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(JPL Publication D-14070)
NOAA/NASA AVHRR Oceans Pathfinder
Sea Surface Temperature Data Set
User's Reference Manual
Version 4.0
April 10, 1998

Jorge Vazquez (JPL/Caltech)
Kelly Perry (JPL/Caltech)
Kay Kilpatrick (RSMAS/University of Miami)

Appendices were provided by:
Katherine Kidwell (NOAA), Robert Evans (University of Miami),
Guillermo Podesta (Univerisity of Miami), Kay Kilpatrick (University of Miami)





1.0 INTRODUCTION

This document describes the NOAA/NASA AVHRR Ocean Pathfinder Sea Surface Temperature (SST) products, their production, quality assurance, archive, and methods of data acquisition. It briefly discusses the Advanced Very-High Resolution Radiometer (AVHRR) instrument from which the data are derived, and the National Oceanic and Atmospheric Administration (NOAA) satellite platforms. Pathfinder SST data are accessed through the Earth Observing System Data and Information System (EOSDIS) Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory (JPL). The document is specifically intended to cover the version 4.1 and version 4.0 data sets. A brief description of earlier algorithm versions 1.0 and 3.0 may be found in the Appendix B. The document is organized with section 2 containing a brief history of the processing of SST from the AVHRR Instrument, section 3 gives an overview of the different products available based on spatial and temporal resolutions, section 4 is a summary of how to access the data and relevant documentation and appendices A-G discuss more detailed information on algorithm development, quality flag assignment, and validation.

2.0 ALGORITHMS AND DATA PROCESSING

2.1 Algorithm Overview

Current retrieval algorithms for sea surface temperature from AVHRR are based largely upon the multi-channel sea surface temperature (MCSST) algorithm (McClain et al., 1985) which may be written as:

SST=1 + 2T4 + (T4 - T5) (1)

where 1 and 2 are constants determined through a least-squares fit to in-situ data, T4, T5, are brightness temperatures as derived from channels 4 and 5 (see table 1) and is a weighting factor based on the knowledge of known absorption coefficients (Emery et al., 1994). In this form the linear model has no correction for water vapor attenuation. A nonlinear SST algorithm (NLSST) was introduced that incorporates an initial estimate of the SST field, where the coefficients are calculated for different water vapor regimes as defined by (T4 - T5) differences. The form of the NLSST algorithm used to derive the Pathfinder SST becomes:

SST=1 + 2T4 + 3 (T4 - T5)*Tsurf + 4 (sec()-1)(T4 - T5) (2)

where the "s" are still coefficients based on a least squares fit to in-situ data and T4, T5 are the brightness temperatures in channels 4 and 5. Theta is the satellite scan angle or the incidence angle of the incoming radiation based on the horizontal plane of the satellite, and Tsurf is a first guess sea surface temperature field; in this case the Reynolds optimally interpolated (OI) sea surface temperature data. A non-linearity in the algorithm arises because of the Tsurf term and the 4 coefficients being calculated over two different (T4 - T5) differences. In version 4.0 and 4.1 of the algorithm the coefficients are calculated for T4 - T5 <= 0.7 and T4 - T5 > 0.7. This form of the algorithm was approved for the reprocessing of the MCSST data by the AVHRR Oceans Science Working group because it tended to lower the overall bias over the widest possible environmental conditions (Evans and Podesta., 1996). A nonlinearity arises from the coefficients being calculated over different water vapor regimes corresponding to (T4 - T5); the V1 algorithm calibration coefficients were calculated yearly for three different water vapor regimes or T4-T5 channel differences.

All the algorithms used the non-linear SST algorithm (NLSST), developed and used operationally by NOAA/NESDIS. Earlier forms of the algorithm such as Version 3.0 also use the nonlinear SST algorithm (equation 2) with calibration coefficients calculated for two different water vapor regimes or T4-T5 channel radiance differences and over 5 month periods centered on each month. The improvement of the version 4.1 data set over previous algorithms such as version 1.0, 3.0 and 4.0 lies in the use of a tree algorithm to calculate the quality flags, thus making the procedure of quality flag assignment more objective. The tree algorithm leads to a quality flag between 0-7 being assigned to a plxel value, with 0 being the lowest quality and 7 being the highest quality. For version 4.1 pixels are defined as best that are assigned a quality flag greater than or equal to 3. In earlier versions pixels were flagged as best which were assigned a quality flag of 3. For more details see http://www.rsmas.miami.edu/~gui/algov4/algoV4doc.html (Evans and Podesta, 1998) or appendix G. Appendix C contains details on the tests used to assign the quality flags for version 4.1. The information is provided by Guillermo Podesta and Katherine Kilpatrick at the University of Miami.

The currently available Version 4.0 Pathfinder data sets cover 1985 to 1995. Version 4.1 data has been processed for 1996. For updates on the status of reprocessing the earlier years with the 4.1 algorithm please see the PODAAC AVHRR Pathfinder homepage http://podaac.jpl.nasa.gov/sst/. Reprocessing of all the 1985-1995 using the Version 4.1 software is planned for the near future.

2.2 Accuracy of AVHRR-derived SSTs

Work is currently underway to determine the accuracy of the data (see Vazquez et al., 1998 or Evans et al., 1996). Current results indicate that the accuracy is regionally dependent and influenced by the water vapor content in the atmosphere. However more work needs to be done to confirm this. Some points of interest from work with previously available AVHRR derived SSTs include:

1) The SST measurement is of the skin temperature, and not the bulk temperature (Schuluesso et al., 1990).
2) Atmospheric water vapor partly affects the retrieval, but no independent water vapor data sets are used in the algorithm (Emery et al, 1994).
3) Most successful uses of past MCSST data concentrated on identifying spatial temperature gradients (Gulf Stream fronts, etc.) rather than absolute temperature values. The present calibrations are designed to provide consistency over the duration of the 5-channel data record.
4) Cloud masking in any `all-pixel' image can be minimized by taking the warmest pixel at a fixed location over all images within one week. The logic is that clouds are "cold", and they move much farther in one week than ocean features.

Your experience with this data is valuable to the NOAA/NASA AVHRR Oceans Pathfinder Project. If you have any questions or comments please contact Jorge Vazquez at podaac@podaac.jpl.nasa.gov as to your experiences using the Oceans Pathfinder data.

Example of Monthly Composite of Sea Surface Temperature from NOAA/NASA AVHRR Oceans Pathfinder SST Data Set



2.3 Processing Flow

The Global Area Coverage (GAC) data are obtained by the algorithm and processing team at the University of Miami. The calibrated data contain the radiances for the 5 channel AVHRR instrument (see table 1). For more details on the instrument design and orbit details see Appendix A.

AVHRR Spectral Bands in microns

Channel Position (µm)
Platform 1 2 3 4 5
NOAA-7,9,11,12,14 0.55-0.68 0.725-1.10 3.55-3.93 10.3-11.3 11.5-12.5


The data are binned from the Global Area Coverage 4km AVHRR resolution to approximately 9km. For more details on the algorithm for version 4.1 see http://www.rsmas.miami.edu/~gui/algov4/algoV4doc.html (Evans and Podesta, 1998). A copy of this document is also included in Appendix G.

The University of Miami provides the processing coefficients based on an analysis of buoy data and a least-squares fit to equation (2). For the version 4.0 and 4.1 data these coefficients are calculated over a 5 month period centered on each month. The University of Miami provides the Pathfinder Team at JPL with processing software to convert the equal-area SST data into the equal-angle products that are distributed through the PODAAC.

