SEVIRI

NOAA Geo-Polar Blended Global Sea Surface Temperature Analysis (Level 4)

jebidiah.jeffery — Thu, 17 Mar 2022 15:24:13 +0000

NOAA Geo-Polar Blended Global Sea Surface Temperature Analysis (Level 4)

The NOAA geo-polar blended SST is a daily 0.05° (~5km) global high resolution satellite-based sea surface temperature (SST) Level-4 analyses generated on an operational basis. This analysis combines SST data from US, Japanese and European geostationary infrared imagers, and low-earth orbiting infrared (U.S. and European) SST data, into a single high-resolution 5-km product. The three flavors of blended SST products are night only; day/night, and diurnal warming.

jebidiah.jeffery Thu, 03/17/2022 - 11:24

The National Oceanic and Atmospheric Administration's (NOAA) office of National Environmental Satellite Data and Services (NESDIS) generates a daily 0.05° (~5km) global high resolution satellite-based sea surface temperature (SST) analyses on an operational basis. This analysis combines SST data from U.S, Japanese and European geostationary infrared imagers, and low-Earth orbiting infrared (U.S. and European) SST data, into a single high-resolution 5-km product - this grid spacing was chosen to allow the resolution to approach the Nyquist sampling criterion for the mid-latitude Rossby radius (~20 km), in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. The input SST data themselves are also processed in-house via the Geo-SST Bayesian and physical retrieval approach (GOES-E/W, Meteosat-10), and, for polar-orbiting and Himawari-8, the Advanced Clear Sky Processor for Oceans (ACSPO).

Algorithms

The analysis employs a rigorous multi-scale optimal interpolation (OI) methodology that approximates the Kalman filter, together with a data-adaptive correlation length scale, to ensure a good balance between detail preservation and noise reduction. The multi-scale OI employs a quad-tree approach avoids the concomitant computation times that result from pursuing a straight OI methodology, while running the analysis at three different correlation scales (coarse, medium, fine) preserves mathematical rigor. The algorithm is fully described in Khellah et al. (2005).

Validation

The product accuracy verified against globally distributed buoys is ~0.02 K, with a robust standard deviation of ~0.25 K. The new analysis has proven a significant success even when compared to other products that purport to have a similar resolution. This analysis forms the basis for other operational environmental products such as coral reef bleaching risk and ocean heat content for tropical cyclone prediction.

Products

Three blended SST analysis products are generated:

Day/night which combines both day and night day;
Night time only which uses only nighttime data;
Diurnally Corrected Analysis which corrects for diurnal fluctuations on the Day/ night Analysis.

The near real-time products are generated operationally at NOAA/OSPO and re-served by CoastWatch for the convenience of a broader number of users. The night only product has been reprocessed (see below) and is available from Sept. 2002 to 2016.

Reprocessing

The analysis has been run for the period 1 Sept. 2002 through 31 Dec. 2016, incorporating reprocessed geostationary and polar-orbiting SSTs. This has been done primarily to furnish a consistent reference baseline for anomaly-based products such as those generated by Coral Reef Watch.

Future enhancements

Forthcoming enhancements include the incorporation of microwave SST products from low-earth orbiting platforms (e.g. GCOM-W1) in order to improve resolution of SST features in areas of persistent cloud and correct for diurnal effects via a turbulence model of upper ocean heating.

