Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
748
datasets available to search
ShareScore release 0.9.0
Dataset results
748 results for “surface temperature”
Figure 5 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 5. Examples of using a cloud mask (satellite image taken on 29 July 2008). At the time of the satellite's flight, all measurement points for the day are blocked by clouds: (a) satellite image in natural colors with missing information along the bands; (b) same image with cloud mask superimposed. Red dots show several locations of one drifter during the day of satellite image acquisition.
Time series of electrical conductivity, temperature and relative stream stage recorded in surface water and streambed sediments of River Erpe and River Gruendlach, Germany
<p><span><a href="../api/records/13336325/draft/files/temp_EC_timeseries.csv/content" target="_blank" rel="noopener noreferrer">temp_EC_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC) and relative stream stage (cm) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Gruendlach, Germany.</p> <p> </p> <p><span><a href="../api/records/13336325/draft/files/porewater_ec_timeseries.csv/content" target="_blank" rel="noopener noreferrer">porewater_ec_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC), relative stream stage (cm) and total pressure (hPa) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Ammer, Germany, and the Sturt River, South Australia.</p>
Quasi-invariance of Tropical Meridional Surface Temperature Gradient in a Wide Range of Climates
<p>Data generated in the study "Quasi-invariance of Tropical Meridional Surface Temperature Gradient in a Wide Range of Climates" are archived here</p>
Supplementary data for ''Observations of the Clear-Sky Spectral Longwave Feedback at Surface Temperatures Between 210K and 310K"
<h3>This is supplementary data for the manuscript ''Observations of the Clear-Sky Spectral Longwave Feedback at Surface Temperatures Between 210K and 310K".</h3> <h3>satellite_data.nc</h3> <p>This file contains the spectral feedback derived from satellite observations by the AIRS instrument (Huang et al., 2020, DOI: 10.5067/5P7KQ31XI7XJ) as a function of wavenumber and surface temperature regime.</p> <h3>model_data_Ts.nc</h3> <p>This file contains the atmospheric profiles from our single-column model that were used for the radiative transfer calculations as well as the spectrally resolved outgoing longwave radiation from those calculations.</p> <h3>model_data_regimes.nc</h3> <p>This file contains derived atmospheric variables used for our analysis as well as the spectral longwave feedback for different surface temperature regimes.</p> <h3>brightness_temperatures.nc</h3> <p>This file contains the simulated and observed brightness temperatures averaged over the atmospheric window for different skin temperatures.</p> <h3>surface_skin_temperatures.nc</h3> <p>This file contains bin-averaged near-surface air temperatures and skin temperatures from ERA5 reanalysis and AIRS retrieval.</p>
North Atlantic average sea-surface temperature in a CMIP6 multi-model ensemble of historical and ssp585/ssp245 simulations
<p>This dataset contains North Atlantic average sea-surface temperatures and derived indices calculated for a multi-model ensemble of historical and future scenario (ssp585, ssp245) simulations contributing to the Coupled Model Intercomparison Project phase 6. A detailed description of the dataset is provided by Zanchettin, D., and Rubino, A., Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability, Communications Earth & Environment, 2024, doi:10.1038/s43247-024-01804-x.</p> <p><br>The data are provided as netcdf files.</p> <p>The name of each file is structured as {model}_r{realization}_historical_{scenario}.nc where {model} is the model name, {realization} is a number corresponding to the historical realization, and {scenario} is either of the two scenarios considered (ssp585 and ssp245).</p> <p>Each file contains data for the following one dimensional variables:</p> <ul> <li>year: the sequence of years for which the data are provided</li> <li>NASST: annual-average spatially averaged North Atlantic sea-surface temperature</li> <li>state: slowly variable component of NASST obtained from a dlm decomposition of NASST</li> <li>strend: stochastic trend of NASST obtained from a dlm decomposition of NASST</li> <li>AMV: Atlantic Multidecadal Varibility index obtained as difference between NASST and state</li> </ul> <p> </p>
Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures
<p><strong>Data repository for <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em></strong></p> <p>This repository contains LIM data underpinning the paper <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em> [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in <em>Weather and Climate Extremes</em> 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details). </p> <p>The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. <strong>In all cases, please see the paper for important details on the data and how they were produced.</strong> </p> <p><em>Global LIMs</em></p> <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> <p><em>Tropical Pacific Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> <p><em>Indian Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> <p><strong>How to cite this</strong> <strong>repository</strong></p> <p>If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a> </p>
A Daily near-surface Air Temperature Dataset for China from 1979 - 2018
<p>The CDAT dataset contains near-surface air temperature data for China during the period of 1979-2018, in Celsius, daily temporal (T<sub>max</sub>, T<sub>min</sub>, T<sub>avg</sub>), and 0.1º spatial resolution.</p> <p>This product integrates multiple data sources such as reanalysis data (ERA5, CMFD), remote sensing data (MODIS), and in situ data, and is obtained by combining the temperature strategy to distinguish between clear sky and non-clear sky weather conditions. It is proved that this dataset has high accuracy and can be used in the further study of regional climate change.</p>