3.0 DATA SET DETAILS (Equal-Angle Products)

3.1 Overview

The NOAA/NASA AVHRR Oceans Pathfinder SST data are distributed in several spatial and temporal resolutions. Each data product is produced as both ascending (daytime) and descending (nighttime) images. These products are distributed as daily and monthly files, which are defined as spatially and temporally averaged bins of all temperature retrievals. There are four main products: 1) best_sst 2) all-pixel sst 3) equal-area and 4) the matchup database. The products are available at different spatial resolutions including at 9km, 18km and 54km spatial resolutions. In addition, in the near future, the equal-angle products will also be available as weekly and monthly averaged SST data. Plans are also underway to create an 8-day average that is compatible with the SEAWIFS ocean color data. Details for these products are described in sections 3.2-3.6. The naming cnvention is such that:

3.2 Equal Angle Best SST data

In the equal-angle projection there is an equal number of pixels in both the longitude and latitude directions. As an example the 9km data sets consist of data with 4096 pixels in the East-West direction (longitudinal) and 2048 pixels in the North-South direction (latitudinal) Consequently a better description of the spatial resolution is in terms of pixels/degree of latitude and longitude. For the 9km equal-angle data set, the spatial resolution in pixels/degree of latitude and longitude is 4096/360 or 11.37777. At the equator, where the number of kilometers per degree of latitude and longitude is 111.19 km, this translates to 9.77 km per pixel. Toward the poles the kilometers per pixel are vastly reduced to the point where they do not contain multiple SST retrievals. The product is available in spatial resolutions of approximately 9km, 18km, 54km and temporal resolutions of daily and monthly images. The grid size for the 9km resolution is 4096x2048, the 18km is 2048x1024, and the 54km is 720x360. Thus the resolution in pixels/degrees is 4096/360 (11.38) for the 9km grid size, 2048/360 (5.69) for the 18km grid size, and 2.0 for the 54km grid size. The data is in DN or digital numbers and for conversion to SST needs to be mutiplied by a slope of 0.15 with a y-intercept of -3.0 added. Thus the conversion equation is simply:

This product, after a series of statistical tests, retains only the highest quality pixels. For a definition of these statistical tests in Version 4.1 of the algorithm see Appendix C. . Briefly in version 4.1 quality flags are assigned between 0-7 depending on what series of tests are passed. Because only pixels of quality 3 or better are kept, there is less data than the `all pixel' product discussed in section 3.4. In the best SST product, cloud-associated areas are rejected, as is the far portion of the swath. Values with pixel quality of 2 or less in hte Version 4.1 data are set to 0. Thus for the best-sst equal-angle product auxiliary information includes the number of points that went into the calculation of the SST for that pixel.

As data are produced it is announced on the PODAAC AVHRR SST Pathfinder homepage at http://podaac.jpl.nasa.gov/sst. Product update bulletins are sent to an e-mail distribution list; you can add yourself to this list through the PODAAC homepage http://podaac.jpl.nasa.gov.

The data are available via ftp and through the subsetting routine on http://podaac.jpl.nasa.gov/sst The product is available in the HDF format as raster images (DFR8API). It contains two bands or image planes of data, the first is the pixel or DN value to be converted to SST and the second is the number of points per bin. This data may be accessed through the FTP site at podaac.jpl.nasa.gov in the /pub/sea_surface_temperature/avhrr/pathfinder/ directory. These data may also be subsetted through the www at http://podaac.jpl.nasa.gov/sst/. An example of the homepage subsetting tool is seen in section 4.1. Example read software may be found under:

Equal Angle Best SST data

Data Set: Equal Angle Best Sea Surface Temperature
JPL Product Numbers: 091-Version 4.0 data
095-Version 4.1 data
Image Size: 4026 x 2048 (daily for 9km data)
2048 x 1024 (daily for 18km data)
720 x 360 (daily for 54km)
Data Size: ~16.5 MBytes (~1.2 MBytes compressed) for 9km data
~4.2 MBytes (~406 KBytes compressed) for 18km data
~518 KBytes
Temporal averaging: Daily, monthly
Format: HDF
# Extractable Parameters: 2 Bands
HDF Band 1: Sea Surface Temperature:
Value of retrieval
HDF Band 2: Number of Observations Per Bin:
Number of SST values that were averaged from the 9km, 18km, or 54km bin.
Data Access: Via anonymous ftp: ftp podaac.jpl.nasa.gov/pub
Via subsetting: http://podaac.jpl.nasa.gov/sst
On tape, contact podaac@podaac.jpl.nasa.gov
File names: 1995363h09da_gdm.hdf (example)
1995363h18da_gdm.hdf (example)
1995363h54da_gdm.hdf (example)

The naming convention is such that the SST data contained in 1995363h09da_gdm.hdf is for an ascending pass on day 363 of 1995 at a 9km spatial resolution. The data on the same day for a descending pass would be contained in:

3.4 Equal-Angle All SST

This product contains all pixels regardless of data quality flag so there is no cloud masking. Thus for the Equal-Angle All Pixel SST product the auxiliary information includes information on both the number of observations that went into the average for that bin and the quality flag assigned to that pixel or bin. HDF files contain three bands or image planes of data including the DN value, quality flag assigned to that DN value, and the # of observations that went into that bin. Quality flags are assigned between 0-7 (see Appendix C) based on a series of statistical tests. As before DN values maybe converted to SST by applying a slope and y-intercept such that SST=0.15*DN-3.0. As is the case with the Best SST product, the product is available in spatial resolutions of approximately 9km, 18km, 54km and temporal resolutions of daily and monthly images. The grid size for the 9km resolution is 4096x2048, the 18km is 2048x1024, and the 54km is 720x360. Thus the resolution in pixels/degrees is 4096/360 (11.38) for the 9km grid size, 2048/360 (5.69) for the 18km grid size, and 720./360. 2.0 for the 54km grid size.

Because of the size of the 9km files, this data is only available on tape. The data maybe ordered through podaac@podaac.jpl.nasa.gov, or the www homepage http://podaac.jpl.nasa.gov. The 18km and 54km are available through the ftp site at podaac.jpl.nasa.gov. Example read software may be found under


Equal Angle All SST

Data Set: Equal Angle All Sea Surface Temperature
JPL Product Number: 090 - Version 4.0 data
094 - Version 4.1 data
Image Size: 4026 x 2048 (for nominal 9km data)
2048 x 1024 (for nominal 18km data)
720 x 360 (for nominal 54km data)
Data Size: ~24.7 MB (~5.3 MB compressed), nominal 9km data
~6.3 MB (~1.8MB compressed) for nominal 18km data
Temporal averaging: Daily, monthly
Format: HDF
# Extractable Parameters: 3 Bands
HDF Band 1:
Sea Surface Temperature:
Value of retrieval
HDF Band 2: Pixel Quality:
Flag Value between 0 and 7 as defined in Appendix C.
HDF Band 3: Number of Observations Per Bin:
Number of SST values that were averaged from the nominal 9km bin.
Data Access: Daily and monthly averaged data via anonymous ftp: ftp podaac.jpl.nasa.gov/pub, daily data is only available on tape.
For tapes, contact podaac@podaac.jpl.nasa.gov
File names: 1995363h09da_adm.hdf (example)
1995363h18da_adm.hdf (example)
1995363h54da_adm.hdf (example)


3.5 Matchup Database

A large validation data set called the Pathfinder Matchup Data Base (PFMDB) is also available (Podesta et al., 1997). These are the buoy data used in determining the coefficients. The data set is a compilation of a multi-year, multi-satellite database of approximately co-temporal, co-located in situ sea surface temperatures and AVHRR measurements. AVHRR data were extracted at the times and locations of the in situ observations. The maximum temporal separation between the satellite retrieval and the in situ observation was required to be 30 minutes for the pair to be considered a "match". Spatially, the satellite retrieval and in situ observation were required to be within approximately 10km of each other to be considered a match. The result of this matching process is a series of records which contain both satellite-derived and in situ observations.

Each Pathfinder SST algorithm version number is associated with a specific set of matchups. For example version 4.0 and 4.1 data is associated with the Version 19.0 matchups. Version 3.0 data is associated with Version 18.0 of the matchups and Version 17.0 is associated with Version 1.0 of the algorithm. Each record in the Version 19.0 data set contains 195 fields which include the value of the satellite measured SST at the buoy location and the SST from the matchup buoy.

The PFMDB version 19.0 is organized into several files, by month of the year. The naming convention is such that : g_noa14_v19.0_9603.matchups.jpl contains data from NOAA-14 for March of 1996. It includes quality-controlled in situ SST data from both moored and drifting buoys. The quantities in the matchup database are listed in http://www.rsmas.miami.edu/~gui/v19/matchups.v19.0.html. A hardcopy of this document may also be obtained by contacting podaac@podaac.jpl.nasa.gov.

Version 19.0 of the matchups is available as JPL product 089 from the anonymous FTPs site at podaac.jpl.nasa.gov in

Software to read the data maybe found in the:

The following table shows the sources of in situ SST data included in the PFMDB:

Source of In Situ SST in PFMDB

BUOY TYPE: SOURCE:

Moored Buoys
- U.S. National Data Buoy Center (via NODC)
- Japan Meteorological Agency
- TOGA/TAO Project
NOAA Pacific Marine Environment Laboratory

Drifting Buoys
-NOAA Atlantic Oceanographic and Meteorological
Laboratory Canadian Marine Environmental Data Service
FTP NAMES /data/g_noa9_v19.0_9503.matchups.jpl (example)


Most of the initial in situ data compilation and quality control was done in collaboration with Dr. Charles McClain and his research group at NASA's Goddard Space Flight Center.