Short Names

GEO-POLAR

Temporal Start Date

September 1, 2022

Temporal Coverage

2002 - Present

Product Families

Sea Surface Temperature

Maturi, E., A. Harris, J. Mittaz, J. Sapper, G. Wick, X. Zhu, P. Dash, and P. Koner, 2017: A New High-Resolution Sea Surface Temperature Blended Analysis. Bull. Amer. Meteor. Soc., 98, 1015–1026, https://doi.org/10.1175/BAMS-D-15-00002.1
Ignatov, Alexander, Xinjia Zhou, Boris Petrenko, Xingming Liang, Yury Kihai, Prasanjit Dash, John Stroup, John Sapper, and Paul DiGiacomo. "AVHRR GAC SST Reanalysis Version 1 (RAN1)." Remote Sensing 8, no. 4 (April 9, 2016): 315. doi:10.3390/rs8040315.
Liu, Gang, Scott F. Heron, C. Mark Eakin, Frank E. Muller-Karger, Maria Vega-Rodriguez, Liane S. Guild, Jacqueline L. De La Cour, et al. "Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-Km Global Products from NOAA Coral Reef Watch." Remote Sensing 6, no. 11 (November 20, 2014): 11579-606. doi:10.3390/rs61111579.
Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, Remote Sens. Environ., 116, 140-158, doi:10.1016/j.rse.2010.10.017, 2012.
Reynolds, R.W., and Chelton, D.B., Comparisons of daily sea surface temperature analyses for 2007-08, J. Climate, 23, 3545-3562, 2010.
Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, Statistical Processing of Large Image Sequences, IEEE Trans. Geosci. Rem. Sens., 14, 80-93, 2005
Liu, Gang, Alan E. Strong, and William Skirving. "Remote Sensing of Sea Surface Temperatures during 2002 Barrier Reef Coral Bleaching." Eos, Transactions American Geophysical Union 84, no. 15 (April 15, 2003): 137-41. doi:10.1029/2003EO150001.
Harris, A., and Maturi, E., Assimilation of Satellite Sea Surface Temperature Retrievals. Bull. Amer. Meteor. Soc., 84, 1575-1580, 2003. doi:10.1175/BAMS-84-11-1575 Thiébaux, J., Rogers, E., Wang, W., and Katz, B., A New High-Resolution Blended Real-Time Global Sea Surface Temperature Analysis. Bull. Amer. Meteor. Soc., 84, 645-656, 2003.

Measurements

Sea Surface Temperature - Geostationary

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 4

Spatial Coverage

Global

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

Less than 24 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

5km

Platforms

Instruments

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Data Portal

CoastWatch Near Real-Time Search

Sample Filenames

GPBCW_B2016298_WW00_sst_5km_night.hdf

HTTPS

Top Level:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst_blended/sst5km/

Night (includes access to OSPO near real time and STAR reprocessed Sept. 2002 to 2016)

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst_blended/sst5km/night/

Day + Night:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst_blended/sst5km/daynight/

Diurnal:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst_blended/sst5km/diurnal/

THREDDS

https://www.star.nesdis.noaa.gov/thredds/socd/coastwatch/catalog_coastwatch_sst_blended_ghrsst.html

ERDDAP

https://coastwatch.noaa.gov/erddap/search/index.html?page=1&itemsPerPage=1000&searchFor=blended+SST+Geo-polar

Seviri (MSG) - Geostationary - Level 3

jebidiah.jeffery — Thu, 21 Oct 2021 16:09:05 +0000

Seviri (MSG) - Geostationary - Level 3

jebidiah.jeffery Thu, 10/21/2021 - 12:09

The National Oceanic and Atmospheric Administration's Office of Satellite Data Processing and Distribution are generating operational sea surface temperature (SST) retrievals from the Geostationary Operational Environmental Satellites GOES-East and West. The generation of SSTs began with GOES-8 in 2000 and has continued to be generated through GOES-15. They are situated at longitude 135oW and 75oW, respectively, thus allowing the acquisition of high-temporal-resolution SST retrievals. The algorithm calculates SST by utilizing a fully physical retrieval scheme based on modified total least squares (MTLS, Koner et al., 2015) and a probabilistic (Bayesian) approach for cloud masking (Merchant et al., 2005). A more detailed description can be described in the Algorithms and Bayesian Cloud Mask section below.

