<p>This dataset contains the wind profiler from 0.14 to 10.38 km above ground level at 1-h intervals for the Beijing Observatory station, which is obtained from the measurements of the Radar wind profiler for the year 2020. It is stored in Matlab format, and is organized by a matrix of 21*8784. the field "h" represents its corresponding height.</p>
Dataset to manuscript "The role of weathering on morphology and rates of escarpment retreat of the rift margin of Madagascar" by Wang et al. (2023) submitted to Journal of Geophysical Research-Earth Surface.
<p>The dataset includes the relevant raw chemical element content data and the chemical weathering condition analysis of river sediment samples of Madagascar. The dataset is a supplement to the manuscript "The role of weathering on morphology and rates of escarpment retreat of the rift margin of Madagascar", by Wang et al. (2023) submitted to the Journal of Geophysical Research-Earth Surface. Commercially sensitive data is hidden but is available by request directly to the corresponding author. The data should <strong>NOT</strong> be used commercially.</p> <p>A MATLAB code for the weathering indices calculation is open-access on GitHub (https://github.com/yanyanwangesd/chemical_weathering). Please contact the corresponding author for more info or technical support on using the code. </p>
Data for: Assessment of cryogenic pretreatment for simulating environmental weathering in the formation of surrogate micro- and nanoplastics from agricultural mulch film
<p>Microplastics (MPs) and nanoplastics (NPs) from mulch films and other plastic materials employed in vegetable and small fruit production pose a major threat to agricultural ecosystems. For conducting controlled studies on MPs' and NPs' (MNPs') ecotoxicity to soil organisms and plants and fate and transport in soil, surrogate MNPs are<br>required that mimic MNPs that form in agricultural fields. We have developed a procedure to prepare MPs from plastic films or pellets using mechanical milling and sieving, and conversion of the resultant MPs into NPs through wet grinding, both steps of which mimic the degradation and fragmentation of plastics in nature. The major goal of this study was to determine if cryogenic exposure of two biodegradable mulch films effectively mimics the embrittlement caused by environmental weathering in terms of the dimensional, thermal, chemical, and biodegradability properties of the formed MNPs. We found differences in size, surface charge, thermal and chemical properties, and biodegradability in soil between MNPs' prepared from cryogenically treated vs. environmentally weathered films, related to the photochemical reactions occurring in the environment that were not mimicked by cryogenic treatment, such as depolymerization and cross-link formation. We also investigated the size reduction process for NPs and found that the size distribution was bimodal, with populations centered at 50 nm and 150–300 nm, and as the size reduction process progressed, the former subpopulation's proportion increased. The biodegradability of MPs in soil was greater than for NPs, a counter-intuitive trend since greater surface area exposure for NPs would increase biodegradability. The result is associated with differences in surface and chemical properties and to minor components that are readily leached out during the formation of NPs. In summary, the use of weathered plastics as feedstock would likely produce MNPs that are more realistic than cryogenically-treated unweathered films for use in experimental studies.</p>
data from "Temperate bats may alter calls to partially compensate for weather-induced changes in detection distance"
<p>Echolocation call parameters of three species of European bats collected between 2017-2019, recorded with a microphone array. This dataset is associated to the manuscript "Temperate bats may alter calls to partially compensate for weather-induced changes in detection distance". Included is the R script used for statistical analyses, as well as the R script used to model detection distances of different targets by bats across weather conditions.</p>
Illustration of a cellular automaton simulating of the invasive hogweed stand area growth based on weather data from Sofia airport
<p>Left. Grid (field) size: 100x100 cells (10000 m2). Initial configuration of occupied cells - 1 cell occupied in the center of the field.</p> <p>Right. Grid (field) size: 100x100 cells (10000 m2). Initial configuration of occupied cells - 100 cells occupied in random order.</p>