3.6 Equal-Area

This product is only available via special request. People interested in this product should contact the JPL PO.DAAC via e-mail (podaac@podaac.jpl.nasa.gov). The equal area product is based on a gridding scheme where the number of bins per longitude is dependent on the latitude. The data set generated for distribution is a nominal 9km equal-area product with 6 different bands or extractable parameters describing the sea surface temperature in a given bin. These are distributed as HDF files, and are approximately 120 MB in size. The equal-area files are also available, upon special request, to users who are familiar with the DSP language. The sum squared SSTs and number of observations per bin are included for proper resampling, should a researcher have special spatial or temporal requirements. Pixel quality and mask bits are determined during processing, and based on a variety of tests. The 6 bands included in an equal-area product are listed in the following table.

Extractable Parameters for an Equal Area Product

BAND PARAMETER DESCRIPTION

1

Bin Number

A unique number assigned to a particular bin based on the equal-area grid. This bin_number then is associated with a specific geographical or latitude, longitude coordinate.

2

# of Observations/Bin

Because the 9 km bins are based on an average of 4 km Level-1B data, this parameter indicates the number of observations that went into the average of each bin.

3

Pixel Quality

A quality flag generated during processing, which indicates the quality of the temperature estimate at each pixel. Values can be between 0 and 7 (Version 4.1) inclusive, depending on a series of statistical tests and comparisons with other sources of data (see Appendix C).

4

Mask Bits

This band contains different image masks that are used, such as cloud or ice masks.

5

Sum SST

For a given 9 km bin this number is the sum of the sea surface temperature values in that bin. This number, along with the number of observations per bin, can then be used to derive the average sst value.

6

Sum Squared SST

For a given 9 km bin this number is the sum of the squared sst values, to be used in computing higher-order statistical moments.


4.0 DATA INFORMATION AND ACCESS

Data may be acquired electronically or on tape, as listed in the tables in section 3. Most data are available via anonymous FTP with the exception of 9km all-pixel data which are available on tape. Tape orders may be placed through http://podaac.jpl.nasa.gov or via email to podaac@podaac.jpl.nasa.gov. All data are available on 8mm UNIX tar tapes as mentioned in section 3.0. Information on the products and their availability is found on the www http://podaac.jpl.nasa.gov/sst/ under AVHRR NEWSThe best_sst at all resolutions and the 18km, and 54km all_plxel data are available via FTP. Data may also be ordered through the EOSDIS Version 0 Information Management System (IMS). It is accessed via http://harp.gsfc.nasa.gov/ims-bin/pub/imswelcome.

4.1 Regional Subsetting and Extraction Using WWW

JPL has developed an on-line regional subsetting capability which is accessible through the NOAA/NASA AVHRR Oceans Pathfinder homepage (http://podaac.jpl.nasa.gov/sst). This capability allows the user to extract regional data from the global data set. The user selects a region using geographic coordinates, the time period and data format HDF, RAW, GIF). The input spawns an automatic subsetting routine at JPL which subsets and stages the data to the ftp site. An e-mail message to the requester provides the location of the extracted data sets. The following figure shows the subsetting page from the Pathfinder homepage. The extraction involves entering the maximum and minimum coordinates in latitude and longitude.


4.2 Downloading the Data Using Anonymous FTP

For data that is available via ftp, connect to podaac.jpl.nasa.gov using ftp, and enter 'anonymous' for a user name. Please use your full e-mail address for a password. At the ftp prompt enter:cd / pub/sea_surface_temperature/avhrr/pathfinder/
There are 11 sub directories. They are browse_v1/, browse_v3, browse_v4, browse_v4.1, data_v1, data_v3, data_v4, data_v4.1, matchups/, software/, and /doc.

document: this directory contains documentation such as the users guide in post script and a readme file.
matchups: this directory contains the matchup database, see section 3.6.
software: this directory contains FORTRAN AND IDL routines for reading the HDF data files.. Use of the IDL routines and package requires that you have IDL version 3.5 or better installed on your system, and use of the FORTRAN and C routines requires that you acquire, compile, and install the HDF library (available via anonymous ftp from ftp.ncsa.uiuc.edu). For more information about HDF or to acquire the HDF library, contact the National Center for Supercomputing Applications (NCSA) at http://hdf.ncsa.uiuc.edu.
data_v1.0....4.1: directories contain the ascending and descending data for the particular version number. Subdirectories are separated by temporal resolution (daily, monthly), spatial resolution (9km, 18km, 54km), satellite pass (ascending, descending), and data type best_sst, and all_pixel.
browse_v1.0...4.1: contains gif browse images of the data
colorbar.gif: a gif file containing the colorbar

Please note that previous versions of the data will be taken offline once version 4.1 time series is complete for a given year.

4.3 Information about processing status

Status bulletins are e-mailed to a list of users. To add yourself to this list please refer to the JPL PO.DAAC homepage; http://podaac.jpl.nasa.gov. Status updates are also provided in the `Whats new' section of the JPL PO.DAAC homepage and on the Pathfinder homepage http://podaac.jpl.nasa.gov/sst.

5.0 READING AND USING DATA SETS

5.1 Conversion of DN to SSTs and pixel coordinate to latitude and longitude

The files are daily images of sea surface temperature data. The HDF data are in the form of raster images, therefore the data are contained in byte arrays. Values range from 0 to a possible maximum of 255. Values of 0 refer to missing data or cloud cover. The format of the file consists of a byte array of dimension 4096 x 2048 for a nominal 9 km spatial resolution data set. The byte or DN values can then be scaled into the appropriate sea surface temperature by using the following y-intercept and slope values.

where SST is in degrees Celsius. For the nominal 18 km data the dimension of the byte array is (2048,1024), while the dimensions are 720x360 for the 54km data. See section 3.0 for data set details.

To convert from pixel coordinate to latitude and longitude, one needs to use the conversion factor degree/pixel. Data are gridded with respect to an origin at (180°, 90°South). The conversion factor is different for the 9km, 18km, and 54km data sets. For the 9km data set the number of degrees per pixel is 360/4096 for 18km it is 360/ 2048, and for 54km it is 360/720 or 0.5 degrees/pixel. Using these values then dx=0.0878906 for the 9km, 0.175 for the 18km and 0.5 for the 54km data sets respectively. The value of dx is then used in the equation to convert from pixel coordinate (for 54km data "i" goes from 1 to 720 and "j" goes from 1 to 360) in the x-y direction to longitude and latitude and the conversion becomes: As example for the 54km resolution the first pixel is centered at (-179.75, 89.75). The conversion then becomes:

where j,i are the centered pixel locations in the x and y direction and (x1,y1) is the latitude, longitude of the first pixel. For 9m, x1=-179.956 and y1=89.956, and for 18km x1=
-179.912 and y1=89.912.

5.2 Read Software

JPL provides a series of read programs for use with the SST data. They are available on the ftp site and in Appendix D. As read programs are developed they will be added to the ftp site.
The programs include:

5.3 Attributes

The following is an example of the metadata or attributes taken from a best SST 54 km HDF file. Each HDF image or file has associated with it the metadata which is contained in a header. This particular file has two bands of data; the number of observations per bin and the best sea surface temperature value, see section 3.2. Mosaic or appropriate software needs to be used to view the metadata. A C program to view attributes is provided via FTP.

Scientific Data Brows-o-rama
Datasets
There are 2 datasets and 30 global attributes in this file
Available datasets
Dataset Sea Surface Temperature has rank 2 with dimensions [720, 360].
The dataset is composed of signed 8-bit integers. It has the following attributes

    Attribute scale_factor has the value : 0.150000
    Attribute scale_factor_err has the value : 0.000000
    Attribute add_offset has the value : -3.000000
    Attribute add_offset_err has the value : 0.000000
    Attribute calibrated_nt has the value : 20
    Attribute Slope has the value : 0.150000
    Attribute Intercept has the value : -3.000000
    Attribute Unit has the value : Degree in Celsius
    Attribute Equation has the value : SST (Celsius) = DN * .15 - 3.0

Dataset Number of Observations per Bin has rank 2 with dimensions [720,360]. The dataset is composed of signed 8-bit integers. It has the following attributes

    Attribute Band Name has the value : Number of Observations per Bin

Global attributes

    Attribute Title has the value : AVHRR Oceans Pathfinder Equal Angle
    Attribute Data Center has the value : NASA/PO.DAAC AVHRR Oceans Pathfinder
    Attribute Station has the value : NOAA/NESDIS
    Attribute Mission has the value : AVHRR Oceans Pathfinder
    Attribute Mission Characteristics has the value : NOAA/NASA AVHRR Oceans Pathfinder
    Attribute Sensor has the value : NOAA polar orbiter data
    Attribute Sensor Characteristics has the value : National Oceanic and
    Atmospheric Administration Polar Orbiter
    Attribute Product name has the value : Equal Angle Map HDF
    Attribute Quality type has the value : Best SST
    Attribute Binning period has the value : DAILY
    Attribute Pass has the value : Ascending
    Attribute Processing control has the value : Algorithm: V4.2 pathfinder
    flagtree 14oct97
    Attribute Data start time has the value : 8/5/1996 00:00:00
    Attribute Data end time has the value : 8/5/1996 23:59:59
    Attribute Data processing time has the value : Tue Feb 10 14:39:24 1998
    Attribute Start year has the value : 1996
    Attribute End year has the value : 1996
    Attribute Start day has the value : 218
    Attribute End day has the value : 218
    Attribute Start millisec has the value : 44804
    Attribute End millisec has the value : 44804
    Attribute Number of rows has the value : 360
    Attribute Number of columns has the value : 720
    Attribute Map projection has the value : Equirectangular projection
    Attribute Parameter name has the value : Sea Surface Temperature
    Attribute Orbit has the value : 8250.000000
    Attribute Maximum Latitude has the value : 89.750000
    Attribute Minimum Latitude has the value : -89.750000
    Attribute Maximum Longitude has the value : 179.750000
    Attribute Minimum Longitude has the value : -179.750000

