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46 results for “surface air temperature”
Evaluation of the influence of rain on air surface temperature measurements
<h2>Description</h2> <p>The dataset is constituted by three .csv files, which contain the measurements performed in an experiment aiming to evaluate the influence of rain on temperature readings. Two devices under tests (DUTs), one naturally ventilated and one artificially ventilated, are compared with a reference system. A .csv file is produced for DUT1, DUT2 and the reference system. Here below the content of each file is briefly described:</p> <ul> <li>Dataset_reference: accurate air temperature measurements obtained using the reference system, which is not affected by rain. The system is constituted by four aspirated thermometers (called Meteo1, Meteo2, Meteo 3, Meteo 4) manufactured at the Danish Technology Institute. The column "PT500" contains instead the rain temperature measurements. The readings are produced using a Fluke Super-DAQ (1586A). </li> <li>Dataset_DUT1: measurements of the naturally ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> <li>Dataset_DUT2: measurements of the artificially ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> </ul>
A long-term (1981-2020) 1-km daily extreme and mean near surface air temperature product over Yellow River Basin of China
<p>The dataset includes the semless 1-km daily extreme and mean near surface air temperature products over Yellow River Basin of China. The fourth version is from 1 January 2011 to 31 Decmber 2020.</p>
Equilibrium climate sensitivity experiments using EC-Earth3-LR model — Surface Air Temperature data
<p>Three experiments was conducted using a EC-Earth model with the EC-Earth3-LR configuration (REF), which couples atmosphere, land, ocean and sea-ice components. First, we performed a pre-industrial (PI) control simulation (E280) using pre-industrial forcing, holding atmospheric constituents constant at 1850 levels (e.g., CO<sub>2</sub> concentration at 280 ppm). This simulation was initialized by a pre-run steady restart file (from a 500-year pre-industrial control simulation) and ran for 2000 years. We also conducted two sensitivity experiments (E400 and E560) by adjusting the CO<sub>2</sub> concentration to 400 ppm and 560 ppm, respectively, at the start year of the E280 experiment, and continued for over 3000 years (3069 years for E400, and 3013 years for E560). For our statistical analysis, we only considered the integration periods after the spin-up, using the last 2000-year outputs from the three simulations.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2023).</p> <p>Cao, N., Zhang, Q., Wang, Z., Power, K.E., & Liu, C. (2023). The non-negligible impact of internal multi-centennial climate variability on estimating equilibrium climate change. Submitted to <em>Geophysical Research Letters</em>.</p> <p> </p> <p><strong>Model configuration</strong><br> Time periods: 2000-year time slice for all three experiments<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for Surface Air Temperature data.</p>
Global LAke Surface water Temperature (GLAST): Global lakes are warming slower than surface air temperature due to accelerated evaporation
<p>This repository houses a dataset, known as the Global LAke Surface water Temperature (GLAST), which provides both temporal and spatial details at high resolution for 92,245 lakes worldwide during the period of 1981-2099, with 36% of them situated in Arctic regions. The dataset was established based on four decades (1982-2020) of Landsat satellite images and a physical model (FLake). For a comprehensive overview of the dataset's production methodology, please refer to the paper titled 'Global lakes are warming slower than surface air temperature due to accelerated evaporation' (Tong et al., 2023, Nature Water). Detailed information regarding each data file can be found in the 'readme.docx' file.</p>
Global record-breaking recurrence rates indicates more widespread and intense surface air temperature and precipitation extremes
Open the record for dataset details and reuse information.
