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13 results for “GOES-16”
GOES-16 Derived 1-km resolution Daily Solar Insolation (2022)
<p><strong><span>Dataset Description</span></strong></p> <p><span>This is the University of Alabama in Huntsville (UAH) GOES satellite-derived solar insolation dataset. The dataset and method have been described in previous documents, including Jacobs et al. (2008), Paech et al. (2009), Mecikalski et al. (2011, 2018), Diak (2017) and Cheng et al. (2020). The data are in separate files for Julian days 1-365 (or 366). The dataset extends for the period 1 January 1985 through 31 December 2023.</span></p> <p><span>Prior to 2022, the dataset domain was on a 474 x 407 grid at 2 km resolution centered on the State of Florida. From 2022 onward the data domain is on a 1668 x 1668 grid of 1 km resolution that includes several Southeastern U. S. states in addition to Florida. The daily solar insolation is in units of MegaJoules per square meter (MJ/m^2/day), and the data themselves are in compressed ASCII format with the latitude (degrees) and longitude (degrees) of a given point listed. These solar insolation data are used to produce an evapotranspiration (ET) product that covers all watersheds that flow onto the extent of the Floridan aquifer system, which will allow water managers in north Florida to use the dataset when conducting water budget analyses that necessarily cross state borders. The ET data are subsequently used for climatological studies and water resources management.</span></p> <p><span>From 1985 to 2021, the solar insolation data was developed using native 1-km resolution GOES-East visible channel data. For each grid point, an averaging of 4 x 4 1-km resolution pixels was done to define a 2 km resolution product. For the 2022 to current datasets, native 500-meter, 10-minute resolution channel 2 (0.64 µm) data are used rom the GOES-16/-18 satellite's Advanced Baseline Imager. For each grid point from 2022 onward, an averaging of 2 x 2 500-meter resolution pixels occurred to define a 1 km resolution product. The GOES data were gathered from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).</span></p> <p><span>Prior to 2022, a simple climatological or daily model reanalysis based total precipitable water (TPW) correction was implemented to the solar insolation data. From 2022 onward, the TPW correction for solar insolation is performed using the NESDIS Blended Hydrometeorological Product suite (BLENDHYDRO) “blended total precipitable water” data. These data, as obtained from NOAA CLASS, represents a merging of microwave derived TPW products from multiple polar-orbiting and geostationary satellite sensors including: AMU/MHS onboard the NOAA and MetOp satellite series; SSMIS onboard the DMSP satellite series; ATMS onboard S-NPP and NOAA-20; Sounder onboard the GOES satellite series; and GPS Met onboard Orbview-1, and have a spatial resolution of 16 km at the Equator. From 24 hourly files per day, a daily average TPW grid was created and then mapped onto the 1 km x 1 km grid that spans the domain of solar insolation coverage.</span></p> <p><strong><span>Data Quality</span></strong></p> <p><span>For the solar insolation dataset, between 15 and 22 pyranometer stations from across the state of Florida are utilized, with ~30%<span> </span>used for calibration of the satellite-estimated model product and the remaining used for validation of model performance. Every effort was made to screen for data quality, with the highest quality data being reserved for calibration. These data were provided by three State of Florida Water Management District (WMD) weather station networks (South Florida (SF), Saint John’s River (SJR) and Southwest Florida (SWF), the University of Florida (UF) Institute of Food and Agricultural Sciences (IFAS) Florida Automated Weather Network (FAWN), and the United States Geological Survey (USGS) network.</span></p> <p><span>The following uncalibrated and calibrated pyranometer station-averaged statistics were developed for comparison of satellite-estimated and pyranometer-measured daily-integrated insolation at the ten verification pyranometer station locations: mean bias error (MBE), root mean square error (RMSE, and as a percentage of the mean pyranometer-measured value given in parentheses), and coefficient of determination (R2). As an example for 2023, the uncalibrated values are: MBE = –0.59 MJ/m^2/day, RMSE = 1.38 MJ/m^2/day (8%), and R2 = 0.96. The calibrated values are: MBE = 0.19 MJ/m^2/day, RMSE = 1.20 MJ/m^2/day (7%), and R2 = 0.97.</span></p> <p><strong><span>References</span></strong></p> <p><span>Cheng, P., A. Pour-Biazar, R. T. McNider, and J. R. Mecikalski, 2020: Validation of GOES-based surface insolation retrievals and its utility for model evaluation. <em>J. Atmos. Ocean Tech</em>., <strong>37</strong>, 553–571.