6.0 REFERENCES

Brown O.B., J.W.Brown and R.Evans, 1985. Calibration of AVHRR infrared observations, J. Geophys.. Res., 90 (C6), 11667-11677.
Brown J. W., O. B. Brown, and R. H. Evans, 1993. Calibration of AVHRR Infrared channels: a new approach to non-linear correction, J. Geophys.. Res., 98 (NC10), 18257-18268.
Emery, W., Y. Yu, and G. Wick, 1994. Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation, J. Geophys. Res., 99 (C3), 5219-5236.
Evans, R. H., Shenoi, S. H., Podesta, G. P., 1994 (in prep), A report on the exploratory analysis of pathfinder sea surface temperature retrieval algorithms, University of Miami.
Evans, R. H., 1995 (in prep), Science Working Group Report.
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 temperatures, J. Geophys.. Res., 90, 11587-11601.
McMillin, L. M., and D. S. Crosby, 1984. Theory and validation of the multiple window sea surface temperature technique, J. Geophys.. Res., 89(C3), 3655-3661.
NOAA Technical report NESDIS 1989: Non linearity corrections of the thermal infrared channels of the Advanced Very High Resolution Radiometer: assessment and recommendations.
Podesta, G.P., S. Shenoi, J.W.Brown, and R.H. Evans, 1995. AVHRR Pathfinder Oceans Matchup Database 1985-1993 (Version 18), draft technical report of the University of Miami Rosenstiel School of Marine and Atmospheric Science, June 8, 33pp.
Reynolds, R.W., 1993. Impact of Mt. Pinatubo aerosols on satellite-derived sea surface temperatures, J. .Climate, 6, 768-774.
Reynolds, R. W. and T. S., Smith, 1994. Improved global sea surface temperature analyses, J. Climate, 7, 929-948.
RSMAS report, 1991, Users manual for DSP data, University of Miami Remote Sensing Group, 300pp.
Schluessel, P., W.J. Emery, H. Grassl and T.Mammen, 1990. On the Skin-Bulk Temperature Difference and its Impact on Satellite Remote Sensing of Sea Surface Temperature, J.Geophys.Res., 95, 13341-13356.
Smith, E., et al., Satellite-Derived Sea Surface Temperature Data Available From the NOAA/NASA Pathfinder Program, http://www.agu.org/eos_elec/95274e.html, © 1996 American Geophysical Union.
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.
Walton, C.,1988. Nonlinear multichannel algorithms for estimating sea surface temperature with AVHRR satellite data, J. Appl. Meteor., 27, 115-124.
Wick, G.A. and W. Emery, 1992. A Comprehensive Comparison between Satellite-Measured Skin and Multichannel Sea Surface Temperature, J. Geophys.. Res., 97(C4), 5569-5595.

7.0 ACKNOWLEDGMENTS

This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. We gratefully acknowledge funding by the Earth Observing System, Data and Information System, NASA Headquarters Code YD, Dr. Martha Maiden, Program Manager. The project is a joint NOAA/NASA program to reprocess a long-time series of sea surface temperature data suitable for global-scale ocean studies.



APPENDIX A

SATELLITE AND INSTRUMENT
(Taken from NOAA-Polar Orbiter User's guide http://perigee.ncdc.noaa.gov/docs/podug/, Kidwell et al., 1997)

AVHRR Instrument Description

The Advanced Very High Resolution Radiometer (AVHRR) represents an improvement over the VHRR sensor flown aboard the ITOS series of operational satellites (the last of which was-NOAA-5). The AVHRR is a cross-track scanning system similar to the VHRR, but features four or five spectral channels, compared to just two for the VHRR. The AVHRR flown aboard TIROS-N, NOAA-6, NOAA-8, and NOAA-10 has four channels, and the AVHRR aboard NOAA-7, NOAA-9, NOAA-11, NOAA-12 and NOAA-13 has five channels. Subsequent satellites in the series will have five. Provision has been made for five channels in the data format for all satellites. Channel 5 contains a repeat of Channel 4 data, when only four different channels are available.

The spectral band widths (in micrometers) of the AVHRR channels for the TIROS-N series and those proposed for the remaining spacecraft are shown in the following Table. In addition, the Instantaneous Field of View (IFOV) in milliradians is included for each channel in the following Table. Spectral band widths (micrometers) of the AVHRR are:

Channel # TIROS-N NOAA-6,-8, -10 NOAA-7,-9,-11, -12,-14 NOAA-13 IFOV
(mr)
1 0.55-0.90 0.58-0.68 0.58-0.68 0.58-0.68 1.39
2 0.725-1.10 0.725-1.10 0.725-1.10 0.725-1.0 1.41
3 3.55-3.93 3.55-3.93 3.55-3.93 3.55-3.93 1.51
4 10.5-11.5 10.5-11.5 10.3-11.3 10.3-11.3 1.41
5 Channel 4
repeated
Channel 4
repeated
11.5-12.5 11.4-12.4 1.30


The IFOV of each channel is approximately 1.4 milliradians leading to a resolution at the satellite subpoint of 1.1 km for a nominal altitude of 833 km. The scanning rate of the AVHRR is 360 scans per minute. The time within each scan line of AVHRR data represents IFOV #1.

The analog data output from the sensors is digitized on board the satellite at a rate of 39,936 samples per second per channel. Each sample step corresponds to an angle of scanner rotation of 0.95 milliradians. At this sampling rate, there are 1.362 samples per IFOV. A total of 2048 samples will be obtained per channel per Earth scan, which will span an angle of +/-55.4 degrees from the nadir (subpoint view).

The IR channels are calibrated in-flight using a view of a stable blackbody and space as a reference. No in-flight visible channel calibration is performed (although the spaceview is available as one reference point). Although these will vary from instrument to instrument, the design goals for the IR channels were an NEdT (Noise Equivalent differential Temperature) of 0.12 K (@ 300 K) and a S/N (signal to noise ratio) of 3:1 @ 0.5% albedo.

Users should be aware that AVHRR Channel 3 data on each TIROS-N series spacecraft have been very noisy due to a spacecraft problem and may be unusable, especially when the satellite is in daylight.

As a result of the design of the AVHRR scanning system, the normal operating mode of the satellite calls for direct transmission to Earth (continuously in real-time) of AVHRR data. This direct transmission is called HRPT, for High Resolution Picture Transmission. In addition to the HRPT mode, about ten minutes of data may be selectively recorded on each of two recorders on board the satellite for later playback. These recorded data are referred to as LAC (Local Area Coverage) data. LAC data may be recorded over any portion of the world as selected by NOAA/NESDIS and played back on the same orbit as recorded or during a subsequent orbit. LAC and HRPT data have identical formats.

The full resolution data is also processed on board the satellite into GAC (Global Area Coverage) data which is recorded only for readout by CDA stations. GAC data contains only one out of three original AVHRR lines and the data volume and resolution are further reduced by averaging every four adjacent samples and skipping the fifth sample along the scan line.

Orbital Information

The TIROS-N series satellites were designed to operate in a near-polar, sun-synchronous orbit The orbital period is about 102 minutes which produces 14.1 orbits per day. Because the number of orbits per day is not an integer, the sub-orbital tracks do not repeat on a daily basis, although the local solar time of the satellite's passage is essentially unchanged for any latitude.

However, the satellite's orbits drift over time (Price 1991). This drift causes a systematic change of illumination conditions and local time of observation which is the major source of non-uniformity in multi-annual satellite time series.

The following table contains the approximate times of the ascending node (northbound Equator crossing) and the descending node (southbound Equator crossing) in Local Solar Time (LST) for the TIROS-N series when the satellites were launched. This table also contains the ascending and descending nodes as of March 1995 for the active satellites.