The bulk parameterizations of turbulent air-sea fluxes in NEMO4: the origin of Sea Surface Temperature differences in a global model study
<p>This repository contains the code and the data used to produce the results of "The bulk parameterizations of turbulent air-sea fluxes in NEMO4: the origin of Sea Surface Temperature differences in a global model study" a discussion paper by G. Bonino, D. Iovino, L. Brodeau, S. Masina submitted to Geoscientific Model Development.</p> <p>- DATA.tar contains the 5 days model outputs to produce the figures in the manuscript.</p> <p>- CODE.tar contains the code and the namelists to run the experiments. The namelists and the modified code for run each experiments are available in the subfolder CODE/cfgs/. </p>
Data used in "Marine heatwaves make more contribution to changing air–water exchange of semi-volatile organic compounds than mean sea surface temperature raising"
<p>Data used in "Marine heatwaves make more contribution to changing air–water exchange of semi-volatile organic compounds than mean sea surface temperature raising"</p>
The warm-season ground surface temperature - surface air temperature over China mainland
<p>This is a dataset for describing warm-season ground surface temperature - surface air temperature over China mainland.</p>
Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer
<p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> GLACIER METEOROLOGICAL DATA<br> SWISS ALPS -2022<br>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br>Data gathered and structured by Thomas Shaw (WSL, Switzerland (until Oct 2022)).</p><p>On and off-glacier meteorological data were gathered and analysed as part of a Marie-Curie project 'TEMPEST' (tempestglacier.com).<br>The dataset consists of hourly low-cost AWS (Davis Vantage Pro2) and simple temperature ('T-')logger (Onset TidBitv2) sensor records on three glaciers in the Swiss Alps (Canton Valais).</p><p>The glaciers are:<br>Haut Glacier d'Arolla (45.967°N, 7.526°E)<br>Glacier d'Otemma (45.956°N, 7.454°E)<br>Glacier du Corbassière (45.975°N, 7.303°E)</p><p>Data are provided in individual Excel files per glacier that contain all hourly data for the sub-period of comparison (11 August-18 September, 2022).<br>Data are quality controlled and checked for obvious errors. Any uncertain values are set to NaN.<br>Air temperature data at 'T-Logger' stations were corrected for heating errors using the comparison of measurements in artificially (AWS) and naturally ventilated (T-Logger) radiation shields on Arolla and Corbassiere glaciers.<br>A multiple linear regression model was applied to estimate these differences at all T-Loggers on all glaciers as a function of incoming shortwave radiation (MeteoSwiss station-derived) and wind speed (measured at AWS).</p><p>Each Excel file contains a 'META' tab for simple metadata related to station locations (latitude 'LAT' (°), longitude 'LON' (°), elevation 'ELE' (m a.s.l.) and flowpath length 'FPL' (m)) and a 'DATA' tab for the hourly data. <br>Suffixes to the station names in each column provide the variable measured at that site:<br>'TA' - 2m air temperature (°C)<br>'TA_Hi' - Maximum air temperature for timestep (°C)<br>'TA_Lo' - Minimum air temperature for timestep (°C)<br>'RH' - 2m relative humiditiy (%)<br>'FF' - Wind speed (m s^-1)<br>'FF_Hi' - Maximum wind speed for timestep (m s^-1)<br>'FF_Lo' - Minimum wind speed for timestep (m s^-1)<br>'DIR' - Wind direction (°)<br>'DEW' - Dewpoint temperature (°C)<br>'PRESS' - Air pressure (mbar)<br>'CHILL' - Calculated wind chill temperature (°C)<br>'Heat_idx' - Calculated heat index (°C)<br>'THSW' - A calculated index that uses humidity and temperature like for the Heat Index, but also includes the heating effects of sunshine and the cooling effects of wind (like Wind Chill) to calculate an apparent temperature of what it "feels" like out in the shade</p><p>Wind speeds and direction measured at off-glacier sites 'OG' are for the lower off-glacier station ('OG_Low'). </p><p>-------------------------</p><p> </p><p>This work was funded by the EU Horizon 2020 Marie Skłodowska-Curie Actions Grant 101026058.<br> </p>
Detectable urbanization effect in observed surface air temperature data series in Pyongyang region, DPR Korea-Supporting Information-data
<p>This is the calculated dataset as supplemental information of a paper entitled "Detectable urbanization effect in observed surface air temperature data series in Pyongyang region, DPR Korea", which will be likely published in Geophysical Research Letters.</p> <p> </p> <p> </p> <p> </p>
Air temperature of surface observation data
<p>Air temperature of surface observation data</p>
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.
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.
Monthly Near-Surface Air Temperature Averages
Global surface temperatures in 2010 tied 2005 as the warmest on record. The International Satellite Cloud Climatology Project (ISCCP) was established in 1982 as part of the World Climate Research Programme (WCRP) to collect and analyze the global distribution of clouds, their properties, and their diurnal, seasonal, and interannual variations. The LAS provides data for Monthly Near-Surface Air Temperature Averages from 1994 to 2008.
MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100
<p>Please be advised that our dataset previously referred to as <strong>“MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100”</strong> has been updated and is now available under the title <strong>“MoCHAT: Global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature projections from 1950 to 2100”</strong>.</p> <p>The latest dataset can be accessed via the following URL: [<a href="https://data.tpdc.ac.cn/zh-hans/data/40d649d6-d99e-45df-9814-c0115a109396">https://data.tpdc.ac.cn/zh-hans/data/40d649d6-d99e-45df-9814-c0115a109396</a>]</p> <p>If you have any questions when using the MoHAT dataset, please feel free to contact Miss Xuwen Lei via <a href="mailto:leixuewen22@mails.ucas.ac.cn">leixuewen22@mails.ucas.ac.cn</a>, Dr. Qingyan Meng via <a href="mailto:mengqy@radi.ac.cn">mengqy@radi.ac.cn</a>, or Mr Qikang Zhao via <a href="mailto:yc27963@umac.mo">yc27963@umac.mo</a>. </p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multisource data (2001-2002)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:<span>T<sub>ave</sub>, </span><span>R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%</span><span>; T<sub>max</sub>, </span><span>R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%</span><span>; T<sub>min</sub>, </span><span>R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%</span><span>).</span></p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2013-2014)
<div> <p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p> </div>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2017-2018)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2011-2012)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2009-2010)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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