GHRSST L2P NOAA/ACSPO GOES-18/ABI America Region Sea Surface Temperature v2.90 dataset

The G18-ABI-L2P-ACSPO-v2.90 dataset produced by the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) system is used to derive Sea Surface Skin Temperature (SST) from the Advanced Baseline Imager (ABI) onboard the GOES-18 satellite. NOAA’s GOES-18 (aka, GOES-T) was launched on March 1, 2022, replacing GOES-17 as GOES West in January 2023. It is the third satellite in the geostationary GOES–R Series, the Western Hemisphere’s most sophisticated weather-observing and environmental-monitoring system. The Advanced Baseline Imager (ABI) is the primary instrument on the GOES-R Series for imaging Earth’s weather, oceans, and environment. <br><br> G18/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with a spatial resolution of 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P is produced in netCDF4 GDS2 format, with 24 granules per day, and a total data volume 1.5 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing). <br><br> Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script available at Documents tab under How-To section. The ACSPO G18 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is available at https://podaac.jpl.nasa.gov/dataset/G18-ABI-L3C-ACSPO-v2.90.

[
  "026:00"
]

[
  "en-US"
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[
  "https://doi.org/10.3390/rs8010079"
]

[
  "GHRSST",
  "geospatial"
]