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[ CIDC FTP Data]
[ NCEP's Sea Surface Temperature CIDC Data on FTP]
Data Access
NCEP Sea Surface Temperature
NCEP Sea Surface Temperature Anomalies
NCEP Sea Surface Temperature Climatology
[rule]
Readme Contents
Data Set Overview
Sponsor
Original Archive
Future Updates
The Data
Characteristics
Source
The Files
Format
Name and Directory Information
Companion Software
The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data
Data Access and Contacts
FTP Site
Points of Contact
References
[rule]
Data Set Overview
This data set contains the National Center for Environmental
Prediction (NCEP), formally the National Meteorological Center
(NMC) Optimal Interpolation (OI) monthly mean sea surface
temperature (SST) data (Reynolds and Smith, 1994) which starts
with November 1981 and continues to the present. Also included are
climatological values for each of the twelve months (Reynolds and
Smith, 1995). The temperatures were derived over water areas on a
1x1-degree latitude and longitude world grid. An Optimal
Interpolation analysis was used to combine measurements from
satellite-borne instruments and in situ ship and buoy platforms.
The monthly SST values are presented in two forms: the actual
values, and as differences (anomalies) from the climatological
temperatures for the month. Additional months of data will be made
available from this site as they become available from NCEP. These
SST measurements provide a means of monitoring both the long- and
short-term variability of the dynamic and thermodynamic properties
of the ocean on a global scale. On this site, this relatively new
OI SST data set replaces the original Climate Analysis Center
"blended" SST data set (Reynolds, 1988). The OI analysis, derived
on a 1x1-degree grid, shows greater regional detail than the
"blended" analysis which was derived on 2x2-degree grid.
Sponsor
The collection and distribution of this data set at this
site are being funded by NASA's Earth Science
enterprise. The data are not copyrighted; however, we
request that when you publish data or results using
these data :
Please reference Reynolds and Smith (1994) and
Reynolds and Smith (1995) as appropriate and
thank the Distributed Active Archive Center
(Code 902) at Goddard Space Flight Center,
Greenbelt, MD, 20771, for putting the data in
its present format and distributing them.
Goddard's share in these activities was
sponsored by NASA's Earth Science enterprise.
Original Archive
These data were obtained from the Climate Modeling
Branch of NCEP/NOAA where the data in its original
format is available. Richard W. Reynolds leads the group
responsible for its calculation. The sst anomalies are
easily calculated as (sst-climatological sst). The
anomalies are also available from the Integrated Global
Ocean Services System (IGOSS).
In setting up the sea surface temperature data as part
of the Goddard Interdiscipline Collection (IDC), two
adjustments were made. The original data ran from south
to north. To conform with the other IDC data sets the
temperature data was flipped so that here it now runs
from north to south. The original data also had unreal
temperature data over land which had been put in by
Cressman interpolation. This was done to provide
complete global data for those desiring to do
interpolation. To avoid possible confusion by some
general users a land mask was inserted so that no land
temperatures appear in this IDC data set.
Future Updates
The Goddard DAAC will update this data set as new data
are processed and made available by NCEP.
The Data
Characteristics
* Parameters, Units
Parameter Range Units
monthly Sea Surface -1.8 to
Temperature (SST) 35
-1.8 to Degrees
Climatological SST 33 Celsius
Monthly SST Anomalies -9.5 to
7.6
* Temporal Coverage:
Start End
November 1981 July 1997
* Temporal Resolution:
All gridded values are monthly means
* Spatial Coverage:
Global
* Spatial Resolution:
1-degree x 1-degree
Source
The NCEP Sea Surface Temperature data set is derived
from both in situ (ocean based) measurements as well as
global satellite observations. The in situ data consist
of ship and buoy observations, while the satellite data
are collected from the Advanced Very High Resolution
Radiometer (AVHRR) flown aboard the NOAA-7, NOAA-9,
NOAA-11, and NOAA-14 polar orbiting platforms.
For the more recent period, 1990-present, the in situ
data were obtained from radio messages carried on the
Global Telecommunication System. The satellite
observations were obtained from operational data
produced by the National Environmental Satellite, Data
and Information Service (NESDIS).
During the period 1981-1989, the in situ data were
obtained from the Comprehensive Ocean Atmosphere Data
Set (COADS) for the 1980s. These data (see Slutz, et
al., 1985 , and Woodruff, et al., 1993) consist of
logbook and radio reports. The satellite data were
obtained from analyses of NESDIS data produced at the
University of Miami's Rosenstiel School of Marine and
Atmospheric Sciences.