Ascending and Descending Node Times in LST

Satellite

Ascending Node (Launch)

Descending Node (Launch)

Ascending Node (3/95)

Descending Node (3/95)

TIROS-N 1500 0300 n/a n/a
NOAA-6 1930 0730 n/a n/a
NOAA-7 1430 0230 n/a n/a
NOAA-8 1930 0730 n/a n/a
NOAA-9 1420 0220 2116 0916
NOAA-10 1930 0730 1753 0553
NOAA-11 1330 0130 1723 0523
NOAA-12 1930 0730 0915 0715
NOAA-13 1340 0140 n/
n/a
NOAA-14 1330 0130 1330 0130


The next table summarizes the important dates for the satellites which have already been launched from the TIROS-N series. The date range in this table is at best an approximation. There may be scattered data sets available before or after these dates.

Launch and data available dates for the TIROS-N series satellites.

Satellite
Launch Date Date Range
TIROS-N October 13, 1978 October 19, 1978-January 30, 1980
NOAA-6 June 27, 1979 June 27, 1979-March 5, 1983
July 3, 1984-November 16, 1986
NOAA-B May 29, 1980 Failed to achieve orbit
NOAA-7 June 23, 1981 August 19, 1981-June 7, 1986
NOAA-8 March 28, 1983 June 20, 1983-June 12, 1984
July 1, 1985-October 31, 1985
NOAA-9 December 12, 1984 February 25, 1985-November 7, 1988
NOAA-10 September 17, 1986 November 17, 1986-September 16, 1991
NOAA-11 September 24, 1988 November 8, 1988-April 11, 1995
NOAA-12 May 14, 1991 May 14, 1991-present
NOAA-13 August 9, 1993 August 9, 1993-August 21, 1993
NOAA-14 December 30, 1994 April 11, 1995-present


SSB has available specific orbital reference information regarding each orbit of the polar orbiters. This information consists of the orbit number, longitude of ascending and descending nodes, height of satellite at each node, and date and local time. SSB routinely receives this nodal information from SOCC two or three weeks in advance of the actual orbit.

A user may want to know the sub-orbital track and areal coverage available for a polar orbiter. The following paragraph describes how to make a "spinner" which would show the user this information. A spinner consists of a base map which is overlaid with a piece of clear acetate containing the sub-orbital track of the satellite. The acetate track is rotated over the base map as desired.

To make a spinner, the Polar-Stereographic map of the Northern Hemisphere should be mounted on stiff cardboard or similar material. The sub-orbital track and width of the orbital swath for the TIROS-N series which should be traced onto a piece of clear acetate and overlaid on the base map. The outer solid lines indicate a 15 degree swath (the actual width of an orbital swath is approximately 25 degrees). The area under the 15 degree swath contains good, usable data with little or no distortion at the edges. A small map pin should be inserted through the "x" on the acetate and into the center (North Pole) of the base map. The numbers indicated on the sub-orbital track are the minutes after the ascending node. The user need only rotate the acetate around the map base until the orbital track is over the desired area and read off the ascending node longitude. Or, conversely, if the orbit number and ascending node longitude are known, then the spinner can be rotated to the proper longitude and the orbital coverage will be shown as that area covered by the spinner.

Users now have the option of downloading a self-extracting file XTRCTORB.EXE to their PC's hard drive. This file generates a program, GNRLORB.EXE and associated files which is the equivalent of making the spinner described in this section. By inputting the longitude of the ascending node (which is also available on the same WWW site), GNRLORB will display the subtrack of a nominal TIROS-N series satellite with marks at five minute intervals from the ascending node and the limits of an AVHRR scan on a choice of map bases: 1) rectangular equal spaced projection from 70S to 70N latitude; 2) Northern Hemisphere Polar Stereographic projection; and 3) Southern Hemisphere Polar Stereographic projection. Users may access this software from NOAA/NESDIS' Product Systems Branch (PSB) Home Page which has a URL of: http://psbsgi1.nesdis.noaa.gov:8080/ISB/NAVIGATION/navpage.html. Users should click on the "Graphical Orbit Locator" to initiate the ftp download process. This same site also contains an overview of the NESDIS polar earth location process, polar satellite equator crossing information and clock drift files for polar satellites, as well as links to TBUS information and the Brouwer/Lyddane Software package.

Another excellent source of satellite navigation information is located at the NOAA Satellite Information System (NOAASIS) Internet site which has the following URL: http://140.90.207.25:8080/noaasis.html. Users should click on the "Navigation" button to access TBUS bulletins, equator crossings, orbital elements and two line elements for both GOES and POES satellites. Also included is the navigation summary for the GOES satellites and the Monthly Predict elements for the POES. Further information on NOAASIS is included in Appendix G of the Polar Orbiter Users Guide.



APPENDIX B

COEFFICIENTS AND VALIDATION

Overview of Algorithms

The NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11 and -14 polar orbiting satellites. Daily and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 resolution). Version 4 algorithm data currently exist for 1985-1995. Version 4.1 data exists for 1996 and a quality flag of 0-7 is assigned to the SST pixel value. The highest quality has a value of 7 and the lowest has a vale of 0. Earlier versions (1,3,4.0) of the data have a quality flag between 0-3 assigned to the SST pixel value. For more details on the assignment of the quality flags please see Appendix C.

Previous versions of the Pathfinder algorithm included Version 1 which covers 1987 to mid 1991, and version 3.0 data which coverd 1991 to day 246 of 1994. The data were produced using the non-linear SST algorithm (NLSST), developed and used operationally by NOAA/NESDIS. The V1 algorithm calibration coefficients were calculated for three different water vapor regimes or T4-T5 channel differences. This V1.0 - processed data will continue to be available until they are reprocessed with the Version 4.1 algorithm. Version 3.0 uses the modified nonlinear SST algorithm and calibration coefficients are calculated for only two different water vapor regimes or T4-T5 channel radiance differences. In addition calibration coefficients are calculated over 5 month periods centered on each month, whereas the V 1 coefficients were calculated over approximately 1 year periods. The Version 3.0 provides data with a better fit to the Pathfinder Matchup Data Base, a multi-satellite, multi-year database of AVHRR and high-quality in situ SST match-ups (see section 3.6 for a description of this data). An important point to be made is that if better coefficients become available, especially during periods of high aerosol content, reprocessing may occur.

The difference between V4.0,4.1 data and earlier versions occurs in the filtering tests performed on the matchups and the satellite retrievals. For more details see http://www.rsmas.miami.edu/~gui/v19/matchups.v19.0.html and http://www.rsmas.miami.edu/~gui/algov4/algoV4doc.html. Briefly the version 4.0, 4.1 algorithm uses a tree algorithm to develop the cloud test. Version 4.1 of the algorithm assigns a quality flag between 0-7 depending on specific tests that are passed. A quality flag of 0 indicates the lowest quality and a quality flag of 7 is the highest. In version 4.1 the best_sst fields are defined as pixels which are flagged with a quality of 3 or better.

Computation of SSTs Using Version 3 Coefficients

The algorithm for V3 data is the same as the V1 algorithm with the exception of the lack of the "e" coeffcient (a time bias that is unnecessary when using monthly coefficients). V3.0 of the algorithm includes calculating a set of coefficients over two rather than three (T4-T5 differences) water vapor regimes and the coefficients are calculated over a 5 month period centered on each month. The two regimes are T4-T5 <= 0.7° and T4-T5 > 0.7°.

This algorithm was approved by the Science Working Group because it tended to lower the overall bias over the widest range of environmental conditions (personal communication with Robert Evans, 1996).

Computation of SSTs using the V4 coefficients

The Version 4.0, 4.1 algorithm used is essentially the nonlinear SST (NLSST: Walton, 1988. The difference between Version 4 and earlier Version 3.0 algorithm is in the application of the statistical tests used to assign the quality flags.. For a detailed explanation of these tests see Appendix C. As is the case for Version 3.0 of the algorithm the calibration drift with time has been excluded because the coefficients are calculated over a monthly instead of yearly time scale. The algorithm is also conditioned for two regimes of atmospheric water vapor, and separate regression coefficients are applied. The form of the algorithm is:

Here:

is the zenith angle of the instrument, and
T4 and T5 are the brightness temperatures from AVHRR channels 4 and 5, respectively
Tsurf is an a-priori estimate of the SST.

T4 and T5 are determined using the procedure outlined in NOAA Technical Report NESDIS 69. Tsurf is an a-priori estimate of the SST. It is calculated after a spatial interpolation to the nominal 9 km grid of the weekly, 1-degree optimum interpolated SST analysis produced by Dr. Richard Reynolds of NOAA/NESDIS (Reynolds and Smith, 1994). The spatial interpolation used is a bilinear interpolation of the 4 closest neighboring points surrounding the nominal 9 km grid point. The empirical coefficients a, b, c, d were determined through a multiple-regression of AVHRR radiances with the in-situ data from the matchup database. The version 4.1 coefficients are calculated for two different T4 - T5 regimes corresponding to two water vapor regimes. The coffiicients are calculated over 5 month periods centered on each month. This differs from version 1.0 of the algoithm where the coefficients were calculated yearly over three different T4 - T5 regimes. For a listing of the version 4.1 coefficients see: http://www.rsmas.miami.edu/~gui/algov4/algoV4doc.html.



