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698 results for “mean temperature”
Annual Maps of Mean Winter Temperature for Eastern North America 1951-2009
We developed annual raster maps depicting spatiotemporal variation in mean winter temperature for the purposes of modeling the spread of the hemlock woolly adelgid. The maps are based on the PRISM and WorldClim datasets as described in methods.
Bonanza Creek LTER: Hourly Air Temperature Measurements (mean, min, max) at Various Heights from 1995 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
This dataset contains the hourly output from Temperature sensors for the Cariou-Poker Creeks Research Watershed (CPCRW). Any temperature data collected in this area is reported here. This includes Level 3 weather stations as well as smaller scale and temporal studies. This data can be sorted and viewed by site, year, hour, height of measurement, mean, min, and max value. Updates of each site are different since some are still on going while other had only a 2-3 year life cycle. This information can be found in the site descriptions and in the metadata.
Mean radiant temperature along a common route for people experiencing homelessness in downtown Phoenix, Arizona (USA) on August 20, 2024
This tabular dataset contains mean radiant temperature (Tmrt) measurements collected using MaRTy, a mobile biometeorological, along a route frequently traveled by people experiencing homelessness in downtown Phoenix, Arizona (USA). It includes Tmrt, air temperature (Tair), relative humidity (RH), wind speed, and wind direction at pedestrian height at 2-second intervals for a typical summer day (August 20, 2024; peak air temperature of 43.3 degrees Celsius) at 0700, 1300, and 1700 (local times). This dataset can inform heat mitigation strategies for vulnerable populations in Phoenix.
SEV-LTER Mean - Variance Experiment Plains Grassland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And How does micro-environmental variation among plots influence how treatments alter soil moisture profiles over three soil depths? This data package includes sensor data from the Mean x Variance experiment in the Plains grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM, which is dominated by the grass species Bouteloua gracilis (blue grama).
SEV-LTER Mean x Variance Experiment Desert Grassland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean Variance Experiment Desert Shrubland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert Shrubland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean Variance Experiment Juniper Savanna Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Juniper Savanna ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean x Variance Experiment Pinon Juniper Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Pinon Juniper ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
seNorge/TG: daily mean temperature over Norway
<p>seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily mean temperature (TG) fields for the 66-year time period 1957-2022. TG is the mean temperature between 06:00 UTC of the day in the timestamp and 06:00 UTC of the previous day. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018</p>
A global spatiotemporally seamless daily mean land surface temperature from 2003 to 2019
<p>The global daily mean land surface temperature product (GADTC product) was generated based on the improved ADTC-based framework (termed IADTC framework) which basically combines the annual temperature cycle and diurnal temperature cycle model. </p> <p>The GADTC product is organized by year and each .tif image contains the global spatiotemporally seamless daily mean land surface temperature (LST) for each day with the unit of Kelvin. </p> <p>The demo code of the IADTC framework is available at https://github.com/faluhong/IADTC-framework. </p>
What Controls the Mean East–West Sea Surface Temperature Gradient in the Equatorial Pacific: The Role of Cloud Albedo
<p>Climatologies for the climate model simulations performed by Burls and Fedorov 2014, Journal of Climate, <a href="https://doi.org/10.1175/JCLI-D-13-00255.1">https://doi.org/10.1175/JCLI-D-13-00255.1</a>. This table shows how the names of the simulation files provided in this dataset relate to the experiment names provided in Table 1 of Burls and Fedorov (2014, JOC).