Nominal orbital parameters for NOAA satellites
NOAA-7 NOAA-9 NOAA-11 NOAA-14
Launch Date 06/23/81 12/12/84 9/24/88 12/30/94
Orbit Sun Synchronous
Nominal Altitude 833 km
Inclination 98.8 degrees
Orbital Period 102 minutes
Equatorial daytime
crossing time at 1430 LST 1420 LST 1340 LST 1330 LST
launch:
Nodal Increment 25.3 degrees
The NOAA-series satellites carry the AVHRR instruments.
The orbital period of about 102 minutes produces 14.1
orbits per day. Because the daily number of orbits is
not an integer, the sub orbital tracks do not repeat
daily, although the local solar time of the satellite's
passage is essentially unchanged for any latitude. The
110.8 degrees cross-track scan equates to a swath of
about 2700 km. This swath width is greater than the 25.3
degrees separation between successive orbital tracks and
provides overlapping coverage (side-lap).
The spectral band widths and Instantaneous Field of View
(IFOV) of the AVHRR instrument are given in the
following table.
Channel Wavelength IFOV
(micrometer) (milliradians)
1 0.58 - 0.68 1.39
2 0.73 - 1.10 1.41
3 3.55 - 3.93 1.51
4 10.3 - 11.3 1.41
5 11.5 - 12.5 1.30
At a nominal altitude of 833 km, the IFOV values result
in a resolution of 1.1 km at the ground for each
measurement at nadir. Over sampling occurs in all the
channels--e.g. in channel 5 there are 1.362 samples per
IFOV. However global coverage is available only in the
Global Area Coverage (GAC) data set. GAC data contains
only one out of three original AVHRR scan lines and
along a scan line every four adjacent samples are
averaged and the fifth sample is skipped. The GAC data
thus has a 4 km resolution. A more detailed,
comprehensive description of the NOAA series satellites
and the AVHRR instrument can be found in the NOAA Polar
Orbiter Data User's Guide (Kidwell, 1991 & 1995).
The Files
Format
File Size 259200 bytes (64800 data values)
Data Format IEEE floating point
Headers none
Trailers none
Delimiters none
Land/water mask Land -99.999
Start 179.5W, 89.5N
Orientation
End 179.5E, 89.5S
Name and Directory Information Naming Convention
The file names for the Sea Surface Temperature data set conform to
the Interdisciplinary Data Collection template
xxxxxxxx.pppppp.llctgrr.[yymm].ddd
which is interpreted as follows:
Substring Function NCEP SST Specific
Indicator meaning
NCEP sea
xxxxxxxx data product ncep_sst surface
designator temperature
products
sst Sea surface
Temperature
Sea Surface
pppppp parameter name anom Temperature
Anomalies
Sea Surface
clim Temperature
Climatology
ll number of 1
levels
c vertical n not
coordinate applicable
temporal
llctgrr t period m monhtly
horizontal
g grid e 1 x 1-degree
resolution
rr spatial go global
coverage
yy year 81 - 96 range of
years
yymm range of
mm month 01 - 12
months
[dd] day not used
bin IEEE 32-bit
ddd data format GrADS
ctl
control file
Note:Indicators in bold are constant. Others refer to
variable values, i.e., ncep_sst.anom.1nmego.9206.bin
Directory Paths to Data Files
/data/inter_disc/surf_temp_press/ncep_sst/sst/yyyy/
/data/inter_disc/surf_temp_press/ncep_sst/anom/yyyy/
/data/inter_disc/surf_temp_press/ncep_sst/clim/yyyy/
where yyyy refers to year.
Companion Software
Several software packages have been made available on the CIDC
CD-ROM set. The Grid Analysis and Display System (GrADS) is an
interactive desktop tool that is currently in use worldwide for
the analysis and display of earth science data. GrADS meta-data
files (.ctl) have been supplied for each of the data sets. A GrADS
gui interface has been created for use with the CIDC data. See the
GrADS document for information on how to use the gui interface.
Decompression software for PC and Macintosh platforms have been
supplied for datasets which are compressed on the CIDC CD-ROM set.
