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Hull Springs Weather Data from 2021-06-12 to 2021-07-16
<pre># General Metadata for Hull Springs Farm Atmospheric Sampling Station ## Files Specific metadata for each deployment can be found as text files with the file format of: HSF_weather_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. ## File Created * 2019-10-27 by KF ## File Modified ## Description These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory. All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific at each site are: * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N) </pre>
Hull Springs Weather Data from 2021-07-16 to 2021-08-14
<p>General Metadata for Hull Springs Farm 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> <pre><code>HSF_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Kinetics and mechanisms of acid-pH weathering of pyroxenes
<p>Weathering of primary silicate minerals under acidic conditions occurs in contexts as varied as acid mine drainage, volcanic environments, soils, stone monuments subjected to acid rain or Geological Carbon Storage (GCS). Considering the abundance of pyroxenes on the Earth crust, knowledge of their weathering kinetics and mechanisms may help to optimize carbonate yield in GCS. Here we report experimental results from the reaction of the clinopyroxenes augite and diopside in acidic solutions. Dissolution at far-from-equilibrium conditions results in the formation of etch pits where crack initiate and propagate by stress corrosion and pressure exerted by swelling of an amorphous, gel-like Si-rich phase, which precipitates despite the undersaturation of the bulk solution and whose formation is highly controlled by the heterogeneity of the mineral surface and the local transport mechanism. These precipitates are commonly localized within deep etch pits and cracks, characterized by a low fluid renewal where high Si-concentrations can be reached locally, so that supersaturation with respect to amorphous silica can occur. Cracks and silica precipitates are most abundant in the case of augite weathered in flow-through experiments. This is related to its faster reaction rate compared to diopside, most likely due to its higher iron content. Finally, in the case of diopside an amorphous magnesium silicate hydrate (M-S-H) precursor forms, which represents an indirect evidence of the high pH conditions prevailing at the diopside-solution interface during dissolution.</p>
Weather data from Environmental Education Center at Lancer Park from 2021-06-05 to 2021-07-19
<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> <pre><code>LP_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>Metadata File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>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 <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Weather data from Environmental Education Center at Lancer Park from 2021-07-19 to 2021-08-17
<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> <pre><code>LP_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>Metadata File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>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 <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Datasets with weather forecasts (Temperature, Wind Direction, Humidity, Pressure, Wind Speed, GHI)
<p>Datasets with weather forecasts for HLU7 (Temperature, Wind Direction, Humidity, Pressure, Wind Speed, GHI) of the CROSSBOW project.</p>
Rye microgrid historical weather forecasts and stochastic scenarios
<p>This datasets connects historical weather forecasts from the Norwegian Meteorological Institute (met.no) and historical observations from Rye microgrid (https://doi.org/10.5281/zenodo.4448894).</p> <p>Each csv file represents a historical weather forecast for approximately 60 hours ahead. Each csv-file also contains the corresponding observations in the same time interval. Finally, the files also contain load, wind generation and solar PV generation predicitons.</p> <p>The predictions are generated using gradient boosting. The predictions ending with "_ls" are based on least square. The predictions ending with "_quantile_i" represent a quantile prediction. For example, "wind_quantile_2" means that there is a 20% probability the wind will be less than this value.</p> <p>The gradient boosting predictor has been trained to predict the wind power, solar power and load using the explanatory variables below:</p> <p>Solar PV: Cloud area fraction, initial production, clear sky production and forecast look-ahead time</p> <p>Wind power: wind speed, wind direction, wind power converted from wind speed forecast, initial production and forecast look-ahead time</p> <p>Load: hour of day, month of year</p>
Data and scripts associated with "Rock weathering controls soil carbon storage potential at a continental scale"
<p>The zipped folder contains the following files:</p> <p>CONUS_mineral_grid.csv [gridded soil mineralogy maps, with coordinates representing cell centers]</p> <p>CONUS_grid.csv [blank grid, used for reproducing the analysis]</p> <p>NCSS_datamerge_071321.R [core script used for running the analyses presented in the associated publication]</p> <p>apply_depthwtavg.R [wrapper function for depth weighted averaging]</p> <p>attach_ENV.R [function to attach climate data]</p> <p>bootstrap_functions.R [functions for spatial bootstrap statistics]</p> <p>depthweightavg.R [function for calculating depth weighted averages]</p> <p>get_MWBM.R [function for compiling climate data into averages]</p> <p>get_SLP.R [function for reading and pre-processing the NASGLP dataset</p> <p>SLP_IDW.R [inverse distance weighting function interpolating the NASGLP data at query points]</p> <p>weather_calcs.R [secondary calculations partitioning Al and Fe; weathering rate estimation]</p>
