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1,121 results for “weathering”
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Sonic Weather
<p>The research explores the potential of sonification as an innovative and alternative approach to the traditional visual representations that have long dominated our understanding of weather patterns. The study investigates the mutual correlation between weather conditions and the sonic identity of places by establishing a connection between weather patterns and the acoustic signature of a designated location. Combining the environmental soundscape with the weather elements illustrates the interactive correlation between changes in weather conditions and the sonic responses of other components of the environment.</p> <p>The screencast briefly explains the Sonic Weather project implementation method and the data mapping approach.</p> <p>The weather patterns audio recordings represent different weather conditions. Please note that some values were triggered deliberately to record different weather conditions that might not occur during the recording.</p> <p>- Weather pattern 01 represents a warm sunny pattern.<br>- Weather pattern 02 represents a windy pattern.<br>- Weather pattern 03 represents a stormy pattern.<br>- Weather pattern 04 represents a rainy pattern.<br>- Weather pattern 05 represents a heavy rain pattern.</p>
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API - ECMWF IFS04 within Europe area
The proportion of REEs in the chemical reactive phase of river sediments serves as an index for silicate weathering intensity
Open the record for dataset details and reuse information.
Ephemeris data for: "Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast"
<p><strong>Background: </strong>Astrometeorology is an ancient science, that addresses the relationship between planet position and weather events. Several Indian studies proved that astrometeorology could be a complementary method to improve numerical weather forecast accuracy. Since 2011, Tamil Nadu Agricultural University is involved in astrometeorological research and devised a novel concept "Planet Activeness Chart". The principle is that "planets' influence on a location's weather varies throughout the day and may be negative, inactive, active, highly active and rule depending on their angle to that location". Most existing astrometeorological studies used planetary position to predict the occurrence of weather events (yes/no) but failed to capture the intensity of such events. The "Planet Activeness Concept" could address this limitation and enhance forecast usability.</p> <p><strong>Methods</strong>: A study was carried out from 2018 to 2021 with six years of data (2011-16) to verify the "planet activeness" on hourly rainfall and wind speed events in Tamil Nadu. The frequency of planet activeness for a weather event was calculated by dividing the number of times a planet was in the selected activeness during a specific event category by the total number of events.</p> <p><strong>Results:</strong> The results indicated that the negative state of the Sun, and the active status of Saturn, Uranus, Venus, and Moon were positively associated with rainfall intensity. The windy planets Mercury and Neptune being in active states, the Sun and Saturn in rule states, Venus and Uranus in negative states, and Jupiter at a highly active state all had a significant influence on the increased wind speed.</p> <p><strong>Conclusion</strong>: Applying the planet activeness concept with azimuth could enhance the accuracy and usability of Astrometeorological forecasts. This study establishes a mathematical relationship between planet activeness and weather as a first step to understanding the science behind this relationship. It is suggested to study different combinations of planets' activeness during a weather event for more insights.</p>
Two-Decadal Variability of Lacustrine Groundwater Discharge: Coupled Controls from Weather and Hydrologic Changes
<p>This is the dataset of radon concentration in lake water and groundwater.</p>
Figure 3 in Structural changes in needle epicuticular waxes of Balkan Abies species in relation to natural weathering
Figure 3. SEM micrographs of A. cephalonica epicuticular waxes. A-F. Adaxial surface of needles with noticeable granules; G-I. Abaxial surface of needles with two stomatal bends; J-L. Abaxial stomatal rows; M-O. Stomatal pores occluded either by wax tubules or by wax crusts; P-R. Agglomeration, thickening and fusion of tubules with needle aging. Bars: A-I = 500 μm; J-L = 50 μm; D-O = 10 μm; P-R = 1 μm.
Figure 1 in Structural changes in needle epicuticular waxes of Balkan Abies species in relation to natural weathering
Figure 1. SEM micrographs of A. alba epicuticular waxes. A-F. Adaxial surface of needles with noticeable granules; G-I. Abaxial surface of needles with two stomatal bends; J-L. Abaxial stomatal rows; M-O. Stomatal pores occluded either by wax tubules or by wax crusts; P-R. Agglomeration, thickening and fusion of tubules with needle aging. Bars: A-I = 500 μm; J-L = 50 μm; D-O = 10 μm; P-R = 1 μm.