For additional information on the decompression software see the
aareadme file in the directory:
software/decompression/
Sample programs in FORTRAN, C and IDL languages have also been
made available to read these data. You may also acquire this
software by accessing the software/read_cidc_sftwr directory on
each of the CIDC CD-ROMs
The Science
Theoretical Basis of Data
Two primary methods exist for determining sea surface temperature
on regional and global scales. The first involves traditional,
direct measurements of SST using ship-borne instrumentation and
both fixed and drifting buoys. A rather large network of these
observation platforms was set up over the past several decades to
provide near continuous measurements of sea surface
characteristics linked through a global telecommunication system.
However, sufficient coverage of the sea surface temperature is
generally available for in situ observations only from 60 degrees
North to 30 degrees South and even there gaps occur.
The second method uses satellite observations to indirectly infer
SST from radiance measurements in a set of discrete spectral
channels sensitive to emission of electromagnetic energy from
Earth's surface. Assuming constant surface emissivity and
negligible effects from the overlying atmosphere, the amount of
energy received by the satellite sensor will vary with
fluctuations in the surface temperature in accordance with the
Planck function. The modulation of the surface- emitted radiation
by the intervening atmosphere can be minimized by judicious
selection of the spectral channels. For the AVHRR instrument, the
3.5-4.0 micron and 10-12 micron channels (channels 3,4,5) are used
in the retrieval of SST. These channels are located in the
so-called "window" regions of the spectrum in which there is
relatively little absorption and emission of infrared radiation by
the atmosphere (except in humid regions like the tropics where
water vapor in the lower atmosphere plays an increasingly
important role in absorbing the surface-emitted radiation). In
addition to the effects of water vapor, clouds are effectively
opaque to radiation across the infrared spectrum (including the
window regions) and therefore tend to mask radiation emitted by
the surface from the satellite sensor. Finally, small but non
negligible variations in the ocean surface emissivity within the
window channels must be taken into account when attempting to
retrieve reliable estimates of SST.
The basis of satellite retrievals of sea surface temperature
involves developing regression formulas relating SST to a
combination of multiple satellite window measurements. The
coefficients of the channel measurements are originally calculated
using theoretical simulations, but then Reynolds and Smith (1994)
state that
"the satellite SST retrieval algorithms are 'tuned' by
regression against quality controlled drifting buoy data
using the multichannel SST technique of McClain et al.
(1985) and Walton (1988). The tuning is done when a new
satellite becomes operational or when verification with
the buoy data shows increasing errors."
The algorithms are global in nature and are not time dependent .
This tuning procedure has the effect of removing global biases
that exist between the satellite-derived SST and the "ground
truth" SST as measured by buoys. The use of multiple channels in
the regression essentially provides corrections for the effects of
varying atmospheric water vapor and surface emissivity in this
type of retrieval. However, before attempting to retrieve SST with
these algorithms, it must first be determined that no
contamination from clouds exist in the observed radiances. This
screening procedure is reviewed briefly in McClain et al. (1985).
The 4 km resolution GAC data are formed into 2x2 or larger blocks
which are subjected to a number of tests to see if any clear
scenes are present. The tests are based on the assumptions that:
clouds are brighter and colder than the clear ocean; the clear
ocean will be nearly uniform from one scene to its neighbors but
clouds will often not be; and at night the channel 3 (3.7
micrometers) black body temperature will behave differently, when
compared to those from channels 4 & 5, over clear ocean than over
cloudy scenes. There are separate day and night tests. In the open
ocean the GAC data is organized into "targets" with a nominal
spacing of 25 km. These targets are processed one at a time by the
satellite SST algorithm. A target consists of five 11x11 arrays,
one corresponding to each of the five AVHRR channels. If some
clear ocean scenes are identified within the target a single sea
surface temperature value is calculated for the target, otherwise
the target is rejected as cloud covered. Details concerning the
cloud screening and AVHRR SST algorithms are given by McClain et
al. (1985) and Walton (1988).
Processing Sequence and Algorithms
The methodology called optimal interpolation (OI) is used to
produce the sea surface temperatures on a 1x1-degree world grid,
with land areas excluded. Four data measurement sets are collected
and screened for bad data. These are: in situ ship measurements,
in situ buoy measurements, daytime satellite measurements, and
nighttime satellite measurements. There is no reflected solar
radiation at night hence the day and night satellite SST
algorithms differ. After the data preparation, Optimal
Interpolation analysis is used to calculate the final SST values.