</p> <table> <thead> <tr> <th scope="col">Experiment # in Article (Table 1)</th> <th scope="col">Name of Files</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>PreInd_T31_gx3v7*.nc</td> </tr> <tr> <td>2</td> <td>80p_op_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>3</td> <td>60p_op_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>4</td> <td>40p_op_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>5</td> <td>20p_op_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>6</td> <td>20p_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>7</td> <td>40p_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>8</td> <td>60p_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>9</td> <td>80p_LWP_1590deg_T31_gx3v7*.nc</td> </tr> <tr> <td>10</td> <td>20p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>11</td> <td>40p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>12</td> <td>60p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>13</td> <td>80p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>14</td> <td>20p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> <tr> <td>15</td> <td>40p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> <tr> <td>16</td> <td>60p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> <tr> <td>17</td> <td>80p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> <tr> <td>18</td> <td>20p_ILWP_3060deg_tropx8_T31_gx3v7*.nc</td> </tr> <tr> <td>19</td> <td>40p_ILWP_3060deg_tropx8_T31_gx3v7*.nc</td> </tr> <tr> <td>20</td> <td>60p_ILWP_3060deg_tropx8_T31_gx3v7*.nc</td> </tr> <tr> <td>21</td> <td>80p_ILWP_3060deg_tropx8_T31_gx3v7*.nc</td> </tr> <tr> <td>22</td> <td>PreInd_0.9x1.25_gx1v6*.nc</td> </tr> <tr> <td>23</td> <td>40p_LWP_1590deg_0.9x1.25_gx1v6*.nc</td> </tr> <tr> <td>24</td> <td>60p_LWP_1590deg_0.9x1.25_gx1v6*.nc</td> </tr> <tr> <td>25</td> <td>40p_ILWP_1590deg_tropx2_0.9x1.25_gx1v6*.nc</td> </tr> <tr> <td>26</td> <td>60p_ILWP_1590deg_tropx2_0.9x1.25_gx1v6*.nc</td> </tr> </tbody> </table> <p>Article abstract:</p> <p>The mean east–west sea surface temperature gradient along the equator is a key feature of tropical climate. Tightly coupled to the atmospheric Walker circulation and the oceanic east–west thermocline tilt, it effectively defines tropical climate conditions. In the Pacific, its presence permits the El Niño–Southern Oscillation phenomenon. What determines this temperature gradient within the fully coupled ocean–atmosphere system is therefore a central question in climate dynamics, critical for understanding past and future climates. Using a comprehensive coupled model [Community Earth System Model (CESM)], the authors demonstrate how the meridional gradient in cloud albedo between the tropics and midlatitudes (Δα) sets the mean east–west sea surface temperature gradient in the equatorial Pacific. To change Δα in the numerical experiments, the authors change the optical properties of clouds by modifying the atmospheric water path, but only in the shortwave radiation scheme of the model. When Δα is varied from approximately −0.15 to 0.1, the east–west SST contrast in the equatorial Pacific reduces from 7.5°C to less than 1°C and the Walker circulation nearly collapses. These experiments reveal a near-linear dependence between Δα and the zonal temperature gradient, which generally agrees with results from the Coupled Model Intercomparison Project phase 5 (CMIP5) preindustrial control simulations. The authors explain the close relation between the two variables using an energy balance model incorporating the essential dynamics of the warm pool, cold tongue, and Walker circulation complex.</p>
Percentiles of daily mean, min, and max temperature 1986-2005
<p>Hourly ERA5 air temperature at surface data at 0.5˚ grid resolution was downloaded from the Climate Data Store service for the period 1986-2005 (corresponding to the standard IPCC climate reference period). Daily mean, minimum, and maximum temperatures were computed. Percentile values of these were computed over the range of 1% to 99% in 1% steps.</p>
Mean Land Surface Temperature in the Municipality of São Paulo between 2017 and 2023
<p>Mean Land Surface Temperature (LST) and mean Normalized Difference Vegetation Index (NDVI) for each pixel in the municipality of São Paulo, in Brazil, using Landsat 8 data from 2017-01-01 to 2023-01-01 (6 years) and the algorithm from Ermida et al. (2020). The average overpass time is 10:04 a.m. in the local time (GMT-3) and the spatial resolution is 30 meters.</p> <p>Variables:</p> <ul> <li>mean_LST: mean Land Surface Temperature [Celsius (oC)]</li> <li>mean_NDVI: mean NDVI</li> <li>*_sd: Standard deviation of the mean</li> <li>RED, GREEN, BLUE and NIR: mean reflectance of each band</li> </ul> <p>Acknowledgments: FAPESP grant 2021/11762-5</p>
Maximum temperature data from thermal safety assessment of type 21700 lithium-ion batteries with NMC, NCA and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)