Hull Springs Weather Data from 2021-08-14 to 2021-08-23
<p>General Metadata for Hull Springs Farm 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> <pre><code>HSF_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (JSON format)
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly JSON files containing geospatial vector polylines describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>Each probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. Each JSON file contains one top-level object composed of name/value pairs with the names issuanceDate, validDate, ColdFronts, WarmFronts, OccludedFronts, and StationaryFronts. The name/value pairs for createDate and validDate are always present. The other name/value pairs are only present if there is corresponding data. The values for issuanceDate and validDate are UTC timestamp strings.</p> <p>The ColdFronts, WarmFronts, StationaryFronts, and OccludedFronts names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front of the given type. Each object is composed of five name/value pairs with the names lats, lons, cols, rows, and confidence. The value for the name confidence is a number that is the average of the values of the probability map cells intersected by the front polyline. The values associated with the names lats, lons, cols, and rows are arrays. These arrays represent the vertices of a polyline describing the location of a frontal boundary in both geospatial and grid cell coordinates.</p>
NCAS CDAO Sky-Camera Time-Lapse Video for 2008-10-05 11:00 UTC (showing Cumulus humilis clouds, which are also known as fair weather cumulus).
<p>A time-lapse video showing Cumulus humilis clouds, which are also known as fair weather cumulus. This video has been created from images taken by the Sky-Camera at the National Centre for Atmospheric Science (NCAS) Capel Dewi Atmospheric Observatory (CDAO) near Aberystwyth, UK - formerly known as the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility. The images are freely available under a UK Open Government Licence from http://tinyurl.com/nerc-mstrf-sky-camera/ . For more details visit http://mst.nerc.ac.uk .</p>
Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events
<p>Datasets generated during and/or analyzed during the Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events.</p>
Dataset for "Three-dimensional modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm", Marshalko et al. (2023), Space Weather
<p>Results of 3-D modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm in 2003 (29-31 October).</p> <p>Electric_field_YYYYMMDDHHMMSS_YYYYMMDDHHMMSS.h5 files (in hdf5 format) contain horizontal electric field components Ex and Ey (in mV/km) corresponding to 6 h of data and latitude and longitude grids corresponding to Ex and Ey arrays. Ex and Ey are 2160x567x543 arrays (temporal resolution is 10 s, thus, 2160 time steps). Latitude and Longitude are 567x543 arrays. All values are in single-precision floating-point format. Electric field values were obtained with the use of the conductivity-based inducing source following Marshalko et al. (2023).</p> <p>Files Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat contain the ground electric field time series (in mV/km) modeled during the Halloween geomagnetic storm in 2003 (29-31 October) at IMAGE magnetometers' locations, Mäntsälä Finnish natural gas pipeline GIC recording point (MAN), and Point X located 0.5 degrees north of MAN. The files are in plain-text (column-based) format. Electric field values in Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat were obtained with the use of the conductivity-based inducing source, MT intersite impedance method, and Spherical Elementary Current Systems (SECS) based approach, correspondingly, following Marshalko et al. (2023).</p>
Model output from the Bern3D model of 600 kyr weathering step-change experiments
<p>Model output data from the Bern3D model that were used for the manuscript "Carbon cycle responses to changes in weathering and the long-term fat of stable carbon isotopes".</p> <p>Information on the different data files is included in the README.</p>
Sex, body size, and winter weather explain migration strategies in a partial migrant population of American Kestrels (Falco sparverius)
<p>Given increasing evidence that climate change affects the annual cycles of birds, it is important to understand the mechanisms underlying individual migration strategies and population-level patterns in partial migrants. In this study, we found that thermoregulation (body size and winter temperatures) was a key driver of American Kestrel (<em>Falco</em> <em>sparverius</em>) migration decisions. The annual proportion of migrants in the population, however, was not explained by winter weather and may be the result of differential survival. We measured stable hydrogen isotope values (δD) of talon tissues collected from 501 breeding and overwintering birds to distinguish migrant from resident kestrels in a partially migratory population of American Kestrels in southwestern Idaho in 2013–2021. We then evaluated drivers of migration decisions by assessing potential correlates of migration strategies, whether individuals switched