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Dataset results
18 results for “meteorological observations”
Fine-scale meteorological observations from walking traverses in two Phoenix Area Social Survey (PASS) 2017 neighborhoods (2019)
This dataset includes human-biometeorological observations from 2.5 km walking traverses with a mobile weather station. The traverses occurred in two 2017 Phoenix Area Social Survey neighborhoods (U18: South Phoenix/Salt River (Audubon) and W15: Camelback Mountain) on one day in June and October, at 12pm and 4pm on each day. Specifically, air temperature, humidity, wind speed, and radiant energy (infrared and solar radiation) in 3-dimensions were measured at 2-second intervals. Additionally, mean radiant temperature was calculated from the radiation measurements. The meteorological observations are spatially referenced with latitude and longitude coordinates. The paths through the neighborhoods were chosen to maximize proximity to PASS 2017 participants’ homes.
ClimHyrdoDB Archive: Meteorologic and hydrologic observations from LTER and USFS sites, 2001-2020 - orignal database format
This dataset is an archive of the ClimHydroDB database, which was actively used from early 2001 to mid 2020. The database contained contributions from 62 contributors (primarily from the LTER Network and US Forest Service) and 672 research sites. Data records total approximately 16 million (raw) or 1.6 million (aggregated) for 22 meteorologic or hydrologic variables. This archive contains the 23 core tables of the ClimHydroDB database as text tables of comma separated values, plus the database entity relationship diagram (ERD), User Guide, database table descriptions (DDL, SQL script), and a zip file of related documents and presentations. Database design: At last upgrade, the database was implemented in Microsoft SQL Server 2008 (see DDL for more information). Database tables are primarily in a key-value pair arrangement, with controlled input for many fields, and extensive cross referencing. This design allows many types of descriptors to be assigned, e.g., for the types of activities taking place at research stations, or for physical parameters to describe a research area itself. The EML metadata for tables holding controlled vocabularies are described using the string “List of …”. Cross reference tables are described in metadata as such, including the parent table names. Database history: To facilitate intersite research within the LTER network, site data managers developed a system to provide climatic summaries dynamically, called ClimDB. Later funding from the U. S. Forest Service allowed the original database to be expanded to include hydrologic variables, and the combined database was renamed ClimHydroDB in 2003. The database also harvested real-time streamflow data from USGS gauging stations, using code developed by the Georgia Coastal Ecosystem LTER. As of 2021, the ClimHydroDB content is available as data packages from individual contributing sites, each containing identically formatted text tables in the ODM 1.1 format, for integration with CUAHSI tools
Observed and WRF-simulated near-surface meteorological parameters on selected James Ross Island glaciers during heatwaves in summer 2022/23
<p>The files contain time series of near-surface meteorological conditions observed on Triangular Glacier and Davies Dome on James Ross Island, Antarctica and simulated time series for these glaciers based on the Weather Research and Forecasting (WRF) model output. Observations of 2-m air temperature, 2-m wind speed, net radiation and glacier surface height are available from 01 November 2022 to 16 January 2023 (net radiation is available only on Triangular Glacier). Simulated values of 2-m air temperature, 2-m wind speed, net radiation, sensible and latent heat fluxes are available from 08 November 2022 to 16 January 2023.</p>
Meteorological and ground observations in the Qinghai-Tibet Engineering Corridor
<p>Observation of meteorological factors was conducted at two permanent meteorological stations (Golmud and Wudaoliang) and one field meteorological station (Xidatan) with daily meteorological records. All three meteorological stations contain ground observations.</p>
Atmospheric, hydrodynamic and water quality observations from environmental-quality stations, water level sensors, acoustic Doppler velocimeters, and meteorological stations located at the Guadalquivir river estuary (2008 - 2010)
<p>The dataset included in this repository was obtained during the project entitled “Propuesta metodológica para diagnósticar las consecuencias de las actuaciones humanas en el estuario del Guadalquivir” funded by the Autoridad Portuaria de Sevilla (APS), by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía), CTM2011-22580, MedEX (CTM2008-04036-E) and PR11-RNM-7722. The data were collected in real time from 2008 until 2010 with a remote monitoring system installed by the Institute of Marine Sciences of Andalusia (ICMAN-CSIC) (Navarro et al., 2011).</p> <p> </p> <p>The environmental quality station recorded turbidity, temperature, conductivity, normalized turbidity, dissolved oxygen, oxygen, oxygen saturation, percentage of oxygen saturation, fluorescence, normalized fluorescence, and salinity every thirty minutes. Current data were measured every 15 minutes by means of acoustic current profilers. The former datasets were obtained at several depths and different locations along the Guadalquivir estuary. Water level sensors recorded the position of the free water surface every 10 minutes at several locations along the Guadalquivir estuary. Wind velocity and direction and solar radiation were measured every 10 minutes in a meteorological station at the mouth of the Guadalquivir estuary.</p> <p>Brief description of dataset.