Data and code to accompany 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record'
<p>Data and analysis code to accompany the manuscript 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record', published in <em>Oceanography and Marine Biology: An Annual Review </em>(<a href="https://doi.org/10.1201/9781003363873-2">https://doi.org/10.1201/9781003363873-2</a>). Data files include records of sea surface temperature (SST) collected in Pacific Grove, California, USA from January 20, 1919 to the end of 2020. The analysis code produces a continuous 100+ year record with adjustments made for time of day the data were collected, and filling gaps in the data set where necessary. </p> <p>This dataset makes use of an earlier 83-year version of the sea surface temperatures produced by Breaker et al. 2005 available at <a href="https://aquadocs.org/handle/1834/20890">https://aquadocs.org/handle/1834/20890</a>, with the data file available at <a href="https://purl.stanford.edu/rc833pc4972">https://purl.stanford.edu/rc833pc4972</a> </p>
Surface measurement data of polished LTCC: Characterization of pores in polished low temperature co-fired glass-ceramic composites for optimization of their micromachining
<p>Pores are intrinsic defects of ceramic composites and influence their functional properties significantly. Their characterization is therefore a pivotal task in material and process optimization. It is demonstrated that polished section analysis allows for obtaining precise information on pore size, shape, area fraction, and homogeneous distribution. It is proven that laser scanning microscopy provides accurate height maps and is thus an appropriate technique for assessing surface features. Such data is used to compare areas with good and poor polishing results, and various surface parameters are evaluated in terms of their informative value and data processing effort. The material under investigation is a low-temperature co-fired ceramic composite. Through statistical analysis of the data, the inclination angle was identified as an appropriate parameter to describe the polishing result. By using masked data, direct conclusions can be drawn about the leveling of load-bearing surface areas, which are crucial in photolithographic processing steps and bonding technology. A broad discussion of different defects based on the results contributes to a critical analysis of the potentials and obstacles of micromachining of low-temperature cofired ceramic substrates.</p>
Dataset for "Roles of surface forcing in the Southern Ocean temperature and salinity changes under increasing CO2: perspectives from model perturbation experiments and a theoretical framework"
<p>Reference: Kewei Lyu, Xuebin Zhang, John A. Church, Quran Wu, Russell Fiedler, and Fabio Boeira Dias (2022), Roles of surface forcing in the Southern Ocean temperature and salinity changes under increasing CO<sub>2</sub>: perspectives from model perturbation experiments and a theoretical framework, <em>Journal of Physical Oceanography</em>, <a href="https://doi.org/10.1175/JPO-D-22-0095.1">https://doi.org/10.1175/JPO-D-22-0095.1</a></p>
Changes in community-weighted trait mean, functional diversity, precipitation, temperature and surface area along an elevational gradient in Tenerife, Canary Islands
<p>This dataset comprises community-weighted trait means and functional diversity of leaf traits, precipitation, temperature and surface area of the elevational belt recorded in roadside (disturbed) and interior (less disturbed) plots, along an elevational gradient of 2,300 m in Tenerife, Canary Islands. The leaf traits measured were specific leaf area (SLA), nitrogen, carbon, phosphorous, nitrogen to carbon ratio, leaf dry matter content (LDMC), sodium, potassium and magnesium. The environmental variables measured are total precipitation of the growing season, mean temperature of the growing season and surface area of the elevation belt. This dataset has been used for the analysis presented in Ratier Backes et al. (in press). Mechanisms behind elevational plant species richness patterns revealed by a trait-based approach. <em>Journal of Vegetation Science</em>.</p>
Probing temperature-responsivity of microgels and its interplay with a solid surface by superresolution microscopy and numerical simulations
<p>This dataset supports the publication ' Probing temperature-responsivity of microgels and its interplay with a solid surface by super resolution microscopy and numerical simulations' published on ACS Nano. DOI: https://doi.org/10.1021/acsnano.2c07569</p>
Data from: Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing
<p>This dataset contains the data for the publication " Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing "</p>
Discrete surface turbidity samples and underway sea surface temperature and sea surface salinity measured in Aarhus Bay during a demonstration of an experimental autonomous surface vehicle
