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32 results for “clear-sky”
CO2-dependence of Longwave Clear-sky Feedback is sensitive to Temperature [Dataset]
<p>These data are simulated from PyRads (https://github.com/danielkoll/PyRAD) and are used to plot figures in our study.</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>
Clear-sky Outgoing Longwave Radiation Fluxes from Offline RTE-RRTMGP for AMIP Simulation of CMIP6 Models
<p>This study utilizes RTE-RRTMGP (<a href="https://doi.org/10.1029/2019MS001621">https://doi.org/10.1029/2019MS001621</a>) to compute clear-sky, aerosol-free outgoing longwave radiation (OLR) fluxes for AMIP simulation of CMIP6 models, using 36-year average of monthly-mean temperature and humidity profiles at 19 pressure level as an input. This dataset is used to investigate the cause of inter-model spread in OLR when the same set of radiative transfer schemes is used.</p>
Irradiance measurements, satellite images, and clear-sky expectations for Spring 2014 in Tucson, AZ.
<p>This dataset contains the metadata and measurements from a number of irradiance sensors and rooftop solar power systems in addition to clear-sky expectations for those sensors. Irradiance derived from images from the GOES-W satellite are also included. The dataset covers April, May, and June 2014 over the Tucson, AZ, area. The data is in a number of formats including CSV, plain text, HDF5, and netCDF4. </p> <p> </p> <p>See the following for a description of the NREL calibrated GHI sensor:</p> <p>Andreas, A.; Wilcox, S.; (2010). Observed Atmospheric and Solar Information System (OASIS); Tucson, Arizona (Data); NREL Report No. DA-5500-56494. http://dx.doi.org/10.5439/1052226</p> <p> </p> <p>More information about the custom irradiance and rooftop PV sensors can be found in:</p> <p>A. T. Lorenzo, W. F. Holmgren, M. Leuthold, C. K. Kim, A. D. Cronin, and E. A. Betterton, “Short-term PV power forecasts based on a real-time irradiance monitoring network,” in <em>2014 IEEE 40th Photovoltaic Specialist Conference (PVSC)</em>, 2014, pp. 0075–0079.</p>
WRF-Solar AOD550 & Clear-Sky Irradiance Forecasts & Verifying Observations over CONUS
<p>This dataset is fully described in and is a companion of Lee et al. (2022):</p> <p>Lee, J. A., P. A. Jiménez, J. Dudhia, and Y.-M. Saint-Drenan, 2023: Impacts of the aerosol representation in WRF-Solar clear-sky irradiance forecasts over CONUS. J. Appl. Meteor. Climatol., 62, 227–250, https://doi.org/10.1175/JAMC-D-22-0059.1.</p> <p>Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) The default Tegen climatology; 2) Imposing AOD forecasts from the GEOS-5 model; 3) Imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model; and 4) The Thompson-Eidhammer aerosol-aware water/ice-friendly aerosol climatology. Over eight months of these 15-min output forecasts are compared against high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected due to the lesser sensitivity of GHI to aerosol loading. Hourly-average clear-sky DNI and GHI was also analyzed, and additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is.</p> <p>wrf_aeronet_aod550_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 317 MB</p> <p>WRF-Solar AOD550 spatially interpolated to AERONET stations and AERONET observed AOD550. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_inst_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 102 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 1 h to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>cams_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>CAMS clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 00 UTC from 20191119 to 20200730) and model lead time (every 1 h to 54 h).</p>
Data and code for Spaulding-Astudillo and Mitchell (2023c), "Clear-sky convergence and the origin of tropical congestus clouds"
<p>Data and code for Spaulding-Astudillo and Mitchell (2023c), "Clear-sky convergence and the origin of tropical congestus clouds".