Operationally both daily and weekly OI analyses are made. For this
study weekly OI analyses were used. The monthly OI fields are
derived by a linear interpolation of the weekly OI fields to daily
fields then averaging the daily values over a month. The monthly
fields are in the same format and spatial resolution as the weekly
fields. The discussion is broken into four parts:
* data preparation
* Optimal Interpolation (OI) analysis
* the Climatology
* DAAC modifications
Data preparation
Reynolds and Smith (1994) list the preliminary steps.
Before the SST data are used in the OI they must pass
the quality control procedures. These procedures include
the use of programs that track ships and buoys by their
identification codes and eliminate observations with
unlikely position changes. All in situ observations that
pass the tracking tests and all satellite retrievals are
tested for accuracy of the SST. All observations are
discarded if the SST is less than -2-degree C or greater
than 35-degree C or if the SST anomaly lies outside +
-3.5 times the climatological standard deviations. These
tests were designed to eliminate some of the worst
data."
To improve the SST near the ice edge, sea ice information is used.
It became available at the National Center for Environmental
Prediction (NCEP) in real time starting in 1988. The data have a
2-degree spatial and a weekly temporal resolution.
"If a grid box was ice covered (concentration of 50% or
greater), an SST value was generated with a value of
-2-degree C. The freezing point temperature of sea water
with a salinity of 33 to 34 psu is -1.8-degree C. This
range of salinity is typical near the ice edge in the
open ocean. Thus, -2-degree C is slightly too negative.
After the SST field has been computed, any SST gridded
value less than -1.8-degree C is set to -1.8-degree C.
The use of simulated SSTs of -2-degree C over
ice-covered regions allows the analysis to reach its
fixed minimum more robustly."
The OI analysis assumes that the data contains errors but is not
biased. Reynolds et al. (1989) showed that regional, time
dependent biases exist between the satellite and in situ SST, and
that most of the problem lay with the satellite measurements.
Further, daytime and the nighttime satellite measurements had
different biases. These perturbations arise from volcanic aerosol
clouds and other causes (Reynolds, 1993; Reynolds and Smith,
1994). Before the OI analysis, separate adjustments are made in
the satellite daytime and nighttime measurements using the Poisson
technique of Reynolds (1988) and Reynolds and Marsico (1993). The
procedure uses Poisson's equation to form preliminary weekly
blends of the in situ and satellite measurements on a 4x4-degree
grid. The blended results have a resolution of about 12-degrees.
The blended analysis adjusts any large scale satellite biases and
gradients relative to the boundary conditions defined by the in
situ analysis. The blended results are then interpolated to the
original satellite measurement positions to determine regional,
time dependent bias corrections (Reynolds and Smith, 1994).
Optimal Interpolation (OI) Analysis
The OI analysis procedure is discussed in Reynolds and Smith
(1994). The analysis is computed in terms of increments rather
than the actual temperatures so that the first guess is preserved
in regions with little or no data. the OI analysis is now produced
both daily and weekly on a 1- degree grid. Since local conditions
tend to persist for a time, the previous OI analysis is used as a
first guess for the next analysis. This was found to be more
accurate than using climatology as a first guess. To reduce the
number of observations used in the OI, averages over 1-degree
squares are computed. These 'super observations' are computed
independently for each ship and buoy identification code and for
day and night satellite retrievals. Ships normally make only one,
6-hourly report in a given grid box. Thus the chief effect of this
averaging is to reduce the number of buoy and satellite
measurement values used in the analysis. The analysis increment is
defined as the difference between the analysis and the first
guess; the data increment is defined as the difference between the
data and the first guess. The analysis increment, r(k), is given
by
r(k) = sum[w(ik)q(i)]
where q(i )are the data increments and w(ik) are the least square
weights.
The subscript (k) ranges over the grid points where the solution
is required and the subscript (i) ranges over the data points.
When there is little or no data in a region the weights, w(ik),
approach zero. The OI is only optimal when the correlations and
variances needed to calculate the w(ik) are known for the analysis
increment and for each type of data increment. The calculation of
the w(ik) involve matrix inversions which become unstable if too
many data points are involved. This is one reason that averages
over the grid squares are formed before the analysis. The ship
measurements are noisier than the buoy and satellite measurements
and therefore have smaller weight values in the analysis.
Reynolds and Smith 1994 Monthly AOI SST climatology
Reynolds and Smith (1995) also produced an adjusted optimal
interpolation (AOI) climatology for each calendar month on a
1x1-degree grid. The following description is taken from the
abstract of their paper.