APPENDIX C

Assignment of Quality Flags-Information
by Guillermo Podesta and Katherine Kilpatrick at the University of Miami

Pixel-by-Pixel Science Quality Flags
One of the main goals of the Pathfinder AVHRR Oceans project is to produce global SST fields of a quality as good as possible. Nevertheless, raw data availability and processing errors (cloud flagging, SST algorithm) may result in SST estimates known to be suspect. The next step in the processing is to perform a series of tests to asess the likelihood that a pixel contains an SST value of suspect quality. The various tests are then combined to define eight overall quality levels for a pixel. Finally, the overall pixel quality levels are taken into account during the spatial binning stage (details below); the outcome is an overall bin quality level. The various steps involved are described in subsequent paragraphs.

First, a series of SST quality tests are applied on a pixel-by-pixel basis. The outcome of each individual test is separately stored in a bit contained within two 8-bit variables called maskl and mask2. In both variables, each bit is independently set to 1 if a given test fails. That is, the flag is set (bit value = 1 ) for pixels that fail the test. The quality flags associated with each bit in the mask variables are described below.

MASK 1
Bit-1 Brightness temperature test. Brightness temperatures for AVHRR channels 3, 4 and 5 must be greater than, or equal to -10"C and less than or equal to 35"C. This test is identifies sensor digitizer errors or very cold pixels associated with high cloud tops.
Bit-2 Cloud test . Pixel must pass a suite of cloud flagging tests, arranged as a decision tree and defined for the given satellite and year (Figure 2). The cloud-flagging decision trees are discussed in detail in the description of the Pathfinder matchups [PUT REFERENCE TO MATCHUPS DOC].
Bit-3 Unused. Always set to O. Reserved for future development.
Bit-4 Unused. Always set to O. Reserved for future development.
Bit-5 Uniformity test 1. Maximum and minimum brightness temperature values are calculated for channels 4 and 5, for a 3x3 box centered around the pixel being classified. The difference between maximum and minimum brigthness temperatures for both channels must be less than 0.7°C. This test seeks to identify contamination by small clouds, and is based on the assumption that SSTs are relatively uniform at small scales (e.g., 3x3 pixels). The 0.7°C threshold was selected by testing different threshold values in the matchup database. For uniformity thresholds below 0.7°C, no significant bias was detected in SST estimates, and the rms of SST residuals was relatively uniform.
Bit-6 Uniformity test 2. This test was similar to that described for bit 5, but the treshold was set as 1.2°C. That is, differences between maximum and minimum brightnmess temperatures must be less than 1.2°C to pass this test. A higher uniformity threshold allows more pixels to pass the test, at the expense of accepting pixels with a higher SST bias.
Bit-7 Zenith angle test 1. Satellite zenith angle must be less than 45 degrees to pass this test. At higher zenith angles, radiation emitted by the ocean has to go through a longer atmospheric path before reaching the AVHRR instrument, with consequently higher chances of being attenuated. The received radiance, therefore, is likely to have a lower proportion of radiance originating from the ocean's surface (the signal of interest) and a greater proportion of radiance re- emitted by the atmosphere. The negative side of limiting zenith angles is the loss in geographic coverage.
Bit-8 Reference test. The absolute difference between the Pathfinder SST for the pixel considered and the reference Reynolds SST field (see discussion above) must be less or equal to 2°C.


MASK 2
Bit-1 Zenith angle test 2. Satellite zenith angle must be less than 55 degrees. This is similar to the test in bit 7 of variable maskl, but it allows a larger range of acceptable zenith angle values, with the goal of gaining geographic coverage.
Bit-2 Stray sunlight test . An examination of data stratified by satellite zenith angle and by side of the AVHRR scan line (left and right of nadir) revealed potential problems under certain Earth-Sun-satellite configurations. This flag identifies configurations in which the problem may potentially occur. The problem is probably associated with stray solar radiation entering the radiometer and it occurs only in the middle to high latitudes in the Southern Hemisphere. For that reason, in the Northern Hemisphere this flag is always set to O (pass). In the Southern Hemisphere, the flag is set to 1 (fail) when (a) the satellite zenith angle is greater than 45 degrees, and (b) the pixel is located on the Sun side of the AVHRR scan line. For an ascending pass (spacecraft flying from south to north), the Sun side of the scan line is located left of nadir; for a descending pass, the Sun side of the scan line is right of nadir. The latitude in the Southern Hemisphere at which the stray sunlight becomes a problem is a function of season. During the austral summer, this problem may potentially reach the mid-latitudes, whereas in austral winter, it is confined to very high latitudes. For speed of processing, we have disregarded the seasonality of the latitude dependence, which may result in "good" pixels being erroneously flagged as failing this test. As this test is later used to define overall quality levels (see below), mid-latitude Southern Hemisphere pixels at high scan angles have the potential of being assigned to the lowest quality level during austral winter.
Bit 3 Unused. Always set to O. Reserved for future development.
Bit-3 Unused. Always set to O. Reserved for future development.
Bit-4 SST test To pass test, the estimated Pathfinder SST must be within geophysically reasonable boundaries: 2°C Pathfinder SST 35°C.
Bit-5 Unused. Always set to O. Reserved for future development.
Bit-6 Ascending/descending test. Result is set to 0 for descending (nightime) AVHRR passes; set to 1 for ascending (daytime) passes.
Bit-7 Edge test. Pixels must not be on the first or last scan lines of a piece, or on the first or last pixels in a scan line. Pixels along edges are not surrounded by pixels so that tests based on 3x3 boxes can be performed. Important: if this test is failed (i.e., if pixel is on an edge), bit values for all other tests (in maskl and mask2) are set to 1. Also, the number of lines or pixels along edges rejected can be adjusted if the size of the homogeneity box is changed: for instance, if a 5x5 box is adopted, the edge test will reject the first and last two pixels in a scan line.
Bit-8 Glint test Glint index must be < 0 005 sr-1 The glint index is computed using the Cox and Munk (1954) formulation, assuming a nominal surface wind speed of 6 m s-1. A value greater than 0.005 sr-1 generally indicates significant presence of sunglint.



Overall Quality Levels of Global SST Fields

The outcomes of the individual quality tests described above are subsequently combined into an overall quality level for each pixel. There are eight possible overall quality levels (levels O to 7) to which a pixel may be assigned. A quality level of 0 indicates very bad SST data, while level 7 is the highest quality.

Pixels of the poorest quality (level 0) are identified through a few initial tests likely to identify potential gross SST errors. These initial tests are illustrated in Figure 1. For brevity, a short name (listed in the previous section) is given to each test; the location of the test result in the appropriate mask variable is indicated tin parentheses) as "MXBY", where X is 1 or 2, indicating whether test result is in maskl or mask2, and Y is the bit number (1-8) in the corresponding mask variable. Whether a test is passed or failed is noted respectively. A pixel is automatically assigned to the lowest quality tO) if any of the following four quality mask bits are set to 1 (i.e., if tests are failed):

1. Brightness temperature test (maskl, bitl);
2. Uniformity test 2 (mask 1, bit 6);
3. Zenith angle test 2 (mask 2, bit 1);
4. Stray sunshine test (mask 2, bit 2).

The seven remaining possible quality levels are assigned by evaluating various combinations of the bits in variables maskl and mask2. These combinations are illustrated in Figure 2. Test names and location of test outcomes are given as in Figure 1.

We stress that overall quality levels are provided only as guidance to users, and that they are not associated with any specific error levels in SST estimates. Further, the quality scale is arbitrary and it does not involve any proportionality (e.g., pixels with quality level 4 are not twice as bad as those with quality level 2).

Once overall quality levels are defined for all pixels in a processing piece, the next step is to combine these vaiues into a bin quality level when the pixels spatially binned. This step actually takes place during the spatial binning stage, described in detail below. For the sake of conceptual continuity, however, we discuss here how the quality level is set for a bin.

Suppose pixels in a given piece are being binned into the Pathfinder equal- area 9-km grid (described below). More than one GAC pixel can be assigned to the same bin. Which pixels are included in the binning, however, is a function of the overall quality levels for all candidate pixels. Only pixels of the highest available quality are aggregated into a bin value; pixels of lower quality are not included during the binning. This is best illustrated with an example. Suppose three pixels could be assigned to bin N; two of these pixels have a quality of 3, and the remaining pixels has a (higher) quality level 5. In this case, only the pixel with quality 5 is binned and the two quality 3 pixels are discarded. That is, the binning procedure considers only the "best" data available for a given bin. Users of binned data may select what SST quality levels they may wish to consider in their specific application. For instance, if quantitative analyses are being performed on SST values (e.g., for climate studies), users will probably want to use only the best quality SST estimates. On the other hand, if the goal is to monitor patterns (e.g., frontal features), users may be willing to accept lower quality levels, trading off SST quality for a more complete coverage.

