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708 results for “Temperature, air”
Air temperature and solar radiation data for C1 data logger (DP211), 1993 - 2006.
Climatological data were collected from a Niwot Ridge climate station (C1) throughout the year using an Omnidata DP211 datapod. This instrument has a sample interval of 5 minutes and records averages of those 5-minute readings every 2 hours. Thus, daily totals represent totals of 288 values. Parameters measured were temperature (averages) and solar radiation (totals).
Air temperature and solar radiation data for D1 data logger (DP211), 1990 - 2006.
Climatological data were collected from an upper Niwot Ridge climate station (D1) throughout the year using an Omnidata DP211 datapod. This instrument has a sample interval of 5 minutes and records averages of those 5-minute readings every 2 hours. Thus, daily totals represent totals of 288 values. Parameters measured were temperature (averages) and solar radiation (totals).
15-minute measurements of water temperature, dissolved carbon dioxide, and air carbon dioxide concentration in 10 New Hampshire and Massachusetts streams, 2015-2020.
Water temperature, dissolved carbon dioxide, and air carbon dioxide were measured at 10 streams and rivers in New Hampshire and northeast Massachusetts during snow-free periods (roughly April to November) at 15-minute intervals, from 2015 to 2020. Five sites were part of the New Hampshire EPSCoR High Intensity Aquatic Network, three sites are part of the Plum Island Ecosystems LTER, and two sites are part of long-term monitoring projects of the Oyster River watershed near Durham, NH.
MFS-M-00001 Air temperature at +2 m, raised bog-ridge, Thermochron (DS1921G-F5)
<p>Air temperature at 2m measured in a raised bog ecosystem (ridge) by Thermochron logger, 2009-present (with several breaks) as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00002 Air temperature at 2m measured in a raised bog-ridge, DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger), 2018-2019, 30 min frequency, N60.89494 E68.66999, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
ChinaHighTEMmin: Daily Seamless 1 km Minimum Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>minimum air temperature</strong> (TEMmin) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.98 and a root-mean-square error (RMSE) of 1.53 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMmin dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
ChinaHighTEMavg: Daily Seamless 1 km Average Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>average air temperature</strong> (TEMavg) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.99 and a root-mean-square error (RMSE) of 1.18 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMavg dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
NAHosMIP - monthly surface air temperature and precipitation v3
<p>This dataset is for data generated from the North Atlantic Hosing Model Intercomparison Project (NAHosMIP), which is documented in <a href="https://gmd.copernicus.org/articles/16/1975/2023/gmd-16-1975-2023.html" target="_blank" rel="noopener">Jackson et al, 2023</a>. </p> <p>Data used in that paper (including AMOC streamfunctions) can be found <a href="https://zenodo.org/records/7643437">here</a> </p> <p><strong>Experiments</strong></p> <ul> <li>picon - preindustrial control which was run as part of CMIP6 (<a href="https://gmd.copernicus.org/articles/9/1937/2016/">Eyring et al., 2016</a>),</li> <li>u03-hos - constant uniform hosing of 0.3 Sv. </li> <li>u03-r50 - experiment with no hosing initialised 50 years into u03-hos</li> <li>u03-r100 - experiment with no hosing initialised 100 years into u03-hos</li> </ul> <p><strong>Models</strong></p> <p>Eight CMIP6 models took part: CanESM5, CESM2, EC-Earth3, HadGEM3-GC3-1LL, HadGEM3-GC3-1MM, IPSL-CM6A-LR, MPI-ESM1-2-HR, MPI-ESM1-2-LR </p> <p><strong>Variables</strong></p> <ul> <li>tas - surface air temperature (monthly resolution)</li> <li>pr - precipitation (monthly resolution)</li> <li>evspsbl - surface evaporation (including sublimation and transpiration)</li> <li>psl - sea level pressure</li> </ul> <p><strong>File name convention</strong></p> <p>We use the CMIP file naming convention, so for