</p> <ul> <li> <p>velocities.csv (in m/s)</p> </li> <li> <p>Turbidity.csv (in Volts), temperature (in Celsius), conductivity (in Siemens/m), normalized turbidity (in FNU), dissolved oxygen (mg/L), oxygen (in Volts), fluorescence (in Volts), normalized fluorescence (in Volts), oxygen saturation (mg/L), percentage of oxygen saturation (%), salinity (in PSU).</p> </li> <li> <p>qual_Salmedina.csv, R_mean (mean radiative flux in W/m²), R_max (max radiative flux in W/m²), Rel_humidity (relative humidity in %), D_mean (wind mean direction in degrees), D_max (wind maximum direction in degrees), D_sig (standard deviation of the wind direction in degrees), V_mean (mean wind velocity in m/s), V_max (maximum wind velocity in m/s), V_sig (standard deviation of the wind velocity in m/s), P_atm_mean (mean atmospheric pressure in mbar), T_mean (mean air temperature in Celsius), T_max (maximum air temperature in Celsius), T_sig (standard deviation of the air temperature in Celsius).</p> </li> <li> <p>Sealevel.csv (in meters)</p> </li> </ul> <p>A wide description of the datasets can be found in Navarro et al (2011).</p> <p>Contact person: infogdfa@ugr.es (or mcobosb@ugr.es)</p>
Basic Meteorological Parameters measured by surface observation
<p>This dataset is an example of various weather stations in europe which collect meteorological data from the earths surface. <br>The selected station is located in Thessaloniki city centre. The dataset is a json of different parameters measured with temporal resolution of 10min. The json includes a 24h time interval.<br>Measured parameters are: <br>2m Air Temperature, Dewpoint, Windspeed, Wind direction, Wind gust, Relative Humidity, Mean Sealevel Pressure, Pressure, Global Radiation</p> <p>The data is availlable via API at Meteologix.com .</p> <p>(curl --request GET --url '<a href="https://api.kachelmannwetter.com/v02/stations/BK0078/observations?endTime=2024-10-21&startTime=2024-10-20" target="_blank" rel="noopener noreferrer">https://api.kachelmannwetter.com/v02/stations/BK0078/observations?endTime=2024-10-21&startTime=2024-10-20</a>' <span>--header </span><span>'Accept: application/json' </span><span> --header </span><span>'Authorization: Basic undefined')</span></p>
Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"
<p>This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.</p>
Observations of snowpack distribution and meteorological variables at the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017
<p>We present a climatic dataset acquired at Izas Experimental Catchment, in the Central Spanish Pyrenees, from 2011 to 2017 snow seasons. The dataset includes information on different meteorological variables acquired with an Automatic Weather Station including precipitation, air temperature, incoming and reflected short and long-wave radiation, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface) and soil temperature; all of them at 10 minute intervals. Snow depth distribution was measured during 23 field campaigns using a Terrestrial Laser Scanner (TLS), and there are also available time-lapse photographs from which can be derived daily information of different variables such as the Snow Covered Area. The experimental site is located in the southern side of the Pyrenees between 2000 and 2300 m above sea level with an extension of 55 ha. The site is a good example of sub-alpine ambient of mid-latitude mountain ranges. Thus, the dataset has a great potential for understanding environmental processes from a hydrometerological or ecological perspective in which snow dynamics play a determinant role.</p>
Selected CO2 and Meteorological Data from BErkeley Atmospheric CO2 Observation Network
<p>Selected CO<sub>2</sub> and meteorological data from BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N) for use in the characterization of the heterogeneity of greenhouse gas concentrations around the San Francisco Bay Area and the constraint of CO<sub>2 </sub>emissions from mobile sources.</p>
Early meteorological observations in Almada (Portugal) for the period 1788-1813
<p>Daily and monthly meteorological observations recorded in Almada (Portugal) during the period 1788-1813.</p>
Data products associated with "Multi-year glaciological and meteorological observations on debris-covered Kennicott Glacier, Alaska, 2016 - 2023"
<p>Data for Petersen, E., R. Hock, M. Loso, W. Guo, C. Markovsky, R. Yang, H. Han, D. Shangguan, and S. Kang, “Multi-year glaciological and meteorological observations on debris-covered Kennicott Glacier, Alaska, 2016-2023,” Geoscience Data Journal, Submitted January 2025.</p>
WeKG-MF: Weather Knowledge Graph of Météo France Meteorological Observations
<p>WeKG-MF, the <strong>Weather RDF Knowledge Graph of Météo France </strong>represents the meteorological observations made by 62 Météo-France weather stations located in different regions in metropolitan France and overseas departments, from 2012 to 2022. This work is supported by the French National Research Agency under grant ANR-18-CE23-0017 (project <a href="https://d2kab.mystrikingly.com/">D2KAB</a>).</p> <p>The raw data was obtained from <a href="https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32.">Météo France's website</a>.</p> <p>Code and description: https://github.com/Wimmics/weather-kg/</p> <p> </p>
2019-2020 AR station alpine meadow ecosystem tower-based observation spectra, GPP and meteorological data
<p> This is the dataset used in the <em>Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems</em> paper. The dataset contains canopy red and far-red SIF data, GPP data, NDVI data, photosynthetically active radiation(PAR) data, temperature(Ta) data, and vapor pressure deficit(VPD) data during the 2019 and 2020 growing seasons in the alpine meadow ecosystem at the AR site(100.4643 E, 38.0473 N, altitude 3033 m).</p>
Observational relationships between ammonia, carbon dioxide and water vapor under a wide range of meteorological and turbulent conditions: RITA-2021 campaign.
