Sea Surface Temperature Near Real Time Geostationary
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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. The current operational satellites
are GOES-13 and 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
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-10 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.
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:
where i is GOES-Imager channel number (2, 4, 5),
S = sec(satellite zenith angle) - 1 and
Tiis channel brightness temperature in
The current algorithm is a deterministic physical retrieval scheme based on
modified total least squares (MTLS, Koner et al., 2015). The retrieval
can be expressed as
where x is the vector of parameters to be retrieved, in this case SST
and total column water vapor, i.e. [SST, TCWV]T, y is
a vector of channel brightness temperatures, and subscripts r,
ig and o denote "retrieved", "initial guess", and "observed"
respectively. The initial guess brightness temperatures, yig
are calculated using the Community Radiative Transfer Model (CRTM) with NCEP
atmospheric profile and surface temperature information on a coarse grid
(1¡«1¡). G is the gain matrix, which multiplies the difference between
observed and initial guess brightness temperatures (i.e. Dy) to
obtain the delta-adjustment in retrieved parameters. This approach is common
to all physical retrieval methods – it is the details of the gain matrix
formulation where methods differ. The MTLS method formulates G starting
from a deterministic paradigm, whereby a true value is assumed to exist, and
the inputs have error terms. Starting from the assumption that
Dy = KDx, where K is the matrix (Jacobian) of
partial derivatives of the components of y with respect to the
components of x, the least squares solution (a.k.a. "normal" equation)
Note that K is obtained as part of the CRTM calculations. MTLS modifies
the above equation accordingly
where I is the identity matrix and l is the regularization strength.
The difference between this (and the key aspect of MTLS) and other
regularization-type methods are that l itself is dynamically calculated on a
where k is the condition number of K, g = max(1, ||Dy||),
and s2endis the smallest singular value
of the matrix [K Dy]. Thus s2end is
a pixel-by-pixel based estimate of the noise in the data, while the log of the
condition number accounts for how ill-conditioned the retrieval is, and
g reduces the damping towards the initial guess when the required
increment is large. One final aspect of the MTLS algorithm is that it is
possible to derive pixel-by-pixel estimates of error in the retrieval, and
this information is included in the GHRSST L2P output as the Sensor Specific
Error Statistics (SSES) bias and standard deviation fields.
Bayesian Cloud Mask
The previous cloud detection scheme was based on a series of threshold tests.
The new methodology applies Bayes' theorem to estimate the probability of a
particular pixel being clear of cloud given the satellite-observed brightness
temperatures, a measure of local texture and channel brightness temperatures
calculated for the given location and view angle using NCEP GFS surface and
upper air data and the CRTM fast radiative transfer model. The method is
described in detail in a paper by Merchant et al. (2005).
NOAA/NESDIS STAR/SOCD generates a matchup data base for validation of the
GOES-SST retrieval algorithms. This is important for the maintenance and
improvement of the GOES-SST products.
The global drifting buoys and the TOGA TAO moored buoy array are matched with
GOES-SST retrievals within one hour and 5 km. The buoys used are extracted by
the Climate Prediction Center (CPC), which is one of the National Weather
Service's National Centers for Environmental Prediction (NCEP). The buoys are
quality controlled using the Reynolds Optimum Interpolation Sea Surface
Temperature (OISST) Analysis and NCEP Atmospheric Analysis Fields before being
matched with the GOES-SST retrievals. Matchup files are stored in the NOAA
Satellite Active Archive (SAA) for user access.
An automated validation system has been in place since the inception of the
operational GOES-SST retrievals. This system computes the GOES-SST accuracy
statistics (monthly) from the satellite-buoy matches. The program uses the
match up file as an input to calculate the number of matches, bias, and
standard deviation. The statistical results are continually updated with the
[Please acknowledge "NOAA CoastWatch/OceanWatch" when you use data from our
site and cite the particular dataset DOI as appropriate.]
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.
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):
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.
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,
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.
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,
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.
Wu, Xiangqian, W. Paul Menzel, and Gary S. Wade. "Estimation of Sea Surface
Temperatures Using GOES-8/9 Radiance Measurements."
Bulletin of the American Meteorological Society 80, no. 6 (June
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