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454 results for “weather data”
PIE LTER 15-minute meteorological data from the Marshview Farm weather station located in Newbury, MA, year 2023
Meteorological measurements for 2023 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Historical Weather, Load, Wind, and Solar Data for the Salt River Project
<p>We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:</p><ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>
Data for "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".
<p>Epidemiological and mobility data analysed in the paper "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".</p>
Weather and Air Quality data for Ireland as RDF data cube
<p>Weather, Air Pollution and Events data represented as RDF data cube. The original weather data has been downloaded from https://www.met.ie//climate/available-data/historical-data and the Air Quality data from <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm</a> and <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm</a>. The Events data refers to random events within the Republic of Ireland.</p> <p>The data has then been uplifted by running the {eeaMapping, metMapping, eventsMapping}.py scripts, which generate R2RML mappings to convert the CSV data to RDF. The mappings re-use vocabularies and ontologies that are W3C recommendations for dataset descriptions (DCAT, https://www.w3.org/TR/vocab-dcat-2/), statistical data (RDF Data Cube, https://www.w3.org/TR/vocab-data-cube/) and provenance data (PROV-O, https://www.w3.org/TR/prov-o/). The scripts use the R2RML engine from https://github.com/chrdebru/r2rml to execute the mappings which generate a data and metadata files for each of the datasets.</p> <p> </p>
Air quality, soil moisture, green roof moisture and weather data from Meetjestad
<p>Soil moisture sensors were developed by citizen science collective Meet je Stad (Measure your City). Measure your City was started in 2015 by inhabitants of the City of Amersfoort, with the goal of measuring climate related indicators. To be able to do so, collaboration was sought with the City of Amersfoort (COA), the local Water Authority and the University of Applied Sciences of Amsterdam. For the first three years the initiative focused on measuring temperature and humidity. Importantly, citizens develop their own research questions, analyze the data together with professionals and discuss potential implications. By doing so, the collective uses citizen science to spread knowledge on both technology and climate change in the most grass-roots manner possible. Within the SCOREwater project, Measure your City was asked to expand measurements with soil moisture measurements and additional temperature and humidity sensors.</p> <p>An important note here is that Measure your City develops their own sensors, has developed their own data platform and uses its own gateways purchased from the Things Network. As a result, much effort is put into constructing sensors that are reliable, low-maintenance and accurate. The latter is important for the City of Amersfoort as well, which intends to not only work on shared knowledge and understanding, but also use the data for policy making. To do so the data has to be reliable. By deploying both these sensors and purchasing company-built sensors, we can compare the data to assess how reliable the Measure your City sensors are.</p> <p>The Measure your City can also be deployed on green roofs to measure soil moisture. Whereas the soil moisture sensor measures soil moisture on two depths (10 centimeter and 40 centimeter), the sensor on a roof only measures soil moisture on one depth. In addition to soil moisture, Measure your City also measures air temperature and relative humidity. Some sensors also measure air quality (particle matter).</p>
Raw data of healthy young adults in the Weather Prediction Task
<p>Raw data of 22 healthy young adults (11 females; average age: 26.29 years; range: 21.72–30.82) in the Weather Prediction Task with 100 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials.</p> <p>Raw data of 15 healthy young adults (9 females; average age: 26.58 years; range: 20.37–28.84) in the Weather Prediction Task with 200 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials.</p> <p>Bochud-Fragnière E, Banta Lavenex P and Lavenex P (2022) What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task. Front. Psychol. 13:886339. doi: 10.3389/fpsyg.2022.886339</p>
Weather station data acquired across multiple locations in the Teakettle Experimental Forest, California, 2011-2017
These weather station records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These weather station records are for studies at the Teakettle Experimental Forest (Lat 36.967, Long -119.017, elevation 2000-2800 m, www.fs.fed.us/psw/ef/teakettle/). Weather stations were located at six sites across the Teakettle Experimental Forest landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. Three full weather stations (north slope, south slope, and valley floor) monitored precipitation, wind, insolation, temperature, relative humidity, and soil moisture. Data were recorded on a 10-minute interval using HOBO (Onset, www.onsetcomp.com) devices.
