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708 results for “Temperature, air”
Prospective Trial of the Effect of Preoperative Forced-air Warming on Perioperative Body Temperature Following Neuraxial Anesthesia in Total Hip Arthroplasty Patients
ClinicalTrials.gov study NCT01626690. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of Vital HEAT (vH2) Temperature Management System to Upper-body Forced-air Warming
ClinicalTrials.gov study NCT00815191. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Core Body Temperature When Using Forced Air Warming or an Active Blanket to Prevent Perioperative Hypothermia
ClinicalTrials.gov study NCT02079311. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Predicted percentage dissatisfied with vertical air temperature difference
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Air temperatures overpredict changes to stream fish assemblages with climate warming compared to water temperatures
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AIRS/Aqua L1B AMSU (A1/A2) geolocated and calibrated brightness temperatures V005 (AIRABRAD) at GES DISC
The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AMSU-A instrument is co-aligned with AIRS so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. AMSU-A is primarily a temperature sounder that provides atmospheric information in the presence of clouds, which can be used to correct the AIRS infrared measurements for the effects of clouds. This is possible because non-precipitating clouds are for the most part transparent to microwave radiation, in contrast to visible and infrared radiation which are strongly scattered and absorbed by clouds. AMSU-A1 has 13 channels from 50 - 90 GHz and AMSU-A2 has 2 channels from 23 - 32 GHz. The AIRABRAD_005 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.
BOREAS RSS-17 Stem, Soil, and Air Temperature Data
The BOREAS RSS-17 team collected several data sets in support of its research in monitoring and analyzing environmental and phenological states using radar data. This data set consists of tree bole and soil temperature measurements from various BOREAS flux tower sites. Temperatures were measured with thermistors implanted in the hydroconductive tissue of the trunks of several trees at each site and at various depths in the soil. Data were stored on a data logger at intervals of either 1 or 2 hours. The majority of the data were acquired between early 1994 and early 1995. The primary product of this data set is the diurnal stem temperature measurements acquired for selected trees at five BOREAS tower sites.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Atmospheric Temperature from Aqua AIRS, V2 (SNDRAQIL3SSDFCNSAT)
This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
In-situ Air Temperature and Relative Humidity in Greenbelt, MD, 2013-2015
This data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale. Sensors were mounted on posts at 2 m above surface and placed on 7 different surface types around the centre: asphalt parking lot, bright surface roof, grass field, forest, and stormwater mitigation features (bio-retention pond and rain garden). Data were also recorded in an office setting and a garage, both pre- and post-deployment, for calibration purposes. This dataset could be used to validate satellite-based study or could be used as a stand-alone study of the impact of surface type on heating in a campus setting.
BOREAS TE-06 1994 Soil and Air Temperatures in the NSA
The BOREAS TE-06 team collected several data sets to examine the influence of vegetation, climate, and their interactions on the major carbon fluxes for boreal forest species. This data set contains measurements of the air temperature at a single height and soil temperature at several depths in the NSA from 25-May to 8-Oct-1994. Chromel-Constantan thermocouple wires run by a miniprogrammable data logger (Model 21X, Campbell Scientific, Inc., Logan, UT) provided direct measurements of temperature.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Atmospheric Temperature from SNPP CrIMSS and Aqua AIRS, V2 (SNDR13IML3SSDFCNSAT)
This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016
This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites.
AMMR Air and Brightness Temperature Data, Wakasa Bay, Version 1
The Wakasa Bay Field Campaign was conducted to validate rainfall algorithms developed for the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E).
Delta-X: Turbidity, Water and Air Pressure, Temperature, MRD, Louisiana, 2021, V4
This dataset provides turbidity measurements with co-located water and air pressure and temperature measurements in portions of the Mississippi River Delta, coastal Louisiana, US. Data were collected at five sites in Atchafalaya River Basin in Spring (2021-03-24 to 2021-04-02) and eight sites in the Atchafalaya River and Terrebonne Basins in Fall 2021 (2021-08-16 to 2021-08-27). In order to sample various hydrodynamic conditions, sensors were deployed at island edges, island interior, and other portions of wetlands. Sensors recorded turbidity, absolute pressure, and temperature. The Delta-X mission is a 5-year NASA Earth Venture Suborbital-3 mission to study the Mississippi River Delta in the United States, which is growing and sinking in different areas. River deltas and their wetlands are drowning as a result of sea level rise and reduced sediment inputs. The Delta-X mission will determine which parts will survive and continue to grow, and which parts will be lost. Delta-X begins with airborne and in situ data acquisition and carries through data analysis, model integration, and validation to predict the extent and spatial patterns of future deltaic land loss or gain. The data are provided in comma-separated values (CSV) files.
MFS-M-00107 Air temperature measured at 2 m, coniferous taiga forest, Thermochron (DS1921G-F5)
<p>Air temperature at 2m measured in a coniferous mixed taiga forest by Thermochron (DS1921G-F5) (temperature logger), 2015-continues, 30, 60 or 180 min frequency depending on season, there one gap in data, N61.07960 E69.45287, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00005 Air temperature measured at 2m, coniferous mixed forest ecosystem, DS18B20 (APIK)
<p>Air temperature at 2m measured in a coniferous mixed forest ecosystem by DS18B20 (temperature logger), 2012-continues, 30 and 60 min frequency, several gaps in data, N60.89458 E68.70920, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
Daily Maximum and Minimum Air Temperature for Africa
<p>These datasets are daily maximum and minimum temperatures (degree Celsius). It is based on a 2-meter air temperature - daily maximum and a daily minimum of the MERRA-2 Model (M2SDNXSLV v5.12.4). We used the Global Historical Climatology Network Daily (GHCNd) stations to extract corresponding temperature values from MERRA-2 datasets. The primary objective of these datasets is to provide continuous and seamless time series for hydrologic modeling and climate change adaptation applications in Africa</p>
A daily in-situ dataset of air temperature, ground temperature, and snow depth dataset for Northern Hemisphere
<p><strong>A daily in-situ dataset of air temperature, ground temperature, and snow depth dataset for Northern Hemisphere</strong></p>
In-situ relative humidity and air temperature urban microclimate data
<p>Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and there has been a growth in the number of studies measuring intra-urban temperature variability. Recognizing that the physiological effects of heat depend on humidity as well as temperature, some of these measurement campaigns have included measurements of relative humidity alongside temperature. Reported analyses, however, have not reported whether spatial structure in humidity, independent from temperature, contributes significantly to intra-urban heat variability. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the USA to examine this issue. It is shown that although there are spatial variations in relative humidity there are only very weak spatial variations in the absolute humidity within these cities. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidex, and the spatial variability of the heat metrics is dominated by temperature variability. A practical consequence of this is that a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.</p>
Indoor Air Quality, Temperature and Cognitive Performance
ClinicalTrials.gov study NCT06380582. IPD Sharing: YES. Countries: 1. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.