Figure 1 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 1. Maps of the White Sea. Dashed lines show boundaries of the sea and their internal parts (Lebedev et al., 2011).
EngRAD: Multivariate Weather Measurements Across England
<h1>EngRAD Dataset</h1> <p>The EngRAD dataset contains measurements of 5 different weather variables collected at 487 grid points in England from 2018 to 2020.</p> <h2>Dataset Overview</h2> <p>Data has been provided by <a href="https://open-meteo.com/" target="_blank" rel="noopener">Open-Meteo</a> and licensed under <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Attribution 4.0 International (CC BY 4.0)</a>. The numerical weather prediction model used to generate the data is ECMWF IFS, which has a spatial resolution of 9 km. The grid points are located in correspondence with cities. Each point is associated with location information such as geographic coordinates, elevation, closest city, and the county to which it belongs. The physical variables collected are:</p> <ol> <li>Air temperature at 2 meters above ground (°C)</li> <li>Relative humidity at 2 meters above ground (%)</li> <li>Summation of total precipitation (rain, showers, snow) during the preceding hour (mm)</li> <li>Total cloud cover (%)</li> <li>Global horizontal irradiance (W/m²)</li> </ol> <p>These variables are typically of interest in applications related to solar radiation, such as solar power production.</p> <h3>Channels:</h3> <p>The <code>data.h5</code> file contains two tables, accessible by the following keys:</p> <ul> <li> <p><strong><code>data</code></strong>: Contains the measurements for each point across different weather variables.</p> <ul> <li><strong><code>temperature_2m</code></strong>: Air temperature at 2 meters above ground (°C). Instantaneous measurement.</li> <li><strong><code>relative_humidity_2m</code></strong>: Relative humidity at 2 meters above ground (%). Instantaneous measurement.</li> <li><strong><code>precipitation</code></strong>: Total precipitation (rain, showers, snow) sum of the preceding hour (mm). Preceding hour sum.</li> <li><strong><code>cloud_cover</code></strong>: Total cloud cover as an area fraction (%). Instantaneous measurement.</li> <li><strong><code>shortwave_radiation</code></strong>: Global horizontal irradiation (GHI) (W/m²). Preceding hour mean.</li> </ul> </li> </ul> <p> </p> <ul> <li> <p><strong><code>metadata</code></strong>: Contains the following detailed information for each point:</p> <ul> <li><code><strong>city</strong></code>: The name of the city where the measurement point is located.</li> <li><code><strong>county</strong></code>: The county in which the city is situated.</li> <li><code><strong>admin_name</strong></code>: The administrative name associated with the city or region.</li> <li><code><strong>lat</strong></code>: The latitude coordinate of the measurement point.</li> <li><code><strong>lon</strong></code>: The longitude coordinate of the measurement point.</li> <li><code><strong>elevation</strong></code>: The elevation (in meters) above sea level at the measurement point.</li> <li><code><strong>population</strong></code>: The population of the city where the measurement point is located.</li> </ul> </li> </ul> <h3>Dataset size</h3> <ul> <li><strong>Time steps</strong>: 26304</li> <li><strong>Points</strong>: 487</li> <li><strong>Channels</strong>: 5</li> <li><strong>Sampling rate</strong>: 1 hour</li> <li><strong>Missing values</strong>: 0.00%</li> </ul> <h3>Credits</h3> <p>This dataset has been introduced in the paper:</p> <p>Ivan Marisca, Cesare Alippi, and Filippo Maria Bianchi. "Graph-based forecasting with missing data through spatiotemporal downsampling." <em>Proceedings of the 41st International Conference on Machine Learning</em>, PMLR 235:34846-34865, 2024.</p> <p>Please consider citing the paper if you use the dataset for your research.</p> <pre><code>@inproceedings{marisca2024graph,</code><br><code> title = {Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling},</code><br><code> author = {Marisca, Ivan and Alippi, Cesare and Bianchi, Filippo Maria},</code><br><code> booktitle = {Proceedings of the 41st International Conference on Machine Learning},</code><br><code> pages = {34846--34865},</code><br><code> year = {2024},</code><br><code> volume = {235},</code><br><code> series = {Proceedings of Machine Learning Research},</code><br><code> publisher = {PMLR}</code><br><code>}</code></pre>
Four great space weather events observed by SOHO and WIND