Abstract
In response to the development of a new higher resolution sea
surface temperature (SST) analysis at the National Center for
Environmental Prediction (NCEP), a new monthly 1-degree global sea
surface temperature climatology was constructed from two
intermediate climatologies: the 2-degree SST climatology presently
used at NCEP and a 1-degree SST climatology derived from the new
analysis. The 2-degree SST climatology used a 30-year 1950-79 base
period between roughly 40S and 60N based on in situ (ship and
buoy) SST data supplemented by 4 years (1982-85) of satellite SST
retrievals. The 1-degree SST climatology was based on monthly
analyses using in situ SST data, satellite SST retrievals, and
sea-ice coverage data over a 12-year period (1982-93). The final
climatology was combined from these two products so that a
1-degree resolution was maintained and the base period was
adjusted to the 1950-79 period wherever possible (approximately
40S and 60N). Compared to the 2-degree climatology, the 1-degree
climatology resolves equatorial up welling and fronts much better.
This leads to a better matching of the scales of the new analysis
and climatology. In addition, because the magnitudes of
large-scale features are consistently maintained in both the older
2-degree and the new 1-degree climatologies, climate monitoring of
large-scale anomalies will be minimally affected by the analysis
change. The use of 12 years of satellite SST retrievals makes this
new climatology useful for many additional purposes because its
effective resolution actually approaches 1-degree everywhere over
the global ocean and because the mean SST values are more accurate
south of 40S than climatologies without these data.
Reynolds and Smith (1995) point out that this new climatology is
an improvement over both the Climate Analysis Center Climatology
(Reynolds, 1988) and that developed by Shea et al. (1992). A major
advantage is that this climatology has a 1x1-degree resolution
while both of the other two have a 2x2 degree resolution.
Reynolds and Smith also formed an SST anomaly data set by
subtracting climatological SST from the monthly OI analysis SST.
All three data sets ( OI analysis SST, the AOI Climatology, and
the SST monthly anomalies) are included at these site.
Interdisciplinary Data Collection Changes
The Interdisciplinary Data Collection version of the NCEP OI SST
varies in two points from the data set in the IGOSS Products
Bulletin.
* Originally the data progressed from the South Pole northward.
We reversed the data so that it now progresses from North to
South. This was done to match the other data sets in our
collection.
* In the original data set, land grids are filled by Cressman
interpolation (Cressman, 1959). This was done to allow easier
interpolation by those desiring to do so. Some inexperienced
analysts might mistake these interpolated values over the
continents for real temperatures. To prevent this we followed
the ISLSCP (International Satellite Land Surface Climatology
Project) convention and put a mask over the land areas.
Scientific Potential of Data
Njoku et al. (1985) provide a comprehensive description of the
importance and applications of accurate knowledge of the sea
surface temperature over both local and global scales. Some of the
applications are
* quantification of the heat, moisture, and radiative fluxes
between the ocean and atmosphere, which defines the surface
energy balance at the air-sea interface Liu, 1988, Liu, 1990,
Liu and Gautier, 1990)
* initialization and validation of mesoscale and large-scale
general circulation models
* studies of the climatological warming or cooling of the
global oceans (Reynolds et al. 1989)
* understanding of the dynamics of the ocean (e.g., ocean
currents) as influenced by the gradients of sea surface
temperature and forcing by atmospheric winds
* correlation of oceanic primary productivity (phytoplankton
blooms) with SST, especially during transient events or
periodic events such as El Nino
* understanding the importance of oceans as a sink for
atmospheric carbon dioxide and how the relevant mechanisms
are affected by SST (Moore and Bolin 1986).
Validation of Data
As described in Reynolds (1988), the sea surface temperature
monthly mean files are subjected to objective quality controls. As
reported there for the "blended" analysis, the global monthly
average bias error is less than 0.1 degree C. The global monthly
average RMS error is less than 0.8 degree C. However, errors at
individual grid points could be larger. Reynolds and Smith (1994)
show that the higher spatial resolution of the new OI analysis
gives better regional detail than did the "blended" analysis.