<p>Data of safety investigation and thermal abuse behavior of commercial type 21700 LIB cells is provided.</p> <p>It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC.</p> <p>Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at different states of charge (SOC) from 0 to 100.</p> <p>Different cathode chemistries are compared (NMC, NCA and LFP), as well as for NCA chemistry, the high energy (HE) and high power (HP) cell design.</p> <p>For each cell, data includes the maximum temperature measured during thermal abuse at the surface on the center of the cell. Additionally, the mean value and standard deviation for each cell type and state of charge is provided.</p> <p>This data is supporting this article in the journal Batteries:</p> <p><a href="https://doi.org/10.3390/batteries9050237">https://doi.org/10.3390/batteries9050237</a></p> <p>Additional supporting material to this article are the exothermal data for thermal abuse, that are published here:</p> <p><a href="https://doi.org/10.5281/zenodo.7707929">https://doi.org/10.5281/zenodo.7707929</a></p> <p> </p>
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), Andrews Experimental Forest
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), adjusted for the effects of solar radiation and sky view factors, Andrews Experimental Forest. Maps were created using PRISM (Parameter-elevation Regressions on Independent Slopes Model), developed by Dr. Christopher Daly at Oregon State University’s PRISM Climate Group in 2010 (prism.oregonstate.edu). Grids were exported into ASCII format from GRASS GIS software; values are in degrees C x 100. Spatial resolution is 50 meters. Two sets of temperature values are available: (1) values derived from an interpolation of point station temperature values accounting for elevation; and (2) values from (1), adjusted for effects of solar radiation exposure and sky view factors. Radiation exposure and sky view factors were calculated from a two-stream solar radiation model that accounts for elevation, slope, aspect, and shading from adjacent pixels on a 50-m digital elevation model. Temperature data were obtained from selected benchmark and reference stand climate stations within the HJ Andrews, as well as National Weather Service Cooperative (COOP) and USDA NRCS Snow Telemetry (SNOTEL) stations in the vicinity. Due to the sparseness of the station data outside the Andrews, values outside the Andrews are considered to have high uncertainty. Temperature values assume an open site with no canopy cover, so are not appropriate for describing temperatures within the forest canopy. See MS033 for radiation grids used to make the radiation adjustments.
Hourly Mean Radiant Temperature Distribution on a summer day (2012-06-27) in the greater Phoenix, Arizona (USA) metropolitan area
This dataset is a collection of 1-m resolution mean radiant temperature (Tmrt) rasters generated using digital surface models and the Solar and longwave environmental irradiance geometry (SOLWEIG) model. The dataset provides hourly Tmrt in the Phoenix, Arizona (USA) metropolitan area for a typical summer day (June 27, 2012, peak air temperature of 41 degrees C) from 07:00 hrs to 20:00 hrs (local time (America/Phoenix)). The dataset can serve as a guide for heat mitigation programs within the area and as input for heat exposure studies.
Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates
<p>Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. </p><p>Data is provided in .nc files, one for each climate.</p>
Global 1-km monthly mean land surface temperature product (2003-2020)
<p>The monthly mean land surface temperature (MMLST) reflects stable intra- and inter-annual temperature variations, and has a wide range of applications in climatological and meteorological studies. We used a combination method (including a weighted average model derived from 253 flux sites and considering the influence of the count of valid observations) and MODIS instantaneous LST products (MOD11A1 and MYD11A1) to generate a global 1-km MMLST dataset for the years 2003–2020. The validation with SURFRAD stations showed a root mean square error of 1.5 K and a Bias of 0.5 K. Compared with existing MMLST product, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature, and had a better ability to retrieve spatial details of temperature variations.</p> <p> </p>
Weekly mean parasitism rate (%) Daily mean Temperature (°C) Daily Mean R.H. (%) Figure 7. Parasitism rate of Aenasius arizonensis with Daily mean temperature and mean R.H.in 2018 for Tömük / Mersin. in Determining some biological parameters of Aenasius arizonensis (Girault) (Hymenoptera: Encyrtidae) on cotton mealybug and the rate of parasitism in field conditions
Weekly mean parasitism rate (%) Daily mean Temperature (°C) Daily Mean R.H. (%) Figure 7. Parasitism rate of Aenasius arizonensis with Daily mean temperature and mean R.H.in 2018 for Tömük / Mersin.
Fig. 7. Daily mean temperature variation recorded from 28 December 2004 in A New Genus of Microteiid Lizard from the Caparaó Mountains, Southeastern Brazil, with a Discussion of Relationships among Gymnophthalminae (Squamata)
Fig. 7. Daily mean temperature variation recorded from 28 December 2004 to 17 November 2005 by a data logger left at 2400 m elevation at Parque Nacional do Caparaó, near the trail leading to the Pico da Bandeira.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.