FIGURE 1:
Click here for Picture

FIGURE 2:
Click here for Picture



APPENDIX D

READ SOFTWARE

These programs are available under the FTP site podaac.jpl.nasa.gov in the /pub/sea_surface_temperature/avhrr/pathfinder/software/.

Read Software for HDF Images
The HDF image files can be read using the following sample program written in IDL. This program is available under the FTP site podaac.jpl.nasa.gov using the anonymous login.

; READ_HDF written by K. L. Perry, 8/96
PROGRAM: read_pfsst_data.pro
;
;       An IDL program to read the Pathfinder SST
;       data which is given in the form of 8-bit
;       raster images.
;
;IMPORTANT VARIABLES:
;
;       For the "BEST" SST, there are two datasets
;       orig_sst = Sea Surface Temperature
;       flag_data = Flag and Number of Observations
;
;       For the "ALL" SST, there are three datasets
;       orig_sst = Sea Surface Temperature
;       pix_qual = Pixel Quality
;       flag_data = Flag and Number of Observations
Kelly Perry, 8/96
;==========================================================
;***>The name of the input file must be entered by the user


        filename='1993200h09da-adm.hdf'

; OPEN THE HDF FILE

        file=HDF_OPEN(filename)

; FIND THE NUMBER OF IMAGES AVAILABLE IN THE HDF FILE

        nimg=hdf_dfr8_nimages(filename)

; READ THE DATA IN EACH IMAGE
; (PLEASE NOTE: There are three images for "All" SST and
; two images for "Best" SST)

        if (nimg eq 3) then begin
                hdf_dfr8_restart
                hdf_dfr8_getimage,filename,orig_sst,orig_pal
                hdf_dfr8_getimage,filename,pix_qual,pix_pal
                hdf_dfr8_getimage,filename,flag_data,flag_pal

        endif else begin
                hdf_dfr8_restart
                hdf_dfr8_getimage,filename,orig_sst,orig_pal
                hdf_dfr8_getimage,filename,flag_data,flag_pal
        endelse

; MULTIPLY THE SST DIGITAL NUMBER BY THE CALIBRATION NUMBER (0.15)
; AND THEN ADD THE OFFSET (-3.0) TO GET DEGREES CELSIUS

        orig_sst=0.15*orig_sst-3.0

        HDF_CLOSE,file
        end


C FORTRAN Program to Read HDF Data
 CWritten by K.L. Perry, A.V. Tran, R.M. Sumagaysay
C read_pfsst_data: a FORTRAN program to read the Pathfinder
C                  SST data which is given in the
C                  form of 8-bit raster images.
C
C IMPORTANT VARIABLES:
C
C For the "BEST" SST, there are two datasets
C       orig_sst = Sea Surface Temperature
C       flag_data = Flag and Number of Observations
C
C For the "ALL" SST, there are three datasets
C       orig_sst = Sea Surface Temperature
C       pix_qual = Pixel Quality
C       flag_data = Flag and Number of Observations
C
C NOTE: The user must enter the following into this program:
C
C       1. the number of datasets
C          nds=3 if input file is "best" sst
C          nds=2 if input file is "all" sst
C
C       2. the array sizes
C          for ~9km resolution, x_length=4096, y_length=2048
C          for ~18km resolution, x_length=2048, y_length=1024
C          for ~56km resolution, x_length=720, y_length=360
C
C 3.    the input filename
C
C
C 8/96 K.L. Perry, A.V. Tran, R.M. Sumagaysay
C
C==============================================================
C NOTE: THE HDF LIBRARY MUST BE INSTALLED IN ORDER TO
C RUN THIS PROGRAM
C SET THE PARAMETERS
C***>User must input the below parameters

        integer x_length,y_length
        parameter(x_length = 4096)
        parameter(y_length = 2048)
        integer nds
        parameter(nds = 3)
C       = number of datasets

C IDENTIFY THE VARIABLES

        integer retn
        byte in_data(x_length,y_length)
        integer temp(x_length,y_length)
        real orig_sst(x_length,y_length)
        real pix_qual(x_length,y_length)
        real flag_data(x_length,y_length)
        real cal,offset

C READ THE RASTER IMAGE DATA SETS
C NOTE: THERE SHOULD BE TWO DATA SETS FOR PATHFINDER "BEST"
C SST, AND THREE DATA SETS FOR PATHFINDER "ALL" SST

        cal= .15
        offset = 3.0

        do 500 n=1,nds

C***>The name of the input file must be entered by the user

           retn=d8gimg('93200h09da-adm.hdf',in_data,x_length,y_length,0)

           do 200 i=1,x_length
              do 100 j=1,y_length

C CONVERT FROM BYTE TO INTEGER (ie, add 256 if in_data < 0)

                 if (in_data(i,j).lt.0) then
                    temp(i,j)=in_data(i,j)+256
                 else
                    temp(i,j)=in_data(i,j)
                 endif

C MULTIPLY THE PATHFINDER DIGITAL NUMBER BY THE CALIBRATION NUMBER (0.15)

C AND ADD THE OFFSET (-3.0) TO GET DEGREES CELSIUS

                 if (nds.eq.3) then
                    if (n.eq.1) then
                       orig_sst(i,j)=(cal*temp(i,j))-offset
                    endif

                    if (n.eq.2) then
                       pix_qual(i,j)=temp(i,j)
                    endif

                    if (n.eq.3) then
                       flag_data(i,j)=temp(i,j)
                    endif

                 else
                    if (n.eq.1) then
                       orig_sst(i,j)=(cal*temp(i,j))-offset
                    endif

                    if (n.eq.2) then
                       flag_data(i,j)=temp(i,j)
                    endif

                 endif

C CODE FOR THE OUTPUT SHOULD BE WRITTEN BY THE USER
C AND INSERTED BELOW THIS COMMENT STATEMENT
C
C AS AN EXAMPLE OF WRITING THE DATA TO A NON-HDF FORMAT,
C CODE WHICH PRINTS THE DATA TO THE SCREEN IS SHOWN.
C NOTE, HOWEVER, THAT THE USER IS HIGHLY DISCOURAGED FROM
C WRITING AN ENTIRE DATA SET TO THE SCREEN (OR TO AN
C ASCII FILE) DUE TO THE SIZE OF THE INPUT.
c       if (nds.eq.3) then
c          if (n.eq.1) then
c             write(*,*) i,j,orig_sst(i,j)
c          endif
c
c          if (n.eq.2) then
c             write(*,*) i,j,pix_qual(i,j)
c          endif
c
c          if (n.eq.3) then
c             write(*,*) i,j,flag_data(i,j)
c          endif
c       else
c          if (n.eq.1) then
c             write(*,*) i,j,orig_sst(i,j)
c          endif
c
c          if (n.eq.2) then
c             write(*,*) i,j,flag_data(i,j)
c          endif
c       endif
100           continue
200        continue
500   continue
stop


/*C Program to Read HDF
  Written by 96 K.L. Perry, R.M. Sumagaysay, A.V. Tran 
============================================================= 
read_pfsst_data: a C program to read the Pathfinder SST
                 data which is given in the form of
                 8-bit raster images.