Weather station data acquired across multiple locations in the foothills of the Tehachapi mountains at Tejon Ranch, California, 2011-2017
These weather station records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These weather station records are for studies in the foothills of the Tehachapi mountains at Tejon Ranch (Lat 34.983, Long -118.716, elevation 750-930 m, www.tejonranch.com). Weather stations were located at six sites across the Tehachapi foothills. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. Three full weather stations (north slope, south slope, and valley floor) monitored precipitation, wind, insolation, temperature, relative humidity, and soil moisture. Three micro stations (west slope, east slope, and ridge) measured soil moisture at -20 cm. Data was recorded on a 10-minute interval using HOBO (Onset, www.onsetcomp.com) devices.
Weather station data acquired across multiple locations in the Tehachapi mountains at Tejon Ranch, California, 2011-2017
These weather station records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These weather station records are for studies the Tehachapi mountains at Tejon Ranch (Lat 34.967, Long -118.583, elevation 1600-1700m, www.tejonranch.com). Weather stations were located at six sites across the Tehachapi foothills. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. Three full weather stations (north slope, south slope, and valley floor) monitored precipitation, wind, insolation, temperature, relative humidity, and soil moisture. Three micro stations (west slope, east slope, and ridge) measured soil moisture at -20 cm. Data was recorded on a 10-minute interval using HOBO (Onset, www.onsetcomp.com) devices.
The Jefferson Project 2021 weather data from ten surface weather stations on Lake George, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project had ten weather monitoring stations on and around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, WX_Glenburnie, VP_TeaIsland, VP_AnthonysNose, and VP_HarrisBay. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, and N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which has undergone data correction and downsampling to an hourly frequency.
The Jefferson Project 2022 weather data from nine surface weather stations on Lake George, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project had nine weather monitoring stations on and around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, WX_Glenburnie, VP_TeaIsland, and VP_HarrisBay. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, and N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which has undergone data correction and downsampling to an hourly frequency.
Non-continuous TS/Ph7b Weather Tower Data, Everglades National Park (FCE LTER), South Florida from May 2008 to 2017
A weather tower was constructed in Taylor River at TS-PH7b to collect meteorological data necessary for the estimation of Evapotranspiration estimates within the dwarf mangrove ecotone of FCE. This data set also includes surface water and soil temperature data that was used to investigate groundwater-surface water interactions at the site.
Non-continous meteorological data from Butternut Key Weather Tower, Florida Bay, Everglades National Park (FCE LTER), April 2001 through August 2013
The following abstract is from Price, R.M., Nuttle, W.K., Cosby, B.J., and Swart, P.K. 2007. Variation and Uncertainty in Evaporation from a Subtropical Estuary: Florida Bay, Estuaries and Coasts. 30(3): 497-506: Variation and uncertainty in estimated evaporation was determined over time and between two locations in Florida Bay, a subtropical estuary. Meteorological data were collected from September 2001 to August 2002 at Rabbit Key and Butternut Key within the Bay. Evaporation was estimated using both vapor flux and energy budget methods. The results were placed into a long-term context using 33 years of temperature and rainfall data collected in south Florida. Evaporation also was estimated from this long-term data using an empirical formula relating evaporation to clear sky solar radiation and air temperature. Evaporation estimates for the 12-mo period ranged from 144 to 175 cm yr21, depending on location and method, with an average of 163 cm yr21 (6 9%). Monthly values ranged from 9.2 to 18.5 cm, with the highest value observed in May, corresponding with the maximum in measured net radiation. Uncertainty estimates derived from measurement errors in the data were as much as 10%, and were large enough to obscure differences in evaporation between the two sites. Differences among all estimates for any month indicate the overall uncertainty in monthly evaporation, and ranged from 9% to 26%. Over a 33-yr period (1970 to 2002), estimated annual evaporation from Florida Bay ranged from 148 to 181 cm yr21, with an average of 166 cm yr21. Rainfall was consistently lower in Florida Bay than evaporation, with a long-term average of 106 cm yr21. Rainfall considered alone was uncorrelated with evaporation at both monthly and annual time scales; when the seasonal variation in clear sky radiation was also taken into account both net radiation and evaporation were significantly suppressed in months with high rainfall.
