NCEP Reynolds Optimally Interpolated
Sea Surface Temperature Data Sets


The NCEP Reynolds Optimally Interpolated (OI) Sea Surface Temperature product consists of weekly and monthly global sea surface temperature fields on a 1 degree by 1 degree grid. The analysis uses both in-situ SSTs and satellite derived SSTs from the NOAA Advanced Very High Resolution Radiometer (AVHRR). The satellite derived SSTs are from the Multichannel Sea Surface Temperature products that have been constructed operationally from the five-channel AVHRR by NOAA's Environmental Satellite, Data, and Information Service (NESDIS) since late 1981. This product is available from 1981 to the present, with a one week time lag.

Table of Contents:

1. Dataset Overview:

Dataset Identification:

Reynolds Optimally Interpolated Weekly and Monthly Sea Surface Temperature Data Set

Dataset Introduction:

Multichannel sea-surface temperatures (MCSST) have been computed from AVHRR radiances operational since 1981. In-situ data is derived from both ship and buoys. Both these data sets are optimally interpolated [Reynolds and Smith, 1994] onto a 1 degree by 1 degree grid. The optimally interpolated data is available on both weekly and monthly grids.


To produce a long time series of SST data that can be use for global climate studies.

Summary of Parameters:

Sea Surface Temperature


In order to understand the processes involved in global climate change many different scientific measurements are needed. One of the parameters critical to understanding how the ocean affects climate on a global scale is sea surface temperature (SST). An example of the importance of this measurement for climate studies is their use in the study of the western boundary currents of the world's ocean. The western boundary currents play an important role in the Earth's heat balance. They carry a tremendous amount of heat poleward from low-latitude regions. Because the currents exhibit strong SST gradients, the SST measurements can be used to determine their displacements. Knowledge of the displacements, in turn, allows us to improve our understanding of ocean circulation and heat transport.

2. Investigator(s):

Richard W. Reynolds (INTERNET: Diane C. Stokes (INTERNET: Climate Modeling Branch W/NP24 Environmental Modeling Center National Centers for Environmental Prediction World Weather Building, Room 807 5200 Auth Road Camp Springs, MD 20746 USA Voice: (301) 763-8000 x7580 for Reynolds, x7581 for Stokes FAX: (301) 763-8125 For the satellite data: Robert Evans
University of Miami/Rosentiel School of Marine and Atmospheric Sciences

3. Theory of Measurements:

The history of SST computation from AVHRR radiances is discussed at length by [McClain et al., 1985]. Briefly, radiative transfer theory is used to correct for the effects of the atmosphere on the observations by utilizing "windows" of the electromagnetic spectrum where little or no atmospheric absorption occurs. Channel radiances are transformed (through the use of the Planck function) to units of temperature, then compared to a-priori temperatures measured at the surface. This comparison yields coefficients which, when applied to the global AVHRR data, give estimates of surface temperature which have been nominally accurate 3 degrees. The in-situ data is collected from ships and buoys

4. Equipment:

Sensor/Instrument Description:

Advanced Very High Resolution Radiometer (AVHRR) Buoys, Ships

Collection Environment:

NOAA-Series Satellites, Ships and Buoys


NOAA-7, NOAA-9, NOAA-11, NOAA-14 polar-orbiting satellites

Source/Platform Mission Objectives:

Each of the NOAA polar-orbiting satellites have carried an AVHRR as one of three sensors aboard the spacecraft. AVHRR was designed for multispectral investigations of meteorological, oceanographic, and hydrologic parameters, measuring emitted and reflected radiance in four or five spectral bands, spanning the visible portion of the spectrum to the thermal infrared.

Key Variables:

The sensor measures emitted and reflected radiation from Earth in two visible channels and three infrared channels.

Principles of Operation:

Each AVHRR scan views Earth for 51.282 milliseconds, during which time each channel of the analog data output is digitized. Scans occur at the rate of 6 per second, and the sampling rate of the AVHRR sensors is 39,936 samples per second per channel. During a scan, the detectors view an internal target, cold space, and the external scene. The temperature of the internal target is monitored, and space is assumed to have a black- body temperature of 3K. In this way, a simple two-point linear calibration is done internally (Schwalb, 1978). The nonlinear modification to this calibration is achieved at the time of postprocessing, and takes into account sensor nonlinearities, measurement of internal target temperature, calculation of target radiance, internal reflections and emissions, etc..

Sensor/Instrument Measurement Geometry:

The AVHRR has a cross-track scanning system which use an elliptical beryllium mirror rotating at 360 RPM about an axis parallel to the Earth. The 110.8 cross-track scan equates to a swath width of about 2700 km. This swath width is greater than the 25.3 separation between successive orbital tracks, and provides overlapping coverage. Coverage is global, twice daily, at an instantaneous field of view (IFOV) of ~1.4 milliradians, giving a ground field of view of ~1.1 km at nadir for a nominal altitude of 833 km.

