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1,121 results for “weather”
The Jefferson Project 2017 weather data from seven 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 and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had five weather monitoring stations around the lake collecting data on precipitation, temperature, wind, and air quality. These stations are 'WX-CedarLane', 'WX-DFWI', 'WX-GullRock', 'WX-MossyPoint' and 'WX-WhaleRock'. Weather data from two vertical profiler sites, 'VP-AnthonysNose' and 'VP-TeaIsland', are also included in this dataset. The stations have a sensor payload that include some combination of the following sensors: HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, LiCor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and down sampling to an hourly frequency.
The Jefferson Project 2018 weather data from eight 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 2018, The Jefferson Project had six weather monitoring stations around the lake collecting data on precipitation, temperature, wind, and air quality. These stations are WX_CedarLane, WX_DFWI, WX_PilotKnob, WX_GullRock, WX_MossyPoint, and WX_WhaleRock. Weather data from two vertical profiler sites, VP_AnthonysNose and VP_TeaIsland, are also included in this dataset. 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, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and downsampling to an hourly frequency.
The Jefferson Project 2019 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 2019, The Jefferson Project had seven weather monitoring stations around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidiity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, and WX_Glenburnie. Weather data from three vertical profiler sites (VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland) are also included in this dataset. 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, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and downsampling to an hourly frequency.
The Jefferson Project 2020 weather data from seven 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 2020, The Jefferson Project had seven weather monitoring stations around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidiity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, and WX_Glenburnie. 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.
Weather station data acquired across multiple locations on the San Joaquin Experimental Range, 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 San Joaquin Experimental Range (Lat 37.083, Long -119.716, elevation 210-520 m, www.fs.fed.us/psw/ef/san_joaquin/). Weather stations were located at six sites across the 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. 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.
Palmyra Atoll Weather Tables and Monthly Rainfall 2008-2017
Weather data collected from Palmyra Atoll by US Fish and Wildlife Service and the Nature Conservancy for the period of 2010-2016, with rainfall data available from 2008-2017.
Influence of weather forecast resolution on the circulation of Lake George, NY.
This dataset contains outputs of numerical modeling for Lake George, New York, hydrodynamics. These numerical simulations were generated to assess the impact of increasing the resolution of weather forecasts on the lake’s thermal state. This research focused on June 2017, when an increase of biological activity was associated to the deepening of the thermocline in the south of the lake. Increasing the resolution of the weather forecast led to a more accurate representation of the water temperature in the lake, including the deepening of the thermocline. The dataset was used in support of “The influence of weather forecast resolution on the circulation of Lake George, NY”.
Hourly weather data from the Arctic LTER Moist Acidic Tussock Experimental plots from 2011 to present, Toolik Filed Station, North Slope, Alaska.
Hourly weather data from the LTER Moist Acidic Tussock Experimental plots. The station was installed in 1990 in block 2 of the Toolik LTER experimental moist acidic tussock plots. The plots are located on a hillside near Toolik Lake (68 38' N, 149 36'W). Global solar radiation, photosynthetic active radiation, unfrozen precipitation, air temperature, relative humidity, wind speed, and wind direction are measured at 3 meters. Additional sensors in greenhouses and shade houses plots measure air temperature, relative humidity and photosynthetic active radiation during the growing season. The sensors are read every minute and averaged or totaled every hour.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (No Long-term Weather Trend) Control Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations without a long term weather trend.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (With a Long-term Trend) on Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations with a long-term weather trend.
Evaporation Estimates for Long Key C-MAN Weather Station, Florida Bay (FCE) from July 1998 to May 2004
This file contains data from the National Data Buoy Office Coastal-Meteorological Automated Network (C-MAN) weather station near Long Key, in Florida Bay (LONF1, 24deg 50min 36sec N, 80deg 51min 42sec W). The time period is 1600 EST February 6, 2004 through 0700 EST May 28, 2004. The record is a combination of NDBO data (wind speed, air pressure, air temperature water temperature) and Harbor Branch data (relative humidity) needed to calculate evaporative water loss. The file is one of several from this location. Together, they can be used to estimate of hourly, daily, monthly, seasonal and annual evaporation rates. Calculations include hourly water loss and cumulative water loss. Times are Eastern Standard.
Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 01-Jan-2015 to 31-Dec-2015
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-2016 to 31-Dec-2016
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.