Publications from government weather and climate authorities on the X platform and replies from citizens in five Brazilian cities during a year between 2021 and 2022
<p>This dataset contains messages published and replies received by government weather and climate authorities on the X (former Twitter) social media platforms. The data comprises government weather and climate authorities for the Brazilian cities of São Paulo, Rio de Janeiro, Belo Horizonte, Porto Alegre, and Belém. Government weather and climate authorities are city hall departments or sectors responsible for informing and keeping the population updated about weather events.</p><p>Publications made by the authority and replies published by citizens to these publications are observed. This data supports the study on the interaction dynamics between the climate authority and citizens over time.</p><p><strong>Data Structure</strong></p><p>Two files are available <strong>publications.csv</strong> and <strong>replies.csv</strong>.</p><p>Each line in the publications' file (<strong>publications.csv</strong>) refers to an authority publication/tweet. For each publication, it is stored the public authority's unique Twitter identifier (<i>AUTHORITY_ID</i>), the tweet unique identifier (<i>TWEET_ID</i>), the Unix timestamp that indicates when it was published (<i>TIMESTAMP</i>), and the text of the publication (<i>TEXT</i>). </p><p>Each line in the replies file (<strong>replies.csv</strong>) is a reply from a citizen to an authority. For each reply, it is stored the authority's unique Twitter identifier (<i>AUTHORITY_ID</i>), the unique identifier of the authority's tweet being replied to (<i>TWEET_ID</i>), the replier masked unique Twitter identifier (<i>AUTHOR_ID</i>), and the reply Unix timestamp (<i>TIMESTAMP</i>) that indicates when it was published.</p><p>All data were collected through the X's application programming interface (API) provided to scientific researchers. Publications and replies were posted by users (authorities and citizens) with public visibility.</p><p><strong>Data Content</strong></p><p>The dataset covers 1-year observation period, starting on July 17, 2021, and ending on June 16, 2022. It contains a total of 10,229 publications and 5,471 replies. The observed authorities are as follows:</p><p> </p><p><strong>City Authority name X handle </strong><i><strong>AUTHORITY_ID</strong></i></p><p>São Paulo Centro de Gerenciamento de Emergências Climáticas da Prefeitura de SP <i>@cge_sp </i>268407434</p><p>Rio de Janeiro Sistema de Alerta localizado no Centro de Operações do Rio (COR) @<i>alertario </i>87487749</p><p>Belo Horizonte Defesa Civil de Belo Horizonte @<i>defesacivilbh </i>837731966</p><p>Porto Alegre Defesa Civil Porto Alegre @<i>defesacivilpoa </i>1037420896473022466</p><p>Belém Defesa Civil de Belém @<i>defesacivilbel </i>1346501728632500225</p><p> </p><p>As weather and climate authorities are government bodies, the whole content of their publications is of public interest according to Brazilian law. Thus, the text messages in their publications on social media are in the public domain and are stored in this dataset. As the data structure describes, text messages of citizens' replies are not stored. According to the terms of use of the X platform, citizen text messages cannot be publicly stored outside the X platform. Such text messages are public on that platform, and, for reproductivity, they can be recollected using the platform web page or API informing the <i>TWEET_ID</i> stored in this dataset.</p>
Data from: Prediction of three years of annual rain attenuation statistics at Ka-band in French Guiana using the Numerical Weather Prediction model WRF
<p><span>This study highlights the interest in using an Atmospheric Numerical Simulator (ANS) relying on a high-resolution weather forecast model coupled with an ElectroMagnetic Module (EMM) to compute Ka-band rain attenuation statistics in an equatorial region. An optimization of the parametrisation of the Weather Research and Forecasting meteorological model (WRF) is carried out using measurements collected from a propagation experiment carried out by CNES and ONERA near Kourou in French Guiana. Both simulated and experimental annual Complementary Cumulative Distribution Functions (CCDF) of rain attenuation are presented in this dataset.</span></p> <p>More specifically, this dataset includes the statistical distribution from both the WRF-EMM model and from the propagation experiment for the years 2017, 2018, 2020 and the whole three-year period.</p>
Asphalt profile temperatures and weather data of CyPaTs test track