Manufacturer of Sensor/Instrument:

ITT Aerospace



Channels 1 and 2 are calibrated to produce at-satellite radiances using a time dependent correction which accounts for sensor degradation and intercalibrates among the satellites. Channels 4 and 5 are calibrated using a non-linear function based on the internal calibration targets, baseplate temperatures, instrument dependent repsonse curves, and NOAA-provided gains and offsets. Channel 3 is calibrated using the gains and offsets in the GAC data record. The thermal channels are then converted to equivalent brightness temperatures using a lookup table based on the inverse Planck function convoloved with the instrument response.


The instrument is designed to maintain a constant operating temperature for the IR detectors and provide a signal-to-noise ratio (SNR) of 3:1 at 0.5% albedo.

Frequency of Calibration:

The thermal infrared channels are calibrated in flight using a view of a stable blackbody and space as a reference. Channels 1 and 2 have no onboard calibration capabilities, however, they are calibrated before launch.

Other Calibration Information:

In an effort to develop a consistent set of in-flight calibration algorithms for channels 4 and 5, a radiance-based correction procedure was developed to account for the non-linear response characteristics of the detectors. This procedure resulted in a single correction algorithm applicable over the entire range of AVHRR operating temperatures, representing a significant improvement over the use of myriad tables to look up temperature corrections.

5. Data Acquisition Methods:

Full resolution AVHRR data are continuously transmitted and recorded in High Resolution Picture Transmission (HRPT) format. The Global Area Coverage (GAC) data are subsampled to approximately 4 km IFOV, recorded internally, and downlinked daily. The Level-1B data are defined as radiometrically-corrected and calibrated data in physical units at full instrument resolution as acquired. To produce the NOAA GAC Level-1B data, the Level-0 (unprocessed) instrument data are quality controlled, assembled into discrete data sets, and have calibration and Earth location information appended. Data are then stored as full orbits consisting of both ascending (daytime) and descending (nighttime) data.

6. Observations:

Sea Surface Temperature

Data Notes:

Not Applicable

Field Notes:

Ship and Buoy Data are used in conjunction with the NOAA/NESDIS operational satellite sea surface temperature data set to create weekly and monthly optimally interpolated SST fields on a global 1 degree by 1 degree grid. 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 Rosentiel School of Marine and Atmospheric Sciences.

7. Data Organization:


The basic granule is weekly and monthly global 1 degree by 1 degree data.

Data Format:

The data are stored in 2 byte raw integers where the values are in SST*100. Thus to convert to SST need to divide the value by 100.

Sample Data Record:

The following is an example of data output for the monthly data.
MON = 1 DATES = 92 1 1 - 92 1 31 SST (110.5W,10.5S) = 25.45
IMON = 2 DATES = 92 2 1 - 92 2 29 SST (110.5W,10.5S) = 25.81
IMON = 3 DATES = 92 3 1 - 92 3 31 SST (110.5W,10.5S) = 26.41
IMON = 4 DATES = 92 4 1 - 92 4 30 SST (110.5W,10.5S) = 26.92
IMON = 5 DATES = 92 5 1 - 92 5 31 SST (110.5W,10.5S) = 26.82
IMON = 6 DATES = 92 6 1 - 92 6 30 SST (110.5W,10.5S) = 26.31
IMON = 7 DATES = 92 7 1 - 92 7 31 SST (110.5W,10.5S) = 25.43
IMON = 8 DATES = 92 8 1 - 92 8 31 SST (110.5W,10.5S) = 24.78
IMON = 9 DATES = 92 9 1 - 92 9 30 SST (110.5W,10.5S) = 24.42
IMON = 10 DATES = 92 10 1 - 92 10 31 SST (110.5W,10.5S) = 24.40
IMON = 11 DATES = 92 11 1 - 92 11 30 SST (110.5W,10.5S) = 24.37
IMON = 12 DATES = 92 12 1 - 92 12 31 SST (110.5W,10.5S) = 24.43

Data Range:

The data range is greater than -1.8 degrees and less than 35 degrees

Sample Data Record:

Not Availble

Related Datasets:

9. Data Manipulations:


Derivation Techniques and Algorithms:

The AVHRR Level-1B sensor counts in the visible channels (1 and 2) are first converted to Rayleigh-corrected radiances and then to optical depth for use in removing the effects of the atmosphere and viewing and illumination geometry. Channels 3-5 are transformed to units of "brightness temperature", using the Planck black body function and a newly- determined (Brown et al., 1993) correction for sensor calibration non-linearity in the longer-wavelength channels. For the satellite SST the algorithm used is the NOAA/NESDIS operational SST fields.

