Wednesday, September 13, 2023

The PO.DAAC is pleased to announce the public release of the NOAA GHRSST L2P/L3C v2.90 sub-skin SST datasets from the new geostationary satellite, Himawari-9 (H09).

The H09 was launched on 2016-Nov-02 by the Japan Meteorological Agency (JMA), and put on standby until 2022-Dec-13, replacing Himawari-8 (H08) satellite. Both H08 L2P/L3C data are available in PO.DAAC (L2P: https://doi.org/10.5067/GHH08-2PO27; L3C: https://doi.org/10.5067/GHH08-3CO27).

The H09 datasets are produced with the NOAA enterprise Advanced Clear Sky Processor for Ocean (ACSPO) v2.90 SST system, by the NOAA Center for Satellite Applications and Research (STAR). The datasets are formatted in netCDF-4, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Processing Specification version 2.0 (GDS2). Detailed information regarding the GHRSST Level 2P/3C products are available from the GHRSST website and also via PO.DAAC’s GHRSST mission webpage.

The L2P datasets are reported in the instrument native swath projection with 2km/nadir spatial resolution. The L3C datasets represent a gridded (at 0.02-deg equal-angle projection) version of L2P. Both L2P and L3C are reported hourly, 24 granules per day, with a daily volume of 1.8 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. Filenames 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).

ACSPO v2.90 enhancements and updates over the v2.70 (employed with both G16 and H08) include: 

  1. Improved clear-sky mask minimizes false alarms/over-screening in dynamic and coastal areas while reducing cloud leakages (in particular, residual cloud in clear/cloud transition zones);
  2. Significantly simplified NLSST formulation uses fewer ABI bands.
  3. Two thermal front layers added (location of fronts, and intensity in unit of kelvin/km).
  4. Improved Single Sensor Error Statistics (SSES) algorithm. SSES biases and standard deviations are now more representative of the retrieval uncertainties, rather than skin-bulk differences.
  5. Improved ACSPO processor is cleaner and better organized, with some bugs fixed and potential sources of instabilities mitigated.
  6. Last two bits in the l2p_flags are redefined. bit15 is set to 0 for high-quality SSTs (QL=15), and to 1 if QL<5. Bit16 is set to 0 when there is valid water in pixel (ocean, lake, river); and to 1 for (land, ice, or invalid).

Both datasets are described and discoverable via the PO.DAAC dataset information pages, and accessible via the Cloud OPeNDAP and Harmony API services. We recommend users to download the data with the podaac-data-subscriber python tool.

DOI:

H09-AHI-L2P-ACSPO-v2.90 https://doi.org/10.5067/GHH09-2P290

H09-AHI-L3C-ACSPO-v2.90 https://doi.org/10.5067/GHH09-3C290

References:

Gladkova, I., A. Ignatov, A. Semenov (2022). Analysis of ABI bands and regressors in the ACSPO GEO NLSST algorithm, Proc. SPIE, Ocean Sensing and Monitoring XIV; 1211804, https://doi.org/10.1117/12.2620058.

Comments/Questions? Please contact podaac@podaac.jpl.nasa.gov or visit the PO.DAAC on Earthdata Forum.



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