Contacts
Points of Contact
For information about or assistance in using any DAAC
data, contact
EOS Distributed Active Archive Center (DAAC)
Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)
Technical inquiries about the NCEP OI SST data set
should be addressed to:
Dr. Richard W. Reynolds
Internet: wd01rr@sgi11.wwb.noaa.gov
301-763-8000 ext 7580(voice)
301-763-8125(fax)
Diane C. Stokes
Internet: wd01dm@sgi26.wwb.noaa.gov
301-763-8000 ext 7581(voice)
301-763-8125(fax)
Climate Modeling Branch W/NP24
NCEP/NWS/NOAA
5200 Auth Road, Room 807
Camp Springs, MD 20746, USA
References
Cressman, G.P. 1959. An operational objective analysis system.
Mon. Wea. Rev., 87:367-374.
Kidwell, K.B.,compiler and editor, July 1991: NOAA Polar Orbitor
Data Users Guide (Tiros-N, NOAA-6,7,8,9,10,11 and -12),
NOAA/NESDIS, National Climate Data Center, Washington, DC.
Kidwell, K.B., editor 1995: NOAA Polar Orbitor (POD) Data Users
Guide NOAA/NESDIS, National Climate Data Center, Washington, DC:
http://www2.ncdc.noaa.gov/POD/
Liu, W.T. 1988. Moisture and latent heat flux variabilities in the
tropical Pacific derived from satellite data. J. Geophys. Res.,
93:6749-6760.
Liu, W.T. 1990. Remote sensing of surface turbulent flux. In
Surface Waves and Fluxes, Vol. II (G.L Geenaert and W.J. Planr,
eds.), Kluwer Academic press, pp. 293-309.
Liu, W.T., and C. Gautier. 1990. Thermal forcing on the tropical
Pacific from satellite data. J. Geophys. Res., 95:13209-13217.
McClain E.P., W.G. Pichel, and C.C. Walton. 1985. Comparative
performance of AVHRR-based multichannel sea surface temperature.
J. Geophys. Res., 90:11585-11601.
Moore, B., and B. Bolin. 1986. The oceans, carbon dioxide, and
global climate change. Oceanus, 29 (4).
Njoku, E.G., T.P. Barnett, R.M. Laurs, and A.C. Vastano. 1985.
Advances in satellite sea surface temperature measurements and
oceanographic applications. J. Geophys. Res., 90:11573- 11586.
Reynolds, R.W. 1988. A real-time global sea surface temperature
analysis. J. Climate, 1:75-86.
Reynolds, R. W., 1993: Impact of Mount Pinatubo aerosols on
satellite-derived Sea Surface Temperatures. J. Climate, 6,
768-774.
Reynolds, R.W., C.K. Folland, and D.E. Parker. 1989. Biases in
satellite-derived sea surface temperature data. Nature,
341:728-731.
Reynolds, R. W. and D. C. Marsico, 1993: An improved real-time
global sea surface temperature analysis. J. Climate, 6, 114-119.
Reynolds, R. W. and T. M. Smith, 1994: Improved global sea surface
temperature analyses. J. Climate, 7, 929-948.
Reynolds, R. W., and T. M. Smith, 1995: A high-resolution global
sea surface temperature climatology, J. Climate, 8, 1571-1583.
Shea, D.J., K.E. Trenberth, and R.W. Reynolds. 1992. A global
monthly sea surface temperature climatology, J. Climate, 5,
987-1001.
Slutz, R. J., S. J. Lubker, J. D. Hiscox, S. D. Woodruff, R. L.
Jenne, D. H. Joseph, P. M. Steuer, J. D. Elms, 1985: Comprehensive
Ocean-Atmosphere Data Set: Release 1. NOAA Environmental Research
Laboratory, Boulder, CO, 268 pp.
Walton, C. C., 1988: Nonlinear Multichannel Algorithms for
Estimating Sea Surface Temperature with AVHRR Satellite Data, J.
Appl. Meteor., 27, 115-124.
Woodruff, S.D., S. J. Lubker, K. Wolter, S.J. Worley, and J.D.
Elms, 1993: Comprehensive Ocean-Atmosphere Data Set (COADS)
Release 1a: 1980-1992. Earth System Monitor, Vol. 4, No. 1,
September 1993, NOAA.
------------------------------------------------------------------------
[NASA] [GSFC] [Goddard DAAC] [cidc site]
NASA Goddard GDAAC CIDC
Last update:Fri Oct 24 12:54:03 EDT 1997
Page Author: H. Lee Kyle -- lkyle@daac.gsfc.nasa.gov
Web Curator: Daniel Ziskin -- ziskin@daac.gsfc.nasa.gov
NASA official: Paul Chan, DAAC Manager -- chan@daac.gsfc.nasa.gov