example:</p> <p>tas_Amon_HadGEM3-GC31-LL_u03-r100_r1i1p1f1_gn_215001-215912.nc</p> <pre><code>$variable_$timeresolution_$model_$experiment_$version_$grid_$date.nc</code></pre> <p>Files with the same variable and model are combined in a tar file:</p> <pre><code>$variable_$timeresolution_$model_$version_$grid.tar</code><br><br><strong>AMOC timeseries<br></strong><br>These are included in the file M26.tar. This is the maximum streamfunction at 26.5N<strong><br><br>Additional precip and wind files<br><br></strong>Also included are files used by <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003959">Ben-Yami et al, 2024</a> who examined the impacts of an AMOC <br>collapse on monsoons. The files contain monthly mean (mmean) or annual mean (ymean) of <br>precipitation (prcp) and surface winds (ua and va) for the experiments <br>u03-r50 (for HadGEM3-GC31-MM, CanESM5, CESM2) or u03-r100 (for IPSL-CM6A-LR)<br><br>Files are named<br><br></pre> <pre><code>BY24_$model_$exp.tar</code></pre> <pre><br><br></pre>
Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850-2100)
<p>A long-term (1850-2100) monthly air temperature (tas) product with a spatial resolution of 0.5 degree. This is a merged product from 31 CMIP6 models using the Deep-learning model which reduce bias, spatial downscaling and data merge at the same time,. To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain one historical data (1850-2014) , four future scenarios data during 2015-2100 (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and four future scenarios data in Australia . The dataset is stored in NetCDF format, containing the variable tas, representing air temperature, produced in centigrade (℃) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.75E to 179.75E, the latitude from -89.75N to 89.75N. </p>
Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities
<p>These datasets were generated to assess linear and nonlinear Granger causalities in the submitted manuscript, Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities by Bhatti et al. submitted to AGU-GRL. Nonlinear GC here is achieved with the Kernel Granger causality by Marinazzo et al. (2008). The data was used to develop theoretical experiments that help validate the strengths and limitations of both the linear Granger causality and the Kernel Granger causality before applying to real world datasets</p>
Supporting data and software for: Low-temperature open-air synthesis of PVP-coated NaYF4:Yb,Er,Mn upconversion nanoparticles with strong red emission
<p>Upconversion nanoparticles (UCNPs) have unique photonic properties that make them ideally suited for many applications. They are excited by low-energy near-infrared photons and emit at higher energy (typically visible) wavebands. However, synthesis of UCNPs requires either high pressure reaction chambers or inert atmospheres. Combined with the requirements for high-temperatures (200 to 400 °C) and long reaction times (e.g. up to 24 hours), these place barriers to entry for UCNP research, in terms of both financial barriers and knowledge/"know how". These constraints may also limit the scale of UCNP production for end-user applications.</p> <p>We adapted and further developed a method for producing UCNPs with simple laboratory equipment, i.e. a hot-plate and beakers. No pressure vessel or inert atmosphere is required. The UCNPs produced have a<span> polyvinylpyrrolidone (PVP) polymer coating, with strong red emission due to Mn<sup>2+</sup> co-doping within the UCNP crystal lattice. It was found that UCNPs of composition NaYF<sub>4</sub>:Yb,Er,Mn (Yb = 20 mol %, Er = 2 mol%, Mn = 35 mol%) maximised the red emission whilst also minimising the diameter of the UCNPs to </span> 36 ± 15 nm. These combination of optical and physical properties should make these UCNPs ideal for further development and exploitation, particularly for biological applications where red emission can penetrate over a centimetre of tissue.</p> <p>This dataset and software accompanies the manuscript <em>'Low-temperature open-air synthesis of PVP-coated NaYF<sub>4:</sub>Yb,Er,Mn upconversion nanoparticles with strong red emission</em>', which was published in Royal Society Open Science on 19th January 2022. https://doi.org/10.1098/rsos.211508</p>
Dataset and R code: Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany
<p>Dataset and R script to replicate results in the manuscript "Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany", currently under review.</p>