</p><p>The Python code used to create Figures 1, 3-6 and supplementary Figure 1 are included in separate directories. Within each directory, we include experimental data generated by the Reference Forward Model (RFM; Dudhia 2017) that is needed to reproduce the figures. To create the figures, run the provided code in a Jupyter notebook or from the command line. Helper functions for data processing within the figure scripts have the nomenclature ..._data.py. Note that the provided code only allows you to visualize RFM data, not produce it yourself. To do so, you must download and run RFM yourself (https://eodg.atm.ox.ac.uk/RFM/). In addition, Figure 1 requires spatio-temporally averaged data from MERRA-2, ERA5, and CloudSat/CALIPSO (Betrand et al. 2023), which we provide in pickle files. See the main text for data citations, which will allow you to access the original, non-averaged climate data. </p><p>RFM data directories have the nomenclature /ctrl-..... For example, ctrl-RHmid-75-zmid-7-uniform-1 where:<br>RHmid=75% is the relative humidity at a mid-tropospheric height zmid=7.5 km. Uniform=True indicates that the tropospheric relative humidity is constant with height. When uniform=False, the relative humidity follows a C-shaped distribution between the surface and the tropopause. The helper functions in ..._data.py allow you to read the RFM output from .asc files. The nomenclature of the RFM output files is standardized: https://eodg.atm.ox.ac.uk/RFM/. </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p>
A synopsis of AIRS global-mean clear-sky radiance trends from 2003 to 2020
<p>Here archived the time series of global-mean clear-sky BTs from AIRS observation, GEOS-5, and ERA5 simulations used for trend analysis.</p>
Selected clear-sky FloX hyperspectral data
<p>Zip - Folders containing calibrated reflectance, reflected radiance and incoming radiance retrieved from measurements with FloX field spectrometers over Alfalfa (2018 in Grosseto, Italy) and grass (2018 in Julich, Germany) during clear sky conditions. The incoming radiance is measured with optics of hemispherical (~180°) field of view and the reflected radiance with conical (~25°) field of view.</p> <p>The data is organized in sub-folders inside the zip-repositories which are named according to the date of recording, containing CSV-files which hold the hyperspecteral information. One file is associated to the integrated full-range (FULL) spectrometer between 340nm and 1000nm, and the fluorescence-range (FLUO) spectrometer between 650nm and 810nm, containing incoming radiance, reflected radiance or reflectance, respectively. The first collumn contains the associated central wavelengths, the first row the time of recording, each subsequent field of the table contains the calibrated radiance (in W m<sup>-2</sup> sr<sup>-1</sup> nm<sup>-1</sup>) or reflectance (unitless) values.</p>
Dataset for "State-dependence of the equilibrium climate sensitivity in a clear-sky GCM" by Matthew Henry et al. (2023).
<p>Dataset for "State-dependence of the equilibrium climate sensitivity in a clear-sky GCM" by Matthew Henry, Geoffrey K. Vallis, Nicholas J. Lutsko, Jacob T. Seeley, and Brett A. McKim.</p>
Primary data for "Temperature-dependence of the clear-sky feedback in radiative-convective equilibrium"
<p>Primary data for the manuscript "Temperature-dependence of the clear-sky feedback in radiative-convective equilibrium".</p> <p>The archive is described in more detail in the enclosed README file.</p>
Clear-sky profile database for the development of Land Surface Temperature algorithms
<p>This dataset includes clear sky atmospheric profiles from the European Centre for Medium Range Forecast (ECMWF) version-5 reanalysis (ERA5), specially selected to support the development of algorithms of Land Surface Temperature (LST) retrieval from Earth observation (EO) data. The profiles were re-sampled from an ERA5 dataset covering the 2009-2019 period, with a 1x1 degree spatial resolution, hourly sampling and using the full vertical resolution (137 model levels). The re-sampling technique is based on a dissimilarity criterion applied to profiles of temperature and specific humidity, in order to obtain regular distributions of atmospheric variables of relevance for LST retrieval in the Thermal Infrared (TIR) spectral range. The database is limited to clear-sky conditions over land, being therefore suitable for the development of satellite land products relying on optical and thermal infrared imagery in general, despite targeting especially LST.</p> <p><strong>Dataset description:</strong></p> <p>The dataset is divided in multiple netCDF4 files based on the range of skin temperature (Tskin; Kelvin) and the range of total column water vapour (TCWV; mm). Each file includes the following variables:</p> <ul> <li>Time</li> <li>Longitude</li> <li>Latitude</li> <li>2-m temperature (t2m)</li> <li>Surface pressure (sp)</li> <li>Total cloud cover (tcc)</li> <li>Total column water vapour (tcwv)</li> <li>Skin temperature (skt)</li> <li>Surface emissivity (emis)</li> <li>Land cover classification (lcc)</li> <li>Temperature profile (t)</li> <li>Specific humidity profile (q)</li> <li>Ozone profile (o3)</li> <li>Pressure profile (p)</li> </ul> <p>All profiles are provided on model levels. For each profile, 6 values of skin temperature and 25 values of emissivity are provided (see publication for details). Emissivity values correspond to the wavelengths of ~11 and ~12 µm.