</span></p> <p><span>Diak, G. R., 2017: Investigations of improvements to an operational GOES-satellite-data-based insolation system using pyranometer data from the U. S. Climate Reference Network (USCRN). <em>Remote Sens. Environ</em>., <strong>195</strong>, 79–95, doi:10.1016/j.rse. 2017.04.002.</span></p> <p><span>Jacobs, J., J. Mecikalski, and S. Paech, 2008: Satellite-based solar radiation, net radiation, and potential and reference evapotranspiration estimates over Florida. Technical Report. July 2008, 138 pp. http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf.</span></p> <p><span>Mecikalski, J. R., W. B. Shoemaker, Q. Wu, M. A. Holmes, S. J. Paech, and D. M. Sumner, 2018: A 20-Year high-resolution GOES insolation–evapotranspiration dataset for water resource management over the State of Florida. <em>J. Irrig. Drain. Eng</em>., <strong>144</strong>(9): 04018025.</span></p> <p><span>Mecikalski, J. R., D. M. Sumner, J. M. Jacobs, C. S. Pathak, S. J. Paech, and E. M. Douglas, 2011: Use of visible Geostationary Operational Meteorological Satellite imagery in mapping reference and potential evapotranspiration over Florida. <em>Evapotranspiration</em>. ISBN 978-953-307-251-7, Editor Leszek Labedzki, Chapter 10, pgs. 229-254.</span></p> <p><span>Paech, S. J., J. R. Mecikalski, D. M. Sumner, C. S. Pathak, Q. Wu, S. Islam, and T. Sangoyomi, 2009: A calibrated, high-resolution GOES satellite solar insolation product for a climatology of Florida evapotranspiration. <em>J. Amer. Water Resources Assoc.</em>, <strong>45</strong>, 1328-1342.</span></p>
Contrail altitude estimation using GOES-16 ABI data and deep learning: Dataset of contrails collocated with CALIOP satellite measurements
Open the record for dataset details and reuse information.
GOES-16 ABI and collocated SNPP-VIIRS imagery for evaluating the emulation of daytime cloud products at night
Open the record for dataset details and reuse information.
GOES-16 simulated BTs over the Southeastern US.
<p>This dataset contains simulated brightness temperatures from WRF V3.9.1.1.1 model output used in "<strong>Examining the Role of the Land Surface On Convection Using High-Resolution Model Forecasts Over the Southeastern United States</strong>", Henderson, Otkin, and Mecikalski, submitted to JGR-Atmospheres, in review.</p>
GOES-16 Derived 1-km resolution Daily Solar Insolation (2023)
<p><strong>Dataset Description</strong></p> <p>This is the University of Alabama in Huntsville (UAH) GOES satellite-derived solar insolation dataset. The dataset and method have been described in previous documents, including Jacobs et al. (2008), Paech et al. (2009), Mecikalski et al. (2011, 2018), Diak (2017) and Cheng et al. (2020). The data are in separate files for Julian days 1-365 (or 366). The dataset extends for the period 1 January 1985 through 31 December 2023.</p> <p>Prior to 2022, the dataset domain was on a 474 x 407 grid at 2 km resolution centered on the State of Florida. From 2022 onward the data domain is on a 1668 x 1668 grid of 1 km resolution that includes several Southeastern U. S. states in addition to Florida. The daily solar insolation is in units of MegaJoules per square meter (MJ/m^2/day), and the data themselves are in compressed ASCII format with the latitude (degrees) and longitude (degrees) of a given point listed. These solar insolation data are used to produce an evapotranspiration (ET) product that covers all watersheds that flow onto the extent of the Floridan aquifer system, which will allow water managers in north Florida to use the dataset when conducting water budget analyses that necessarily cross state borders. The ET data are subsequently used for climatological studies and water resources management.</p> <p>From 1985 to 2021, the solar insolation data was developed using native 1-km resolution GOES-East visible channel data. For each grid point, an averaging of 4 x 4 1-km resolution pixels was done to define a 2 km resolution product. For the 2022 to current datasets, native 500-meter, 10-minute resolution channel 2 (0.64 µm) data are used rom the GOES-16/-18 satellite's Advanced Baseline Imager. For each grid point from 2022 onward, an averaging of 2 x 2 500-meter resolution pixels occurred to define a 1 km resolution product. The GOES data were gathered from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).