Long-term Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 03-Jan-2003 to 31-Dec-2019
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. Annual data sets from 2003 to 2019 were then synthesized and standardized to create a long-term, monotonic time series data set that will be updated annually. 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-2017 to 31-Dec-2017
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.
Precipitation data from a standard can rain gauge at the LTER weather station, Jornada Basin, southern New Mexico, USA, 1992-ongoing
This data package contains precipitation measurements collected from a "dipstick" rain gauge (NOAA standard can type) at the LTER Weather Station in the Jornada Basin, southern New Mexico, USA. The primary purpose of this data set is to validate the LTER Weather Station tipping bucket rain gauge data. The dipstick rain gauge (DSRG) data is measured at least weekly during scheduled maintenance trips to the LTER Weather Station to maintain the evaporation pan water levels. During the summer months this may be twice a week. Additionally, DSRG data is collected after any rain event that requires the collection of the Wetfall/Dryfall precipitation buckets which are located about 10 meters from the DSRG. This is usually any amount greater than 0.02 inches. DSRG data is also collected after very small events when personnel are in the vicinity. Rain gauge records at this gauge began in 1992 and the study is ongoing.
Locally verified daily temperature and precipitation data from a NOAA weather station at USDA Jornada Experimental Range headquarters, southern New Mexico USA, 1914-2006
This data package contains locally verified daily meteorological observations from a NOAA National Weather Service station located at the USDA Jornada Experimental Range headquarters in southern New Mexico, USA. Daily data has been collected there by USDA staff since 1914 for minimum and maximum air temperature and daily accumulated precipitation using standard U.S. climatological service instrumentation and procedures. The included data were verified and transcribed directly from the original paper data sheets and have undergone quality control and assurance procedures different than those in place at NOAA. These data therefore differ from those directly downloadable from NOAA servers. Local verification and transcription of observations from the data sheets ceased in 2006 and data are now directly entered to the NOAA system. Therefore, this dataset is complete and will no longer be added to. All observations from this weather station have also undergone NOAA QA/QC procedures and those data are available by accessing the Jornada Experimental Range, NM US GHCN station through the National Climatic Data Center portal (https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00294426/detail - daily and monthly data are available).
Locally verified monthly summary temperature and precipitation data from a NOAA weather station at USDA Jornada Experimental Range headquarters, southern New Mexico USA, 1914-1998
This data package contains locally verified monthly meteorological observations from a NOAA National Weather Service station located at the USDA Jornada Experimental Range headquarters in southern New Mexico, USA. Monthly summary data (based on daily observations) has been collected there by USDA staff since 1914 for minimum and maximum air temperature and daily accumulated precipitation using standard U.S. climatological service instrumentation and procedures. The included data were verified and transcribed directly from the original paper data sheets and have undergone quality control and assurance procedures different than those in place at NOAA. These data therefore differ from those directly downloadable from NOAA servers. Local verification and transcription of observations from the data sheets ceased in 1998 and data are now directly entered to the NOAA system. Therefore, this dataset is complete and will no longer be added to. All observations from this weather station have also undergone NOAA QA/QC procedures and those data are available by accessing the Jornada Experimental Range, NM US GHCN station through the National Climatic Data Center portal https://www.ncdc.noaa.gov/cdo-web/datasets/GSOM/stations/GHCND:USC00294426/detail - daily and monthly data are available).
Daily weather averages for Palmer Station, Antarctica (1989-2024)
Daily averages of weather data collected at Palmer Station, Antarctica were generated from averages of manual observations (1989-2003) and automatic weather station measurements (2003-present). Weather data was originally acquired by manual observers 4 times per day. An automated weather data system was installed in Nov 2001. Measurements began shifting from manual to automated observations in June 2003 until the manual observations were ended on December 12, 2003, except for snow stake depth, sea ice coverage, and sea water temperature which remain as manual observations. Data are collected, compiled, and distributed by the U.S. Antarctic Program’s polar contractor. Data is archived by the Antarctic Meteorological Research Center (AMRC) at the University of Wisconsin-Madison, and made available online at https://amrdcdata.ssec.wisc.edu/group/palmer-station/ This dataset contains a compiled version of the daily averaged data provided by the archive.
PIE LTER, meteorological data, 15 minute intervals, from the Marshview Farm weather station located in Newbury, MA, year 2021
Meteorological measurements for 2021 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.
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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)
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.