</p> <p><strong>Credit:</strong></p> <p>To use this data please cite this dataset and the respective journal publication:</p> <p>Ermida, S.L.; Trigo, I.F. (2022) A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms. <em>Remote Sens.</em>, <em>14</em>, 2329. <a href="https://doi.org/10.3390/rs14102329">https://doi.org/10.3390/rs14102329</a> </p> <p> </p> <p><strong>Access: </strong></p> <p>Currently, Zenodo does not provide a simple way to download datasets with a large number of files. We recomend trying the <a href="https://zenodo.org/record/3676567#.YnJAxtPMJhE">Zenodo_get</a> to simplify the download.</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2016.1-2016.4)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2016.01-2016.04</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2016.01-2016.04</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: Himawari-8/AHI hourly clear-sky 0.02° LST (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 clear-sky LST dataset derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8/AHI thermal infrared data, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The temporal resolution and spatial resolution of this dataset are one hour and 0.02°, respectively.</p> <p>This is the ELITE Himawari-8/AHI clear-sky LST product in 2020. Please <a href="https://zenodo.org/record/7316873"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019 and <a href="https://zenodo.org/record/7281765"><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: AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W)</li> <li>Temporal Coverage: 2020</li> <li>Spatial Resolution: 0.02°</li> <li>Temporal Resolution: one hour</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <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 (eliteqrs@126.com).</p>
ELITE land surface temperature: FY-4A/AGRI hourly 4km clear-sky LST (2018)
<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 4km hourly clear-sky LST dataset derived by the iTES algorithm (Liu et al. 2022) from the FY-4A/AGRI thermal infrared data, covering the AGRI 4km nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W). The in-situ validation results show that the bias and RMSE of the retrieved AGRI LST are 0.58 and 2.93 K in the daytime, and −0.30 and 2.18 K in the nighttime, respectively. The temporal resolution and spatial resolution of this dataset are one hour and 4km, respectively.</p> <p>This is the ELITE FY-4A/AGRI clear-sky LST product in 2018. Please <strong><em>click here</em></strong> to download the ELITE LST product in 2019.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W)</li> <li>Temporal Coverage: 2018 (starting from Apr.)</li> <li>Spatial Resolution: 4 km (subsatellite point)</li> <li>Temporal Resolution: one hour</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Liu, W., Shi, J., Liang, S., Zhou, S., & Cheng, J. (2022). Simultaneous retrieval of land surface temperature and emissivity from the FengYun-4A advanced geosynchronous radiation imager. International Journal of Digital Earth, 15, 198-225</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2016.5-2016.8)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2016.05-2016.08</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2016.05-2016.08</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2016.9-2016.12)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2016.09-2016.12</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2016.09-2016.12</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2017.1-2017.4)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2017.01-2017.04</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2017.01-2017.04</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2017.9-2017.12)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2017.09-2017.12</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2017.09-2017.12</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2018.1-2018.4)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2018.01-2018.04</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2018.01-2018.04</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2018.5-2018.8)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2018.05-2018.08</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2018.05-2018.08</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</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.