<p>This dataset includes asphalt temperature measurements and weather data of CyPaTs test track located at Campus Groenenborger, University of Antwerp, Antwerp, Belgium. The recorded asphalt temperature measurements and weather data is between March 17<sup>th</sup>, 2021, and March 14<sup>th</sup>, 2022. There are five sheets in each excel file, containing asphalt temperatures at various depths (near-surface, 4cm, 7cm and 10 below asphalt surface), and corresponding weather data. The total number of data points is 371707, with each of these data points including information on weather parameters and 45 sensors embedded in different layers of the asphalt pavement.</p> <ol> <li>Date (time): time of the recorded data</li> <li>TC_x-x: label of the temperature sensor embedded in asphalt pavement, in °C</li> <li>Ta: ambient air temperature, in °C</li> <li>RH: relative humidity, in %</li> <li>FF: wind speed, in m/s</li> <li>SR: solar radiation, in W/m<sup>2</sup></li> </ol>
A Deep Learning Approach to Quantitative Precipitation Estimation Mainly Using Weather Radar Data: Taking the Plum Rain Season in Taiwan as an Example
<p>The following partial dataset is provided by the authors for the Quantitative Precipitation Estimation method presented in the paper.</p> <p>wissdom_out_Taiwan_mosaic_202105010000.bin is wind data, the number means year, data, and time.</p> <p>CAPPI_COMP_DR_202005100000.bin is radar data.</p> <p>qpepre_202005010000-202005010100_1_h.txt is rain data.</p> <p> </p> <p> </p>
Supplementary Data for "Assessing weathering, pedogenesis, and silt generation in granitoid-hosted soils from contrasting hydroclimates"
<p>Supplementary data includes original sample names and locations, raw geochemical and granulometric data, and calculated geochemical data represented as figures for the manuscript titled "<strong>Assessing weathering, pedogenesis, and silt generation in granitoid-hosted soils from contrasting hydroclimates"</strong>. All datasets are in the same excel file on separate tabs. All data were processed according to established procedures cited in the manuscript text. All software used is open access: ImageJ, adobe illustrator, and Microsoft excel.</p>
Supplementary figures for 'Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall.'
<p>Supplementary dynamics and skill plots for the paper 'Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall', submitted to QJRMS.</p>
ERA5 daily meteorological data (Puget Sound) for weather system identification
<p>This dataset includes the ERA5-based daily meteorological data over the US Puget Sound region. The data includes the following meteorological variables at 850hPa pressure level: temperature (T), relative humidity (RH), horizontal wind vector (U and V), vertical wind (W), and geopotential height (Z).</p> <p>The scripts here are used to establish the weather system classification model as in Chen et al. (submitted). More details, including the complete scripts for analysis/plotting will be updated here after the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, N. Sun, Weather Systems Connecting Modes of Climate Variability to Regional Hydroclimate Extremes. (submitted)</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN automatic weather station data set from Nafingalm
<p><strong>ABSTRACT</strong></p> <p>This data set was collected with an automatic weather station (AWS) of <a href="http://acinn.uibk.ac.at/">ACINN</a> at the Nafingalm, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022).</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Location</strong></p> <p>The AWS was located close to a small lake at the Nafinglam in the Weer Valley, Tyrol, Austria. The exact location is: 47.2151419°N / 11.712628°E / 1928 m MSL</p> <p><strong>2. Period</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the AWS data set provided here contains the period from 15 June to 12 September 2022. The time series has a measurement interval of 5 minutes and, depending on the parameter, contains both mean values and instantaneous values.</p> <p><strong>3. Instrument details</strong></p> <p>A detailed description of the AWS, its sensors, parameters, calibration and correction procedures is provided as part of the netCDF file metadata, as well as in a PDF file containing the netCDF header extracted with the Linux command ncdump -h.</p> <p><strong>4. Data file</strong></p> <p>The data are provided in a single netCDF file teamx_pc22_aws_nafingalm.nc together with a description of its content in the PDF file teamx_pc22_aws_nafingalm_ncdump_output.pdf.