Data Processing Sequence:

Processing Steps:

The following description of the processing steps is taken directly from the README file provided by Richard Reynolds and Diane Stokes.
"The optimum interpolation (OI) sea surface temperature (SST) analysis is produced weekly on a one-degree grid. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). A description of the OI analysis can be found in Reynolds and Smith (1994). The bias correction improves the large scale accuracy of the OI. Examples of the effect of recent corrections is given by Reynolds (1993). The bias correction does add a small amount of noise in time. Most of the noise can be eliminated by using a 1/4-1/2-1/4 binomial filter in time. We STRONGLY recommend that this filter be applied to the data fields before they are used. An improved method of correcting the biases is being developed. 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). For this period the weeks were defined to be centered on Wednesday. This was done to agree with the definition used for ocean modeling. In the analysis (see Reynolds and Smith, 1994) SSTs were generated in ice covered ocean regions. After the analysis was completed, any gridded SST values less than -1.8C were set to -1.8C. The analyses were archived to only the nearest 0.01C. Because of round off as analyses where moved between different computers, this minimum value may change by up to +/- 0.02C. 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 Rosentiel School of Marine and Atmospheric Sciences. These satellite analyses were produced for weeks centered on Sunday. Thus the OI weekly analyses for 1981-1989 also were centered on Sunday. We hope that this change in the definition of the week will not cause any problems. The weekly OI fields are stored in yearly files. The files have the name* or oi.comp.bias.* where * is the year, for example The 're' indicates reanalysis. (Because the OI fields are binary (IEEE), the FTP server should be in binary mode before the data is transferred."

Processing Changes:



Special Corrections/Adjustments:

Calculated Variables:

Sea Surface Temperature from satellite and in-situ sources.

Graphs and Plots:

Monthly Reynolds SST

10. Errors:

Sources of Error:

One of the greatest limitations in the AVHRR derived SST is the obstruction by clouds in the field of view. Other sources of error include atmospheric gases and emissions as well as water surface characteristics.

Quality Assessment:

11. Notes:

Limitations of the Data:


Known Problems with the Data:

Cloud cover. Periods of high aerosols after major volcanic eruptions such as Mt. Pinatubo.

Usage Guidance:

For more detailed information, see the reference list. Also for more information see the README files under the FTP site. A detailed description of the OI technique may be found under [Reynolds and Smith, 1994]

Any Other Relevant Information about the Study:

12. Application of the Dataset:

Global climate studies, studies of ocean circulation and its interaction with the atmosphere, calculate heat transport in the ocean.

13. Dataset Plans:

Description of Future Plans:

None at the present time except to keep producing data

14. Related Software:

Software Description:

The PO.DAAC is supplying FORTRAN software to read data. It is important to acknowledge though that the software was supplied through Richard Reynolds and Diane Stokes and was retrieved directly from their ftp site. FORTRAN software for subsetting the data is also available via the FTP site. cd /pub/cmb/sst/oisst

Software Access:

15. Data Access:

Contact(s) Name, Address, Telephone and E-mail:

User Services Office
Physical Oceanography Distributed Active Archive Center (PO.DAAC)
Jet Propulsion Laboratory (JPL)
M/S Raytheon-299
4800 Oak Grove Dr.
Pasadena, CA 91109, U.S.A.
Phone: (626) 744-5508
Fax: (626) 744-5506

Procedures for Obtaining Data:

Through an order form format a user specified temporal and spatial subsetting routine is available. The subset is produced and staged for pick-up via ftp. The requester is notified of the completion of the subset and is given the file name(s) and their location via an e-mail message.

Newly processed data (1991) can be accessed via ftp or the web

FTP site:

16. Output Products and Availability:

Tape Products:

The weekly and monthly global 1 degree data is available on 8mm tape as well as via the FTP site

Film Products:

Not Applicable

Other Products:

17. References:

Brown J. W., O. B. Brown, and R. H. Evans, 1993. Calibration of AVHRR Infrared channels: a new approach to non-linear correction, Journal of Geophysical Research, 98 (NC10), 18257-18268.

JPL Physical Oceanography Distributed Active Archive Center (PO.DAAC) Data Availability, Version 1-94, JPL Publication 90-49, rev. 5.

Kidwell, K., 1991. NOAA Polar Orbiter User's Guide. NCDC/NESDIS, National Climatic Data Center, Washington, D.C..

McClain E. P., W. G. Pichel, and C. C. Walton, 1985. Comparative performance of AVHRR based multichannel sea surface termperatures, Journal of Geophysical Research 90, 11587-11601.

McMillin, L. M., and D. S. Crosby, 1984. Theory and validation of the multiple window sea surface temperature technique. Journal of Geophysical Research, 89(C3), 3655- 3661.

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. 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 using optimum interpolation. J. Climate, 7, 929-948.

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.

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.

Stowe, L. L., E. P. McClain, R. Carey, P. Pellegrino, G. G. Gutman, P. Davis, C. Long, and S. Hart, 1991. Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data, Adv. Space Research, 3, 51-54.

18. Glossary of Terms:

Sea Surface Temperature
The temperature of the layer of sea water nearest the atmosphere.

19. List of Acronyms:

AVHRR....Advanced Very High-Resolution Radiometer
EOS....Earth Observing System
FTP....File Transfer Protocol
GAC....Global Area Coverage
HDF....Hierarchical Data Format
JPL....Jet Propulsion Laboratory
MCSST....Multichannel Sea Surface Temperatures
NASA....National Aeronautics and Space Administration
NOAA....National Oceanic and Atmospheric Administration
PO.DAAC....Physical Oceanography Distributed Active Archive Center
SST....Sea Surface Temperature

20. Document Information:

Document Revision Date:

11 August 1999

Document Review Date:

This document is under review.

Document ID:


Document Curator:

Jorge Vazquez

Document URL: