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708 results for “Temperature, air”
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>
Air temperature data recorded in a shaded area near the shore of the study site Laguna Toreadora (3,920 m asl) from August 2014 to September 2016 using a HOBO Water Temperature Pro v2 Data Logger.
<p class="MsoNormal">Air temperature data (°C) recorded in a shaded area near the shore of the study site Laguna Toreadora (<span>S 02° 46.792", W 079° 13.411"; </span>3,920 m asl) in Cajas National Park, Ecuador from August 2014 to September 2016 using a HOBO Water Temperature Pro v2 Data Logger. Air temperatures were recorded hourly over this period, with an interruption in data collection from May to July 2015.</p>
The station-based error information of monthly snow depth, precipitation and air temperature for CMIP6 models in mainland China
<p>This dataset contains the data of monthly snow depth in terms of RMSD (cm), spatial correlation (R<sub>s</sub>), temporal correlation (R<sub>t</sub>), consistency index (CI), and Hotspot score (H-score) of the 1415 weather stations (only 342 stations with longterm observations were available for R<sub>t</sub>, CI and H-score) in China used for evaluating the snow depth simulated or estimated from 31 CMIP6 models, MERRA2 reanalysis and a remote sensing snow depth dataset (Che). It also includes the data of errors and accumulated errors of monthly precipitation (mm) and air temperature (℃) from the 342 stations of all the 31 CMIP6 models, which can be used for constructing the regression models for analyzing error sources of snow depth simulations. The NA values of monthly precipitation and temperature indicate that the effects of accumulated errors were ignored for the corresponding month and station.</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>
Historic daily air temperature and precipitation series data for Wudaoliang and Tuotuohe sations (2009-2021)
<p>This dataset contains the historic daily air temperature and precipitation for Wudaoliang and Tuotuohe sations in Qinghai Province from 2009 to 2021</p>
A 1-km resolution monthly mean air temperature (Ta) dataset across the Tibetan Plateau during 2001-2015, related with the article "Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods".
<p>We present a 1-km resolution monthly mean air temperature (Ta) dataset across the Tibetan Plateau from 2001 to 2015. It ranges from 25°-45°N, 70°-105°E, covering a total area of ~7,045,000 km2. To develop this dataset, 10 machine learning algorithms were applied to 11 environmental variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data and topographic index data. The best model generated by Cubist algorithm was finally selected to calculate monthly mean Ta, and achieved an overall accuracy of RMSE= 1.00 °C and MAE= 0.73 °C. To get details of this dataset, please refer to the manuscript "Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods". This Ta dataset provides spatially continuous coverage compared with station observed data, and has much higher accuracy and spatial resolution than reanalysis datasets, making it a useful dataset for climate change and environmental studies in the Tibetan Plateau.</p> <p>Xu Y., Knudby A., Shen Y., Liu Y., Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(2): 345-354. (DOI: 10.1109/jstars.2017.2787191).</p> <p>The Ta dataset is provided in ENVI standard format. The coordinate system is WGS84 Geographic Coordinate System.</p>
Air temperature measurements using autonomous self-recording dataloggers in mountainous and snow covered areas
<p>Data and sctripts employed on submitted article on Water Resources Research (AGU Journal)</p>
Dataset: Meltwater discharge, suspended sediment and bedload flux, air temperature and precipitation at the Otemma Glacier terminus (2022 melt season)
<p>This dataset extends existing timeseries from the Otemma Glacier terminus on meltwater discharge (Müller and Miesen, 2022), suspended sediment and bedload flux (Mancini et al., 2023a), and air temperature and precipitation (Müller, 2022) into the 2022 melt season. The methodologies used for data collection were consistent with those used in previous years (see included technical report). Water and sediment flux measurements were performed at gauging station GS1 (aka Station 1) located in the main proglacial river, approx. 0.35 downstream of the glacier terminus (Müller and Miesen, 2022; Mancini et al., 2023a). Air temperature and precipitation data were collected at the "Glacier snout station" adjacent to GS1 (Müller, 2022). Data collection specifics for the 2022 melt season are provided in the <code>report2022.pdf</code> file.</p> <p><strong>DATA:</strong><br><code>river.csv</code> - water and sediment flux timeseries from GS1 with columns:<br>year: yyyy<br>frac_day: fractional day of the year<br>dt: measurement interval [mins]<br>Qw: meltwater discharge [m3/s]<br>Qss: suspended sediment flux [kg/s]<br>Qb: bedload flux [kg/s]<br>Q<em>xx</em>_lo or Q<em>xx</em>_hi: the lower and upper uncertainty bounds for Qw, Qss, Qb [kg/s]<br><em>Extended data for 2020 and 2021 melt seasons also included*</em></p> <p><code>weather.csv</code> - air temperature and precipitation timeseries spanning 18 Nov 2021 to 16 Aug 2022 from 'Glacier snout station' with columns:<br>datetime: local time (UTC+01 with DST) [yyyy-mm-dd hh:mm:ss] <br>temperature: air temperature [°C]<br>precipitation: liquid or solid precipitation [mm w.eq.]</p> <p><em>*Extended flux datasets are also provided for 2020 and 2021 given the difference in scope between Mancini et al. (2023b) and Jenkin et al. (2024, submitted for review). The datasets in Mancini et al. (2023b) have been post-processed for comparative analysis between two different gauging stations (upstream and downstream), while in this work, the most temporally complete timeseries of subglacial sediment export at the upstream gauging station was required. Therefore, the data provided here for 2020 and 2021 include: (i) a short extension of the timeseries, (ii) the removal of a suspended sediment clipping threshold, and (iii) filling of gaps in the 2020 suspended sediment timeseries with real measured data.</em></p>
All-sky daily mean ambient air temperature datasets at 1-km resolution from 2013-2022 in China
<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow:</li> </ul> <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177.</a></li> </ol> <ul> <li>This dataset is daily Tmean from 2013-2022 (<a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177</a>).</li> </ul>
All-sky daily min ambient air temperature datasets at 1-km resolution from 2013-2022 in China
<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow:</li> </ul> <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177</a></li> </ol> <ul> <li>This dataset is daily Tmin from 2013-2022 (<a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>).</li> </ul>
All-sky daily max ambient air temperature datasets at 1-km resolution from 2013-2022 in China
<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow: <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177.</a></li> </ol> </li> <li>This dataset is daily Tmax from 2013-2022 (<a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>).</li> </ul>
Figure 2. Mean monthly temperatures per year for the period April 1998 in Effect of temperature on the flight activity of culicids in Buenos Aires City, Argentina
Figure 2. Mean monthly temperatures per year for the period April 1998 to March 2001, and minimum, maximum and mean temperatures for the period 1861–2003. Buenos Aires City.