migration strategies between years, and whether the proportion of migrants in the population changed over time or was correlated with winter weather. Male kestrels were 1.6 times more likely to migrate than females, and in colder than average winters, smaller birds of both sexes were more likely to migrate than larger birds. Only 27% of 26 recaptured individuals showed evidence of switching their migration strategies on an annual basis. There was no temporal trend in the proportion of migrants in the population, but proportions varied between years. Interestingly, there was no association between winter minimum temperature anomalies and annual migrant proportions in the population, suggesting that differential over-winter survival, or other stochastic processes, may play an important role in population composition. As winters continue to warm, fewer kestrels may migrate and more may remain resident on breeding grounds. However, it is unclear how changes in migration strategies might affect population-level patterns and resilience to climate change.</p>
meteosona-weather-data
<p>This dataset contains meteorological data from a set of stations in the Osona region and also a set of webcam images. In total, there is data from 54 automatic stations owned by meteorology enthusiasts. The data has been retrieved from <strong><a href="http://meteosona.com">MeteOsona</a></strong>.</p> <p>The web scraper project can be found on <a href="https://github.com/guillempla/web-scraper">GitHub</a>.</p> <p>Meteorological data shows basic station information, sample collection data, data on precipitation, air temperature, relative humidity, wind speed and direction, atmospheric pressure and solar radiation. There is data that has been collected at a specific moment, and others that correspond to the monthly summaries of each station.</p> <p>Note that not all stations have the same number of sensors. Therefore, some stations may have fewer data than others. It should also be borne in mind that the fact that the stations belong to amateurs may imply that the data has not been properly reviewed.</p> <p>The dataset is published under the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.</p> <p>The proprietary of this data is <strong>MeteOsona, </strong>and therefore the data is subjected to the <a href="https://www.meteosona.com/avis_legal/">legal notes</a> published on the web.</p> <p> </p>
Historical weather data
<p>Historical weather data for model training.</p> <p>Weather data from 4 locations taken from the local stations of the project pilots with the following attributes: atmospheric pressure, dew point, illuminance, precipitation, pressure tendency, relative humidity, temperature, wind direction, wind speed</p>
Carouge pilot: open data for the weather
<p>Datasets collected in the NAIADES Carouge pilot and containing all the data related to the weather.</p>
Analysis of the Ground Level Enhancement GLE 60 on April 15, 2001, and its Space Weather Effects: Comparison with Dosimetric Measurements - Data
<p>Computed data that was used within the "Analysis of the Ground Level Enhancement GLE 60 on April 15, 2001, and its Space Weather Effects: Comparison with Dosimetric Measurements" paper. Computations of cones were done by OTSO using TSY89 + IGRF13 magnetic field parameters. Contains the atmospheric yield functions used for radiation computation as well as the global radiation map at 35kft for GLE60. Data is provided in .csv format.</p>
Morphed extreme weather data for Vantaa and Sodankylä under RCP climate change scenarios by 2030, 2050 and 2080
<p>Morphed extreme weather data for 2 Finnish locations: Vantaa and Sodankylä. Created for "Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios" publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>). Used climate change scenarios are RPC2.6, RCP4.5 and RCP8.5. Data is created for 2030, 2050 and 2080 and includes 6 extreme weather scenarios: </p> <ul> <li>W1 - Winter with high heating demand</li> <li>W2 - Winter with low heating demand</li> <li>W3 - Winter with the coldest individual day by average temperature</li> <li>S1 - Summer with the lowest cooling demand</li> <li>S2 - Summer with the highest heating demand</li> <li>S3 - Summer with the warmest individual day by average temperature</li> </ul> <p>Selected years and the procedure for their selection are described in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>.</p> <p>Original weather data is downloaded for the selected years from Finnish Meteorological Institute's Open data repository: https://www.ilmatieteenlaitos.fi/havaintojen-lataus under CC BY 4.0 licence.</p> <p>Future change in climate is based on Finnish Meteorological Institute's data used in creating Test Reference Year weather files (<a href="https://www.ilmatieteenlaitos.fi/energialaskenta-try2020">https://www.ilmatieteenlaitos.fi/energialaskenta-try2020</a>) for which the climate change data is presented by Ruosteenoja et al. (2016).</p> <p>The data is statistically downscaled through a method called morphing created by Belcher et al. (2005) with some parts using methods from Räisänen & Räty (2013) and Jylhä et al, (2015). Morphing was computationally conducted through created software <a href="https://github.com/japulk/Weather-Morphing-Tool">https://github.com/japulk/Weather-Morphing-Tool</a> For additional information please refer to <a href="https://doi.org/10.1016/j.energy.2024.131636">original article</a> or contact the authors.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.