<p><strong>Data and code for ‘’Observational relationships between ammonia, carbon dioxide and water vapor under a wide range of meteorological and turbulent conditions: RITA-2021 campaign.’’</strong></p> <p>Corresponding authors: M.C. van Zanten (<a href="mailto:margreet.van.zanten@rivm.nl">margreet.van.zanten@rivm.nl</a>) and Jordi Vila (<a href="mailto:Jordi.vila@wur.nl">Jordi.vila@wur.nl</a>). RIVM and Wageningen University, Meteorology & Air Quality Section</p> <p><strong><br><br><br>Repository structure:</strong></p> <p>The zipped folder includes the following subfolders:</p> <p><strong>Data:</strong></p> <p>Here you will find a zip file that contains the measurement data obtained during the RITA-2021 campaign at the Ruisdael site at Cabauw (The Netherlands) which has been used in the preparations of the article. Not necessarily all data has been used in the final manuscript.</p> <p><strong>MATLAB:</strong></p> <p>Here you will find a zip file that contains the matlab code used for the analysis of the data available under Data.</p>
Meteorological observations and context of the 2017 Antarctic Circumpolar sailing campaign OceanoScientific
<p>This animation shows the locations of air pressure meteorological data collected by the 2017 OceanoScientific sailing campaign under the 40th Southern parallel in the Antarctic Circumpolar Current.</p> <p>Each second of the animation includes 24 frames. On each frame, the dots show locations of air pressure data within 12 hours of the date and time indicated (respectively) at the bottom and right-hand-side. The various colors differentiate between the origins of the data, as received from the Global Telecommunication System (GTS): red for OceanoScientific, green for other Voluntary Observing Ships (VOS), and blue for data buoys. The contours represent 6-hour moving averages of mean-sea-level pressure from the ERA5 reanalysis.</p>
NACP Site: Tower Meteorology, Flux Observations with Uncertainty, and Ancillary Data
This data set contains meteorological, carbon cycle flux, phenology, and ancillary data measured at 47 eddy covariance flux tower sites across North America. The data were used by North American Carbon Program (NACP) Site-Level Synthesis as model driver data and for assessing how well 24 Terrestrial Biosphere Models simulated carbon processes across vegetation types and environmental conditions.*The meteorology data include eight variables: air temperature (K), specific humidity (kg/kg), wind speed (m/s), precipitation (kg/m2/s), surface pressure (Pa), surface incident shortwave radiation (W/m2), surface incident longwave radiation (W/m2), and CO2 concentration (ppm). Gap-filled data were used by modeling teams as input model driver data.*Measured fluxes of net ecosystem exchange (NEE) and derived gross primary productivity (GPP) and respiration (R) and respective calculated uncertainty estimates are provided for each tower site at the native time resolution of the observations (30 or 60-minute) as well as the diurnal, seasonal, and annual time scales. The data were gap-filled following a standard protocol. Components of uncertainty include uncertainties resulting from turbulence, gap-filling, flux partitioning, and u* threshold determination. Flux observations and uncertainty data were used to assess how well models simulated carbon processes.*Remotely sensed NDVI, LAI, and fPAR phenology data were derived from the GIMMS version g NDVI data set for each flux tower site. Phenology data were used by some modeling teams as input model driver data.*Ancillary data and information describe tower location and physical characteristics, disturbance history, and biological and ecological attributes of the vegetation, litter, and soil. These ancillary data were used by modeling teams as input model driver data.The data files are in both ASCII text and NetCDF formats (ALMA standard). The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC).
Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) - meteorological data from Wallops ground station
TARFOX_WALLOPS_MET is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) Surface Meteorological data set Wallops ground station.The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.
Meteorology, MAIDENiso parameter files and observational data for Tungsten, Caniapiscau, and associated GPP stations
<p>This repository contains three datasets:</p> <p>1) Meteorology files needed to run the ecophysiological model of forest growth MAIDENiso at the dendrological sites of Tungsten and Caniapiscau and the eddy covariance flux stations of Uaf and Chibougamau.</p> <p>2) Parameter files needed to run the ecophysiological model of forest growth MAIDENiso at the dendrological sites of Tungsten and Caniapiscau and the eddy covariance flux stations of Uaf and Chibougamau.</p> <p>3) Observational dataset of snow, and d18O concentration at the dendrological sites, and GPP at the eddy covariance flux stations. In addition, observations of river discharge in the Caniapiscau basin (Quebec).</p>
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Allen Brain Atlas
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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.