Figure 1 in Effect of temperature on the flight activity of culicids in Buenos Aires City, Argentina
Figure 1. Collection sites of mosquitoes using CDC light traps. Buenos Aires City, April 1998 to March 2001.
Figure 4 in Effect of temperature on the flight activity of culicids in Buenos Aires City, Argentina
Figure 4. Median, quartiles and thermal amplitude of capture events according to the mean temperature of the day at which they were recorded. Species are ranked from higher to lower thermal amplitudes (indicated in brackets).
Figure 3 in Effect of temperature on the flight activity of culicids in Buenos Aires City, Argentina
Figure 3. Proportion of captures by season and species. Buenos Aires City, April 1998 to March 2001.
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>
PLEIAData:consumption, HVAC (Heating, Ventilation & Air Conditioning), temperature, weather and motion sensor data for smart buildings applications
<p>This dataset presents detailed building operation data from the three blocks (A, B and C) of the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX. The aim of PHOENIX is to improve buildings efficiency, and therefore we included information of:<br> (i) consumption data, aggregated by block in kWh; (ii) HVAC (Heating, Ventilation and Air Conditioning) data with several features, such as state (ON=1, OFF=0), operation mode (None=0, Heating=1, Cooling=2), setpoint and device type; (iii) indoor temperature per room; (iv) weather data, including temperature, humidity, radiation, dew point, wind direction and precipitation; (v) carbon dioxide and presence data for few rooms; (vi) relationships between HVAC, temperature, carbon dioxide and presence sensors identifiers with their respective rooms and blocks. Weather data was acquired from the IMIDA (Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario).</p>
Continuous data of air temperature, relative humidity, and air pressure collected at Kyoto University in January 2022
<p>I present the continuous data of air temperature, relative humidity, and air pressure collected at the Kyoto-A gravity measurement room (latitude: 35.02938 N, longitude: 135.78347 E, elevation: 60.82 m), Graduate School of Science, Kyoto University in January 2022. The values of air temperature, relative humidity, and air pressure were obtained every 1 second by a BME280 sensor (Bosch Sensortec GmbH) on a RT-USB-THP module (RT Corporation). The data were time-stamped and recorded using the Tera Term software on a Windows PC, which was synchronized in time with the NTP server of NICT (ntp.nict.jp).</p> <p>photo.zip contains the photographs of the Kyoto-A gravity measurement room taken in December 2022. The RT-USB-THP module and the Windows PC were located on the basement rock of the Kyoto-A absolute gravity point.</p> <p>data.zip contains the continuous data of air temperature, relative humidity, and air pressure collected at the Kyoto-A gravity measurement room. Each file of 2201??.txt stores the data obtained from 11:55 JST of the corresponding day to 11:54 JST of the next day, according to the operating time of the Tera Term software. The columns in 2201??.txt are as follows from left to right: year, month, day, hour, minute, second, air temperature [degC], air pressure [hPa], and relative humidity [%]. Note that 220101.txt was not recorded due to the failure of the Windows PC on January 1, 2022.</p> <p>figure.zip contains the graphs of air temperature, relative humidity, and air pressure drawn using the GMT4 software. Each graph of 2201??.png shows the time variation in air temperature, relative humidity, and air pressure from 0:00 JST to 24:00 JST of the corresponding day. tonga.png shows the air pressure variation from 12:00 JST on January 15 to 11:54 JST on January 16, and its original data is available as 220115.txt in data.zip. The pressure change of about 2 hPa was observed around 20:40 JST on January 15, associated with the propagation of the Lamb wave generated by the massive eruption of the Hunga Tonga-Hunga Ha’apai volcano.</p>
10-minutes Air Temperature and Relative Humidity Datasets from city of Novi Sad - NSUNET system
<p>On the territory of Novi Sad and its surrounding the urban meteorological network with 27 stations (Ta and RH sensors) was made. The operation time of the network was from July 2014 to February 2018. Stations locations were chosen based on the local climate zone classification system (Stewart and Oke, 2012) that was applied on urban area of Novi Sad. There were 25 stations in the city of Novi Sad and two rural stations (located north and northeast from the city outskirts). Seven LCZ types were defined on the territory of Novi Sad and these 25 stations were located within these built-up types. Rural stations (two) were located in LCZ’s of low plants and dense trees. The measurement frequency of all stations was 10 minutes. This means that the Ta and RH sensors measured a new value every minute, and after ten measurements we got a new average value based on the previous ten measurements. Datasets are 'raw data' i.e. have missing data and outliers. Therefore, before any analysis, the detailed QC steps must be applied!</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>
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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.