</p> <p><strong>5. Analysis</strong></p> <p>A first analysis of the data was performed in a Bachor thesis (Viebahn, 2023), which is available upon request from the first author of this data set.</p> <p><strong>6. Contact</strong></p> <p>Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set.</p> <p><strong>7. References</strong></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society</em>, <strong>103</strong>, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: <em>Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment</em>. Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Viebahn, T., 2023: <em>Windregime und Stabilität in einem alpinen Seitental im Sommer: Eine Standortcharakterisierung im Rahmen der TEAMx Vorkampagne 2022.</em> Bachlor thesis, University of Innsbruck, 75 pp.</p>
Weather-based ML Datasets
<p>Included are three datasets and machine learning models from Handler et al. (2020), Flora et al. (2021), and Chase et al. (2022. 2023). Handler et al. (2020) is a 1-hr HRRR-based nowcasting dataset predicting frozen road surfaces. Flora et al. (2021) is a 0-150 min severe weather dataset derived from the convection-allowing ensemble system known as the Warn-on-Forecast System. Lastly, the Chase et al. dataset is a lower resolution of the Storm EVent ImagRy (SEVIR; Veillette et al. 2020) dataset where the goal is to predict lightning.</p>
weather-data
weather-data <blockquote> <p>relies on <a href="https://github.com/sscu-budapest/datazimmer">datazimmer</a> and the <a href="https://sscu-budapest.github.io/tooling">tooling</a> of sscub</p> </blockquote>
Tornado Detection From Full-Resolution Polarimetric Weather Radar Data (SAMPLE)
<p>This dataset contains a small sample of the tornado dataset described in the talk "A Tornado Detection Algorithm using Deep Neural Networks, Full-Resolution Polarimetric Weather Radar Data, and Explainable AI" presented at the 40th Conference on Radar Meteorology on Aug 31st 2023.</p> <p>The files contained in this data represent approximately 1% of the full dataset that will be released upon final publication.</p> <p>For questions please contact</p> <p>James.Kurdzo@ll.mit.edu and mark.veillette@ll.mit.edu</p>
Artificial space weathering to mimic solar wind enhances the toxicity of lunar dust simulants in human lung cells
<p>During NASA's Apollo missions, inhalation of dust particles from lunar regolith was identified as a potential occupational hazard for astronauts. These fine particles adhered tightly to spacesuits and were unavoidably brought into the living areas of the spacecraft. Apollo astronauts reported that exposure to the dust caused intense respiratory and ocular irritation. This problem is a potential challenge for the Artemis Program, which aims to return humans to the Moon for extended stays in this decade. Since lunar dust is "weathered" by space radiation, solar wind, and the incessant bombardment of micrometeorites, we investigated whether treatment of lunar regolith simulants to mimic space weathering enhanced their toxicity. Two such simulants were employed in this research, Lunar Mare Simulant-1 (LMS-1), and Lunar Highlands Simulant-1 (LHS-1), which were added to cultures of human lung epithelial cells (A549) to simulate lung exposure to the dusts. In addition to pulverization, previously shown to increase dust toxicity sharply, the simulants were exposed to hydrogen gas at high temperature as a proxy for solar wind exposure. This treatment further increased the toxicity of both simulants, as measured by the disruption of mitochondrial function, and damage to DNA both in mitochondria and in the nucleus. By testing the effects of supplementing the cells with an antioxidant (N-acetylcysteine), we showed that a substantial component of this toxicity arises from free radicals. It remains to be determined to what extent the radicals arise from the dust itself, as opposed to their active generation by inflammatory processes in the treated cells.</p>
Weather vane
Western style, copper wind rooster. Low poly, textured for Unity HD pipeline shader. The cock and the compass are separate models. Source: Objaverse 1.0 / Sketchfab
Dust devil detection microbarometer and weather – Summer 2018 data
Open the record for dataset details and reuse information.
Data from: Impact of agricultural weathering on physicochemical properties of biodegradable plastic mulch films: comparison of two diverse climates over four successive years
Open the record for dataset details and reuse information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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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.