Count data of air-breathing fauna from visual transect surveys including water temperature, time, sea and weather conditions in Shark Bay Marine Park, Western Australia from February 2008 to July 2014
This dataset provides information on the relative abundances of air breathing fauna (dugongs, dolphins, sea snakes, marine birds, and sea turtles) in the study area of the Eastern Gulf of Shark Bay, Western Australia. The dataset comprises transects that quantify animal abundances in three microhabitats (shallow seagrass banks, seagrass bank edges, and deep sandy channels). These microhabitats vary in their food supply as well as their potential to facilitate or inhibit detection and escape from predators, mainly the tiger shark (Galeocerdo cuvier). As a result these data have been used to examine risk-specific habitat use behaviors of these fauna, in addition to general abundance estimates.
Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 01-Jan-2003 to 31-Dec-2003
Air temperature, relative humidity, barometric pressure, precipitation, photosynthetically-available and total solar radiation, and wind speed and direction were measured using an automated Campbell Scientific Instruments climate station installed at Marsh Landing on Sapelo Island, Georgia. Observations were logged at 15 minute intervals throughout the study period. The sensors were mounted on a 10m aluminum tower, with wind sensors mounted at the top, light sensors at approximately 5m, and other sensors at 2-3m to minimize interference from the surrounding landscape. This climate station was jointly operated by the Sapelo Island National Estuarine Research Reserve, the Georgia Coastal Ecosystems LTER Project, and University of Georgia Marine Institute.
Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 01-Jan-2004 to 31-Dec-2004
Air temperature, relative humidity, barometric pressure, precipitation, photosynthetically-available and total solar radiation, and wind speed and direction were measured using an automated Campbell Scientific Instruments climate station installed at Marsh Landing on Sapelo Island, Georgia. Observations were logged at 15 minute intervals throughout the study period. The sensors were mounted on a 10m aluminum tower, with wind sensors mounted at the top, light sensors at approximately 5m, and other sensors at 2-3m to minimize interference from the surrounding landscape. This climate station was jointly operated by the Sapelo Island National Estuarine Research Reserve, the Georgia Coastal Ecosystems LTER Project, and University of Georgia Marine Institute.
Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 01-Jan-2005 to 31-Dec-2005
Air temperature, relative humidity, barometric pressure, precipitation, photosynthetically-available and total solar radiation, and wind speed and direction were measured using an automated Campbell Scientific Instruments climate station installed at Marsh Landing on Sapelo Island, Georgia. Observations were logged at 15 minute intervals throughout the study period. The sensors were mounted on a 10m aluminum tower, with wind sensors mounted at the top, light sensors at approximately 5m, and other sensors at 2-3m to minimize interference from the surrounding landscape. This climate station was jointly operated by the Sapelo Island National Estuarine Research Reserve, the Georgia Coastal Ecosystems LTER Project, and University of Georgia Marine Institute.
Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 01-Jan-2006 to 31-Dec-2006
Air temperature, relative humidity, barometric pressure, precipitation, photosynthetically-available and total solar radiation, and wind speed and direction were measured using an automated Campbell Scientific Instruments climate station installed at Marsh Landing on Sapelo Island, Georgia. Observations were logged at 15 minute intervals throughout the study period. The sensors were mounted on a 10m aluminum tower, with wind sensors mounted at the top, light sensors at approximately 5m, and other sensors at 2-3m to minimize interference from the surrounding landscape. This climate station was jointly operated by the Sapelo Island National Estuarine Research Reserve, the Georgia Coastal Ecosystems LTER Project, and University of Georgia Marine Institute.
Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 01-Jan-2007 to 31-Dec-2007
Air temperature, relative humidity, barometric pressure, precipitation, photosynthetically-available and total solar radiation, and wind speed and direction were measured using an automated Campbell Scientific Instruments climate station installed at Marsh Landing on Sapelo Island, Georgia. Observations were logged at 15 minute intervals throughout the study period. The sensors were mounted on a 10m aluminum tower, with wind sensors mounted at the top, light sensors at approximately 5m, and other sensors at 2-3m to minimize interference from the surrounding landscape. This climate station was jointly operated by the Sapelo Island National Estuarine Research Reserve, the Georgia Coastal Ecosystems LTER Project, and University of Georgia Marine Institute.
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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)
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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.