<p>This dataset includes measurements obtained by an autonomous boat that was equipped with a surface water sampling system: the Naval Operating Research Drone Assessing Climate Change (NORDACC). </p> <p>This dataset includes two .csv files</p> <p><br> 2022-10-14_NORDACC_Turbidity.csv<br> This file contains the results of 8 discrete surface water samples that were analyzed for turbidity using a Hach turbidimeter. Surface water samples were acquired by NORDACC on the afternoon of 14 October 2022 in Aarhus Bay. The columns are separated by commas and correspond to: <br> Sample Number, Date (yyyy-mm-dd), UTC time (HH:MM:SS), Longitude (decimal degrees), Latitude (decimal degrees), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> 2022-10-14_NORDACC_UnderwayData.csv<br> This file contains 1 Hz data, delimited by commas, that were collected while NORDACC was in operation. The underway data columns correspond to:<br> Date & Time (ISO format yyyy-mm-ddTHH:MM:SS), Operation State (1=initializing, 2=sailing, 3=water sample), Longitude (decimal degrees), Latitude (Latitude), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> About NORDACC:</p> <p>The Naval Operating Research Drone Assessing Climate Change (NORDACC) was designed by Serbian Akbulut, Jeppe Fogh Rasmussen, Christian Søndergård Hestbech, and Marius Hjorth Andersen, a group of mechatronics students at Aarhus University. The project was supervised by Prof. Claus Melvad (AU) and received external guidance by Dr. Daniel Carlson (Helmholtz-Zentrum Hereon). The NORDACC project was partially supported by Helmholtz-Zentrum Hereon and the Klaus-Tschira Boost Fund that was administered by the German Scholars Organization.</p> <p>NORDACC designs, software, and BOM are open source and provided via Mendeley Data, doi:10.17632/rpzv35pccr.1 </p> <p>For more information about NORDACC see the accompanying paper in HardwareX. </p>
Rising surface temperatures lead to more frequent and longer burrow retreats in males of the fiddler crab, Minuca pugnax
<p><span>The fiddler crab <em>Minuca</em> <em>pugnax</em> occupies thermally unstable mudflat habitats along the eastern United States coastline, where it uses behavioral thermoregulation, including burrow retreats, to manage body temperature (T<sub>b</sub>). We explored the relationship between frequency of burrow use and environmental conditions, including burrow and surface temperatures, relative tidal height, and time of day, by twenty male <em>M. pugnax</em> in breeding areas around Flax Pond, New York, USA. We found a highly significant positive correlation between burrow use and surface temperature, with a clear shift to longer times underground above 32°C degrees. We also experimentally heated live crabs in the laboratory and allowed them to retreat into cooled artificial burrows while continuously measuring body temperatures (T<sub>b</sub>). Laboratory data on cooling times were compared to field observations of burrow retreat durations. The median burrow stay in the field of 2.74 min was enough time for our laboratory crabs to capture over 70% of the cooling potential of artificial burrows 10 or 15 degrees below T<sub>b</sub>. Because crab bodies in burrows experience exponential declines in T<sub>b</sub> due to Newton's law of cooling, there are diminishing returns to remaining in a burrow, and many crabs probably leave before coming to equilibrium. For <em>M. pugnax</em>, burrow retreats reduce time spent feeding and courting, activities that only occur on the surface. Current concerns about the impacts of climate change on animals include whether compensatory mechanisms, like more frequent and longer burrow retreats, will come at the cost of other behaviors necessary for survival and reproduction.</span></p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for 24 hours lead time
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for 24 hours lead time in the years 2015-2020. The data set is provided in .Rdata format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_gamvinereg">https://github.com/jobstdavid/paper_gamvinereg</a></p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for five different lead times
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for the lead times 24, 48, 72, 96 and 120 hours in the years 2015-2020. The data set is provided in .RData format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_tsEMOS">https://github.com/jobstdavid/paper_tsEMOS</a></p>
Global Mean Surface Temperatures for 100 Phanerozoic Time Intervals
<p>This set of Global Mean Surface Temperature (GMST) arrays for 100 Phanerozoic time intervals (stage level) are based on HadleyCM3L simulations (Valdes et al, 2021) that have been modified to better agree with geochemical proxy data (∂18O) and more equable pole-to-equator temperature gradients deduced from lithological indicators of climate (evaporites, calcrete, coals, bauxites, and tillites, etc.) (Scotese et al., 2021). The resolution is 1x1 degrees (latitude/longitude) in three formats: grid-reference, coordinate-list, and netcdf. The accompanying pdf, “Global Mean Temperatures during the Phanerozoic”, by C. R. Scotese, describes the content and methodology used to produce these files. For more information contact: cscotese@gmail.com.</p> <p>*C1 technical correction to the netcdf file grid (361x181).</p> <p>Please cite these following sources when using these data:</p> <p>Scotese, C. R., Song, H., Mills, B. J. W., & van der Meer, D. G. (2021). Phanerozoic paleotemperatures: The earth’s changing climate during the last 540 million years. <em>Earth-Science Reviews</em>, <em>215</em>, 103503. <a href="https://doi.org/10.1016/j.earscirev.2021.103503">https://doi.org/10.1016/j.earscirev.2021.103503</a></p> <p>Valdes, P.J., Scotese, C.R., and Lunt, D.J. (2021). Deep Ocean Temperatures through Time, Climates of the Past, Discussions, <a href="https://doi.org/10.5194/cp-2020-83">https://doi.org/10.5194/cp-2020-83</a>.</p> <p> </p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2021)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE hourly seamless 0.02 ° LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the seamless LST dataset in 2021. Please<a href="../records/10668883" target="_blank" rel="noopener"> <strong><em>click here</em></strong></a> to download the ELITE LST product in Januray-June, 2022.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2021</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2020)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2020. Please <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019 and <a href="https://zenodo.org/record/8260240"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2020</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.