On-Glacier Air Temperatures for Miage Debris-Covered Glacier, 2014
<p>Miage_Glacier_Air_Temperature_Data_2014.xlsx<br> %------------------------------------------%<br> Data Generated on 13th May 2022</p> <p>Data Curator: Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland)</p> <p>Data Provider(s): Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) thomas.shaw@wsl.ch<br> Prof. Benjamin Brock (Northumbria University, Newcastle, UK) benjamin.brock@northumbria.ac.uk</p> <p>Data period: 1st July - 27th September, 2014</p> <p><br> Details:<br> Hourly data are generated for air temperature stations ('T-Loggers') distributed across the debris-covered Miage Glacier, Italy (45.8129°N, 6.8458°E).<br> Air temperatures (°C) were measured using Tinytag thermistors (accuracy +/- 0.2-0.35°C) housed in naturally ventilated Campbell MET20 / MET21 radiation shields.</p> <p>Off-Glacier air temperatures (measured as above) are provided for comparison with on-glacier air temperatures. </p> <p>For additional details can be found in the article: <br> Shaw, T. E., Brock, B. W., Fyffe, C. L., Pellicciotti, F., Rutter, N., & Diotri, F. (2016).<br> Air temperature distribution and energy-balance modelling of a debris-covered glacier. Journal of Glaciology, 62(231), 1–14. <br> https://doi.org/10.1017//jog.2016.31</p> <p>Please cite the above article for any usage of the dataset.</p> <p>Data are shared and compiled as part of a wider project to estimate on-glacier air temperatures from off-glacier data<br> For more details on the 'TEMPEST' project, visit: https://tempestglacier.com/</p>
Air temperature and humidity effects on the performance of conservation detection dogs
<p>This is the dataset which underlies the submitted manuscript entitled " <strong>Air temperature and humidity effects on the performance of conservation detection dogs</strong>".</p> <p>The uploaded data contain an xlsx file,which includes the data, as well as a txt readme file, which explains the header information in the data file.</p>
Physical connection between the tropical Indian Ocean tripole and western Tibetan Plateau surface air temperature during boreal summer
<p><em>These experiments are used to study atmospheric circulation responses to SST forcing related to Indian Ocean tripole mode, including the precipitation, omega, geopotential height, zonal and meridional winds.</em></p>
Figure 2 in Climate variability of extreme air temperature events in the Eastern Black Sea
Figure 2. Changes in the mean monthly air temperature anomalies at the surface (relative to seasonal variability) smoothed by annual (orange) and eight-year (violet) gliding averaging in the eastern part of the Black Sea (42° - 45°N, 37° - 42°E). Their linear trend is shown by black line and the accumulated sum of anomalies after removing the linear trend – by green line. Average values of anomalies for warm and cold half-year are marked by red and blue dots respectively.
Figure 1 in Climate variability of extreme air temperature events in the Eastern Black Sea
Figure 1. Changes in mean monthly air temperature at the surface (red) and their linear trend (blue) in the eastern part of the Black Sea (42° - 45°N, 37° - 42°E).
Figure 4 in Climate variability of extreme air temperature events in the Eastern Black Sea
Figure 4. The annual changes in the mean amplitude (upper part), the number (middle part) and the mean duration (bottom part) of extreme events with positive (red lines) and negative (blue lines) air temperature anomalies in the eastern part of the Black Sea (42° - 45°N, 37° - 42°E), exceeding two standard deviations, and their linear trends.
Figure 3 in Climate variability of extreme air temperature events in the Eastern Black Sea
Figure 3. The annual changes in the mean amplitude (upper part), the number (middle part) and the mean duration (bottom part) of extreme events with positive (red lines) and negative (blue lines) air temperature anomalies in the eastern part of the Black Sea (42° - 45°N, 37° - 42°E), exceeding one standard deviation, and their linear trends.
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International Brain Laboratory public data
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OpenNeuro
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