</p> <p>Prior to 2022, a simple climatological or daily model reanalysis based total precipitable water (TPW) correction was implemented to the solar insolation data. From 2022 onward, the TPW correction for solar insolation is performed using the NESDIS Blended Hydrometeorological Product suite (BLENDHYDRO) “blended total precipitable water” data. These data, as obtained from NOAA CLASS, represents a merging of microwave derived TPW products from multiple polar-orbiting and geostationary satellite sensors including: AMU/MHS onboard the NOAA and MetOp satellite series; SSMIS onboard the DMSP satellite series; ATMS onboard S-NPP and NOAA-20; Sounder onboard the GOES satellite series; and GPS Met onboard Orbview-1, and have a spatial resolution of 16 km at the Equator. From 24 hourly files per day, a daily average TPW grid was created and then mapped onto the 1 km x 1 km grid that spans the domain of solar insolation coverage.</p> <p> </p> <p><strong>Data Quality</strong></p> <p>For the solar insolation dataset, between 15 and 22 pyranometer stations from across the state of Florida are utilized, with ~30% used for calibration of the satellite-estimated model product and the remaining used for validation of model performance. Every effort was made to screen for data quality, with the highest quality data being reserved for calibration. These data were provided by three State of Florida Water Management District (WMD) weather station networks (South Florida (SF), Saint John’s River (SJR) and Southwest Florida (SWF), the University of Florida (UF) Institute of Food and Agricultural Sciences (IFAS) Florida Automated Weather Network (FAWN), and the United States Geological Survey (USGS) network.</p> <p>The following uncalibrated and calibrated pyranometer station-averaged statistics were developed for comparison of satellite-estimated and pyranometer-measured daily-integrated insolation at the ten verification pyranometer station locations: mean bias error (MBE), root mean square error (RMSE, and as a percentage of the mean pyranometer-measured value given in parentheses), and coefficient of determination (R2). As an example for 2023, the uncalibrated values are: MBE = –0.59 MJ/m^2/day, RMSE = 1.38 MJ/m^2/day (8%), and R2 = 0.96. The calibrated values are: MBE = 0.19 MJ/m^2/day, RMSE = 1.20 MJ/m^2/day (7%), and R2 = 0.97.</p> <p> </p> <p><strong>References</strong></p> <p>Cheng, P., A. Pour-Biazar, R. T. McNider, and J. R. Mecikalski, 2020: Validation of GOES-based surface insolation retrievals and its utility for model evaluation. <em>J. Atmos. Ocean Tech</em>., <strong>37</strong>, 553–571.</p> <p>Diak, G. R., 2017: Investigations of improvements to an operational GOES-satellite-data-based insolation system using pyranometer data from the U. S. Climate Reference Network (USCRN). <em>Remote Sens. Environ</em>., <strong>195</strong>, 79–95, doi:10.1016/j.rse. 2017.04.002.</p> <p>Jacobs, J., J. Mecikalski, and S. Paech, 2008: Satellite-based solar radiation, net radiation, and potential and reference evapotranspiration estimates over Florida. Technical Report. July 2008, 138 pp. <a href="http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf">http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf</a>.</p> <p>Mecikalski, J. R., W. B. Shoemaker, Q. Wu, M. A. Holmes, S. J. Paech, and D. M. Sumner, 2018: A 20-Year high-resolution GOES insolation–evapotranspiration dataset for water resource management over the State of Florida. <em>J. Irrig. Drain. Eng</em>., <strong>144</strong>(9): 04018025.</p> <p>Mecikalski, J. R., D. M. Sumner, J. M. Jacobs, C. S. Pathak, S. J. Paech, and E. M. Douglas, 2011: Use of visible Geostationary Operational Meteorological Satellite imagery in mapping reference and potential evapotranspiration over Florida. <em>Evapotranspiration</em>. ISBN 978-953-307-251-7, Editor Leszek Labedzki, Chapter 10, pgs. 229-254.</p> <p>Paech, S. J., J. R. Mecikalski, D. M. Sumner, C. S. Pathak, Q. Wu, S. Islam, and T. Sangoyomi, 2009: A calibrated, high-resolution GOES satellite solar insolation product for a climatology of Florida evapotranspiration. <em>J. Amer. Water Resources Assoc</em>., <strong>45</strong>, 1328-1342.</p>
SatCORPS CERES GEO Edition 4 GOES-16 Northern Hemisphere Version 1.4
CER_GEO_Ed4_GOE16_NH_V01.4 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 16 (GOES-16) over the Northern Hemisphere (NH) Version 1.4 data product. Data was collected using the Advanced Baseline Imager (ABI) on the GOES-16 Platform. Note: Version 1.4 uses the Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis for atmospheric input; Version 1.2 used GMAO's GEOS-5.4.1. No changes have been made to the cloud retrieval algorithm. This data set comprises cloud micro-physical and radiation properties derived hourly from GOES-16 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. The data set is arranged as files for each hour in netCDF-4 format. The observations are at 2 km resolution (at nadir) and are sub-sampled to 6 km (taking every third line and pixel).