<p>The dataset is a supplemental material for the paper: Extreme Space Weather Events of the Past 30 Years: Analysis and Implications for Vigil’s L5 Observations.</p> <p>It consists of data from 4 most extreme space weather events that produced great geomagnetic storms on date:</p> <ul> <li>2001-03-31</li> <li>2003-10-30</li> <li>2003-11-20</li> <li>2004-11-08</li> </ul> <p>The dataset spans a period of 10 days before and 10 days after the date when the most extreme Dst index value was reached. Images and in-situ measurements are from the following space-based instruments in the highest available cadency.</p> <ul> <li>SOHO / EIT - images in 195 filter (https://umbra.nascom.nasa.gov/eit/)</li> <li>SOHO / MDI - magnetograph images (http://soi.stanford.edu/)</li> <li>SOHO / LASCO C2 and C3 - coronagraphs images (https://lasco-www.nrl.navy.mil/)</li> <li>SOHO / CELIAS - proton speed and density measurements (http://www2.physik.uni-kiel.de/SOHO-CELIAS/)</li> <li>WIND / MFI - magnetic field measurements (https://wind.nasa.gov/mfi/)</li> </ul> <p>The data were obtained by the pipeline presented at https://github.com/space-lab-sk/vigil-like-data. They provide a limited vizualization of how the ESA Vigil mission, which is currently under development, would have seen the most extreme events that were ever captured. </p>
Dataset of "Gravity waves – Toward connecting space weather and atmosphere-ionosphere coupling"
<p>The first file is the Ap file, describing the variation of the geomagnetic index used in the model simulations. The data files starting with sw are the model simulation files for the different model experiments. Each file is on a 2x15 (lat x lon) horizontal grid and on 66 pressure levels. There are 24 longitude and 91 latitudes. The files describe the simulations from 7th to 9th August 2018 in two hour time steps. The output variables in each file are zonal wind, geopotential height, and zonal gravity wave drag, in this given order. The files are formatted in the standard binary format for GraDS. </p>
MIDAS GPS TEC data for "On the Annual Asymmetry of High-Latitude Sporadic-F" (Space Weather)
<p>GPS-based TEC data related to high-latitude ionospheric variability in 2014</p>
Energy consumption and weather data collected continuously during a period of 29 months at Challenger building (France).
<p>The dataset consists of energy consumption and weather data collected continuously during a period of 29 months at Challenger building (France).</p> <p>Energy consumption data at 10 minutes intervals include:</p> <ul> <li>Heating and cooling electrical consumption (indoor and outdoor comfort units) for the 8 zones in the South Triangle (kWh)</li> <li>Lighting consumption for the 8 zones in the South Triangle (kWh)</li> <li>Plug load consumption for the 8 zones in the South Triangle (kWh)</li> <li>Sanitary consumption for the 8 zones in the South Triangle (kWh)</li> <li>Blinds consumption for the 8 zones in the South Triangle (kWh)</li> <li>Air handling unit consumption for the South Triangle (kWh)</li> <li>Total electrical consumption for the South Triangle (kWh)</li> <li>Thermal energy consumption for VRF (Variable refrigerant flow) units of the South Triangle (kWh)</li> </ul> <p>Weather data include:</p> <ul> <li>Daily degree days (°C)</li> <li>Hourly humidity (%)</li> <li>Daily rain precipitation (mm)</li> <li>Hourly direct and global radiation (J/cm2)</li> <li>Daily sunshine hours (hrs)</li> <li>Hourly temperature (°C)</li> </ul> <p>The data set contains<br> - Timeseries as CSV files<br> - Timeseries metadata (description, unit, type,...) as JSON file</p> <p>The data have been made available from the BMS database, thanks to the BEMServer open-source platform - <a href="http://www.bemserver.org">www.bemserver.org</a>, developed in the HIT2GAP project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement n. 680708 - <a href="http://www.hit2gap.eu">www.hit2gap.eu</a>.</p>
Weather Data from Env. Education Center at Lancer Park 2019-03-04 to 2019-04-17
<p># General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> LP_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## Metadata File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
Weather Data from Env. Education Center at Lancer Park 2018-08-27 t0 2018-10-15
<p># General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> LP_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## Metadata File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.