The GOES-13 and 15 imagers observe both northern and southern hemisphere sectors every half an hour. These 5-band (0.6, 3.9, 6.7, 10.7, 13.3 µm) images are processed to retrieve SST retrievals at 4-km resolution. The 3.9, 10.7 & 13.3 µm channels are used to determine the SST, while the 0.6, 3.9 and 10.7 µm channels are used to detect cloud contamination. Individual sectors are output as GHRSST Level-2 P SST product files, and retrievals are also remapped, averaged and composited hourly into single-byte per pixel "flat" binary files and posted to a server for user access. The retrievals are available approximately 90 minutes after the nominal epoch of the SST determinations. 3-hour and 24-hour composite files are also made available. CoastWatch Regional Imagery is generated every three hours by combining the 1-hourly SST images for these areas.

The same algorithm approach is used to generate SSTs from Meteosat-11 data (centered at 0¡ longitude), taking a sub-selection of channels from the SEVIRI instrument that corresponds with those of the GOES-Imager. The main differences for the end-user are that the Meteosat SST products are "full-disk", every 15 minutes, and have a resolution at the nadir point of ~3-km, at least for the GHRSST L2P data.

Algorithms

Prior to the implementation of the fully physical retrieval algorithm with GOES-13 and 15, the algorithm retrieval schemes were still based on Radiative Transfer Modeling (RTM), generating skin temperatures rather than bulk temperatures. The form of the prior GOES operational SST equation was:

Short Names

GOES_L3

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Temperature

Koner, P. K., A. Harris, and E. Maturi. "A Physical Deterministic Inverse Method for Operational Satellite Remote Sensing: An Application for Sea Surface Temperature Retrievals." IEEE Transactions on Geoscience and Remote Sensing 53, no. 11 (November 2015): 5872-88. doi:10.1109/TGRS.2015.2424219.
Kurihara, Yukio, Hiroshi Murakami, and Misako Kachi. "Sea Surface Temperature from the New Japanese Geostationary Meteorological Himawari-8 Satellite." Geophysical Research Letters 43, no. 3 (February 16, 2016): 2015GL067159. doi:10.1002/2015GL067159.
Maturi, Eileen, Andy Harris, Jon Mittaz, Chris Merchant, Bob Potash, Wen Meng, and John Sapper. "NOAA's Sea Surface Temperature Products From Operational Geostationary Satellites." Bulletin of the American Meteorological Society 89, no. 12 (December 1, 2008): 1877-88. doi:10.1175/2008BAMS2528.1.
Merchant, Christopher J., and Pierre Le Borgne. "Retrieval of Sea Surface Temperature from Space, Based on Modeling of Infrared Radiative Transfer: Capabilities and Limitations." Journal of Atmospheric and Oceanic Technology 21, no. 11 (November 1, 2004): 1734-46. doi:10.1175/JTECH1667.1.
Merchant, C. J., A. R. Harris, E. Maturi, and S. Maccallum. "Probabilistic Physically Based Cloud Screening of Satellite Infrared Imagery for Operational Sea Surface Temperature Retrieval." Quarterly Journal of the Royal Meteorological Society 131, no. 611 (October 1, 2005): 2735-55. doi:10.1256/qj.05.15.
Merchant, C. J., A. R. Harris, E. Maturi, O. Embury, S. N. MacCallum, J. Mittaz, and C. P. Old. "Sea Surface Temperature Estimation from the Geostationary Operational Environmental Satellite-12 (GOES-12)." Journal of Atmospheric and Oceanic Technology 26, no. 3 (March 1, 2009): 570-81. doi:10.1175/2008JTECHO596.1.
Wick, Gary A., John J. Bates, and Donna J. Scott. "Satellite and Skin-Layer Effects on the Accuracy of Sea Surface Temperature Measurements from the GOES Satellites." Journal of Atmospheric and Oceanic Technology 19, no. 11 (November 1, 2002): 1834-48. doi:10.1175/1520-0426(2002)019<1834%3ASASLEO>2.0.CO%3B2 (2002).
Wu, Xiangqian, W. Paul Menzel, and Gary S. Wade. "Estimation of Sea Surface Temperatures Using GOES-8/9Radiance Measurements." Bulletin of the American Meteorological Society 80, no. 6 (June 1999): 1127-38.