SatCORPS CERES GEO Edition 4 GOES-16 Southern Hemisphere Version 1.2
CER_GEO_Ed4_GOE16_SH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 16 (GOES-16) over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Advanced Baseline Imager on the GOES-16 Platform. Note: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm. This data set comprises cloud micro-physical and radiation properties derived hourly from GOES-16 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. The data set is arranged as files for each hour in netCDF-4 format. The observations are at 2 km resolution (at nadir) and are sub-sampled to 6 km (taking every third line and pixel).
GHRSST NOAA/STAR GOES-16 ABI L3C America Region SST v2.70 dataset in GDS2
The ACSPO G16/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G16/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L2P-v2.70. The L3C output files are 1hr granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules per 24hr interval, with a total data volume of 0.2GB/day. Fill values are reported at all invalid pixels, including pixels with 5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). All valid SSTs in L3C are recommended for users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).
SatCORPS CERES GEO Edition 4 GOES-16 Northern Hemisphere Version 1.2
CER_GEO_Ed4_GOE16_NH_V01.2 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 16 (GOES-16) over the Southern Hemisphere (SH) Version 1.2 data product. Data was collected using the Advanced Baseline Imager on the GOES-16 Platform. Note: Version 1.2 is identical to version 1.0. No changes have been made to the retrieval algorithm. This data set comprises cloud micro-physical and radiation properties derived hourly from GOES-16 geostationary satellite imager data using the Langley Research Center (LaRC) SATCORPS algorithms supporting the CERES project. The data set is arranged as files for each hour in netCDF-4 format. The observations are at 2 km resolution (at nadir) and are sub-sampled to 6 km (taking every third line and pixel).
GHRSST NOAA/STAR GOES-16 ABI L2P America Region SST v2.70 dataset in GDS2
GOES-16 (G16) is the first satellite in the US NOAA third generation of Geostationary Operational Environmental Satellites (GOES), a.k.a. GOES-R series (which will also include -S, -T, and -U). G16 was launched on 19 Nov 2016 and initially placed in an interim position at 89.5-deg W, between GOES-East and -West. Upon completion of Cal/Val in Dec 2018, it was moved to its permanent position at 75.2-deg W, and declared NOAA operational GOES-East on 18 Dec 2018. NOAA is responsible for all GOES-R products, including Sea Surface Temperature (SST) from the Advanced Baseline Imager (ABI). The ABI offers vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P Imager, including five narrow bands (centered at 3.9, 8.4, 10.3, 11.2, and 12.3 um) out of 16 that can be used for SST, as well as accurate sensor calibration, image navigation and co-registration, spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping). From altitude 35,800 km, G16/ABI can accurately map SST in a Full Disk (FD) area from 15-135-deg W and 60S-60N, with spatial resolution 2km at nadir (degrading to 15km at view zenith angle, 67-deg) and temporal sampling of 10min (15min prior to 2 Apr 2019). The Level 2 Preprocessed (L2P) SST product is derived at the native sensor resolution using NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO first processes every 10min FD data SSTs are derived from BTs using the ACSPO clear-sky mask (ACSM; Petrenko et al., 2010) and Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Currently, only 4 longwave bands centered at 8.4, 10.3, 11.2, and 12.3 um are used (the 3.9 microns was initially excluded, to minimize possible discontinuities in the diurnal cycle). The regression is tuned against quality controlled in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min FD data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise, compared to each individual 10min image. In the collated L2P, SSTs and BTs are only reported in clear-sky water pixels (defined as ocean, sea, lake or river, and up to 5 km inland) and fill values elsewhere. The L2P is reported in netCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume of 0.6GB/day. In addition to SST, ACSPO files also include sun-sensor geometry, four BTs in ABI bands 11 (8.4um), 13 (10.3um), 14 (11.2um), and 15 (12.3um) and two reflectances in bands 2 and 3 (0.64um and 0.86um; used for cloud identification). The l2p_flags layer includes day/night, land, ice, twilight, and glint flags. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0).Pixel-level earth locations are not reported in the granules, as they remain unchanged from granule to granule. To obtain those, user has a choice of using a flat lat-lon file, or a Python script, both available at ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel. The ACSPO VIIRS L2P product is monitored and validated against in situ data (Xu and Ignatov, 2014) using the Satellite Quality Monitor SQUAM (Dash et al, 2010), and BTs are validated against RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3C product is also available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L3C-v2.70, where gridded L2P SSTs are reported, and BT layers omitted.
GOES-16 cloud-motion wind and ASCAT ocean surface wind data for the article "Evolution of an atmospheric Kármán vortex street from high-resolution satellite winds: Guadalupe Island case study"
<p>This repository contains GOES-16 cloud-motion winds and ASCAT ocean surface winds derived for and analysed in the article "Evolution of an atmospheric Kármán vortex street from high-resolution satellite winds: Guadalupe Island case study".</p> <p> </p> <p><strong>GOES-16 Local Cloud-Motion Vectors</strong></p> <p>Data in two ASCII text files: <em>raw5x5g16b2_2018d129_1437z_2232z_north.txt</em> and <em>raw5x5g16b2_2018d129_1437z_2232z_south.txt</em>, with the former containing data for the upper half and the latter for the lower half of the study domain between ~26<sup>o</sup>N and ~29.5<sup>o</sup>N. Both files include 96 records, each record corresponding to a specific 5-minute time interval between 14:37 UTC and 22:32 UTC on 9 May 2018—9 May is day of year 129. The start and end times are given at the beginning of each record in YYYYDDDHHMM format, where Y is year, D is day of year, H is hour, and M is minute. For example, the first record contains data between 14:37 UTC and 14:42 UTC, as indicated by the start and end times of 20181291437 and 20181291442. Then follows the four column headers LAT LON SPD DIR, corresponding to latitude (degree), longitude (degree), wind speed (m/s), and wind direction (meteorological convention, degree north), respectively—note that no cloud-top height/pressure value was calculated for the wind vectors. Each subsequent line is a single GOES-16 local cloud-motion vector, derived from 5x5-pixel band 2 (0.64 micron visible red band) image templates, which represent an area of ~2.5x2.5 km<sup>2</sup> at the subsatellite point.</p> <p> </p> <p><strong>MODIS–GOES-16 3D Cloud-Motion Vectors</strong></p> <p>Data in two netCDF files: <em>MOD.A2018129.1810-75_ABI_CONUS_band_02_goes16.nc</em> and <em>MYD.A2018129.2120-75_ABI_CONUS_band_02_goes16.nc</em>, which correspond to the MODIS Terra and MODIS Aqua overpasses, respectively. These joint MODIS–GOES-16 wind retrievals were derived using ~8x8 km<sup>2</sup> red band image templates sampled every 2 km. The data files are self-explanatory, but the variables "lat", "lon", "V_3D", and "H_3D" provide the latitude (degree), longitude (degree), the [east-west, north-south] wind components (m/s), and the geometric stereo height (m) for each wind retrieval.</p> <p> </p> <p><strong>ASCAT Ocean Surface Wind Vectors</strong></p> <p>Data in two netCDF files: <em>ascat_20180509_030000_metopa_59945_srv_o_063_ovw_new.nc</em> and <em>ascat_20180509_040000_metopb_29259_srv_o_063_ovw_new.nc</em>, which correspond to the MetOp-A and MetOp-B overpasses, respectively. These ASCAT ocean surface retrievals are stress-equivalent winds at 10 m height, given on a 6.25-km grid. The data files are self-explanatory, but the variables "lat", "lon", "wind_speed", and "wind_dir" provide the latitude (degree), longitude (degree), the wind speed (m/s), and the wind direction (oceanographic convention, degree north) for each wind retrieval. <em>Note that wind direction follows the oceanographic convention and refers to the direction towards which the wind blows (equivalent to meteorological wind direction minus 180<sup>o</sup>)!</em></p>
SatCORPS CERES GEO Edition 4 GOES-16 Southern Hemisphere Version 1.4
CER_GEO_Ed4_GOE16_SH_V01.4 is the Satellite Cloud and Radiation Property retrieval System (SatCORPS) Clouds and the Earth's Radiant Energy System (CERES) Geostationary Satellite (GEO) Edition 4 Geostationary Operational Environmental Satellite 16 (GOES-16) over the Southern Hemisphere (SH) Version 1.4 data product. Data was collected using the Advanced Baseline Imager (ABI) on the GOES-16 Platform.Note: Version 1.4 uses the Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis for atmospheric input; Version 1.2 used GMAO's GEOS-5.4.1. No changes have been made to the cloud retrieval algorithm. This data set comprises cloud micro-physical and radiation properties derived hourly from GOES-16 geostationary satellite imager data using the Langley Research Center (LARC) SATCORPS algorithms supporting the CERES project. The data set is arranged as files for each hour in netCDF-4 format. The observations are at 2 km resolution (at nadir) and are sub-sampled to 6 km (taking every third line and pixel).
ACTIVATE GOES-16 Supplementary Data Products
ACTIVATE_Satellite_Data_1 is the GOES-16 satellite data supporting the ACTIVATE suborbital campaign.ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.
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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.