TO RUN THIS PROGRAM USE THE FOLLOWING COMMAND: 
read_pfsst_data <infile>
   where: infile = the HDF input file

IMPORTANT VARIABLES:
   For "BEST" SST's, there are two datasets:
      1)orig_sst = Sea Surface Temperature
      2)flag_data = Flag and Number of Observations

   For "ALL" SST's, there are three datasets:
      1)orig_sst = Sea Surface Temperature
      2)pix_qual = Pixel Quality
      3)flag_data = Flag and Number of Observations

**************************************************************
NOTE: The user must input the correct value of X_LENGTH and
      Y_LENGTH below. 
For ~9km resolution, X_LENGTH = 4096 and Y_LENGTH = 2048. 
For ~18km resolution, X_LENGTH = 2048 and Y_LENGTH = 1024. 
For ~54km resolution, X_LENGTH = 720 and Y_LENGTH = 360.
**************************************************************

9/96 K.L. Perry, R.M. Sumagaysay, A.V. Tran
===============================================================*/

/* --------------------------------------------------- */
/* NOTE: THE HDF LIBRARY MUST BE INSTALLED IN ORDER TO */
/* RUN THIS PROGRAM */ 
/* --------------------------------------------------- */

#include <stdio.h>
#include <hdf.h> 

/* INPUT CORRECT X_LENGTH AND Y_LENGTH HERE */
#define X_LENGTH 4096
#define Y_LENGTH 2048

int main(int argc,char *argv[])
{
   int32 fid;
   int32 status,nsds,ngattr;

   int32 sds_id;
   int32 nt,nattrs,rank = 2;
   int32 dimsizes[50];
   char name[512];
   typedef char int8;
   int8 in_data[Y_LENGTH][X_LENGTH];
   int32 i,j,x,y;
   intn retn;
   float64 cal,cal_err,off,off_err;
   int32 *num_type;
   float orig_sst[Y_LENGTH][X_LENGTH];
   float pix_qual[Y_LENGTH][X_LENGTH];
   float data_flag[Y_LENGTH][X_LENGTH];

/* OPEN THE INPUT FILE */
   fid = SDstart(argv[1], DFACC_RDONLY);

/* FIND THE NUMBER OF IMAGES (nsds) and GLOBAL ATTRIBUTES (ngattr) */
   status = SDfileinfo(fid, &nsds, &ngattr);

   if(nsds + ngattr < 1) return;

/* OBTAIN INFORMATION ABOUT EACH IMAGE */
/* NOTE: THERE SHOULD BE TWO IMAGES FOR THE PATHFINDER BEST SST DATA */
/* AND THREE IMAGES FOR PATHFINDER ALL SST DATA. */

   for(i = 0; i < nsds; i++)
   {
      sds_id = SDselect(fid, i);

/* OBTAIN THE NAME, RANK, DIMENSION SIZES, DATA TYPE AND */
/* NUMBER OF ATTRIBUTES */
      status = SDgetinfo(sds_id,name,&rank,dimsizes,&nt,&nattrs);

/* READ THE ENTIRE 8 BIT RASTER IMAGE */
      retn=DFR8getimage(argv[1],in_data,X_LENGTH,Y_LENGTH,NULL);

/* OBTAIN THE CALIBRATION AND OFFSET VALUES */
      status=SDgetcal(sds_id,&cal,&cal_err,&off,&off_err,&num_type);

/* MULTIPLY THE DIGITAL NUMBER BY CALIBRATION NUMBER (0.15) */
/* AND ADD THE OFFSET (-3.0) TO GET DEGREES CELSIUS */

      for (x=0; x<X_LENGTH; x++)
         for (y=0; y<Y_LENGTH; y++)
            if (nsds == 2)
               if (i == 0) 
                  orig_sst[y][x]=cal*in_data[y][x] + off;
               else 
                  data_flag[y][x]=in_data[y][x];
            else 
               if (i == 0) 
                  orig_sst[y][x]=cal*in_data[y][x] + off;
               else if (i == 1)
                  pix_qual[y][x]=in_data[y][x];
               else
                  data_flag[y][x]=in_data[y][x];

/* CODE FOR THE OUTPUT SHOULD BE WRITTEN BY THE USER */ 
/* AND INSERTED BELOW THIS COMMENT STATEMENT */
/* */
/* AS AN EXAMPLE OF WRITING THE DATA TO A NON-HDF FORMAT, */
/* CODE WHICH PRINTS THE DATA TO THE SCREEN IS SHOWN. */
/* NOTE, HOWEVER, THAT THE USER IS HIGHLY DISCOURAGED FROM */
/* WRITING AN ENTIRE DATA SET TO THE SCREEN (OR TO AN */
/* ASCII FILE) DUE TO THE SIZE OF THE INPUT. */

/*
      for (x=0; x<X_LENGTH; x++)
         for (y=0; y<Y_LENGTH; y++) 
            if (nsds == 2)
               if (i == 0)
                  printf("i=%d x=%d y=%d IN_DATA = %d ORIG_SST = %f\n",i,
                            x,y,in_data[y][x],orig_sst[y][x]);
                else
                  printf("i=%d x=%d y=%d IN_DATA = %d FLAG_DATA = %f\n",
                            i,x,y,in_data[y][x],data_flag[y][x]);
             else 
                if (i == 0)
                   printf("i=%d x=%d y=%d IN_DATA = %d ORIG_SST = %f\n",
                      i,x,y,in_data[y][x],orig_sst[y][x]);
                else if (i == 1)
                   printf("i=%d x=%d y=%d IN_DATA = %d PIX_QUAL = %f\n",
                      i,x,y,in_data[y][x],pix_qual[y][x]);
                else
                   printf("i=%d x=%d y=%d IN_DATA = %d FLAG_DATA = %f\n",
                      i,x,y,in_data[y][x],data_flag[y][x]);
*/

   } 

   SDend(fid);
}


IDL Program to Produce Subsets from Raw Global Images
IDL EXTRACTION PROGRAM written by J. Vazquez
pro extract, x,xlatmn,xlatmx,xlonmn,xlonmx,xext,i180
;
;convert from lat,lon coordinates to pixel coordinates
; input: image file and maximum, minimum latitudes and longitudes
;
;for region to extract
;     x: byte array containing image data
;output: extract image file xext
;  i180: parameter that controls whether want -180 to 180 or 0 to 360
;        coordinate system
;      : = 0 0 to 360
;      : = 1 -180 to 180

if i180 eq 0 then xlon1=0.
if i180 eq 1 then xlon1=-180.

xlat1=-90.
xlon1=-180.
delta=4096./360.

iymin=fix((xlatmn-xlat1)*delta)
iymax=fix((xlatmx-xlat1)*delta)
ixmin=fix((xlonmn-xlon1)*delta)
ixmax=fix((xlonmx-xlon1)*delta)

print,ixmin,ixmax,iymin,iymax
nxdim=(ixmax-ixmin+1)
nydim=(iymax-iymin+1)
xext=bytarr(nxdim,nydim)

if i180 eq 1 then x180=bytarr(2048,1024)
if i180 eq 1 then x180(0:1023,*)=x(1024:2047,*)
if i180 eq 1 then x180(1024:2047,*)=x(0:1023,*)
if i180 eq 1 then x=x180

xext(0:nxdim-1,0:nydim-1)=x(ixmin:ixmax,iymin:iymax)
end


FORTRAN Program to Produce Subsets from Raw Global Images
FORTRAN EXTRACTION PROGRAM written by J. Vazquez
subroutine extract(x,xlatmn,xlatmx,xlonmn,xlonmx,xext,i180)
c
c*****convert from lat,lon coordinates to pixel coordinates
c*****input:  image x (raw no header) and maximum, minimum latitudes
c*****        and longitudes for region to extract
c*****output: extracted image file xext
c
c       i180: parameter that controls whether want -180 to 180 or 0 to 360
c             coordinate system
c           : = 0 0 to 360
c           : = 1 -180 to 180
c

byte x(4096,2048),xext(4096,2048),x180(4096,2048)

if (i180 .eq. 0) xlon1=0.
if (i180 .eq. 1) xlon1=-180.
xlat1=-90.
delta=4096./360.
if i180 eq 0 then
   do 60 j=2049,4096
      do 61i=1,2048
         x180(j-2048,i)=x(j,i)
      61 continue
   60 continue
   do 70 i=1,2048
      do 71 j=1,2048
         x180(j+2048,i)=x(j,i)
      71 continue
   70 continue
endif
iymin=fix((xlatmn-xlat1)*delta)+1
iymax=fix((xlatmx-xlat1)*delta)+1
ixmin=fix((xlonmn-xlon1)*delta)+1
ixmax=fix((xlonmx-xlon1)*delta)+1
print,ixmin,ixmax,iymin,iymax
nxdim=(ixmax-ixmin+1)
nydim=(iymax-iymin+1)

ix=0
do 100 j=ixmin,ixmax
   ix=ix+1

iy=0
do 101 i=iymin,iymax
        iy=iy+1

if (i180 .eq. 1) xext(ix,iy)=x180(j,i)
if (i180 .eq. 0) xext(ix,iy)=x (j,i)
101 continue
100 continue
end


Makefile for FORTRAN
Written by K.L. Perry

f77 =   f77
LIBS =  -L/usr/local/lib -ldf
INCLUDE =       -Wf,-I/usr/local/include/hdf
FILES = read_pfsst_data.f
OBJECTS =       read_pfsst_data.o
read_pfsst_data:        $(OBJECTS)
f77 $(OBJECTS) $(LIBS) -o read_pfsst_data

Note: Remember to change the Makefile to suite your own directory structure.


C Program to extract attributes
Written by 8/96 A.V. Tran, K.L. Perry
/*================================================================= 
   read_pfsst_info:  a C program to write the Raster Image and 
                     Attribute information from an AVHRR Pathfinder
                     SST data file to an output file.  Each of the
                     input files is in the HDF format.
                     
   TO RUN THIS PROGRAM USE THE FOLLOWING COMMAND: 

              read_pfsst_info  <infile> <outfile>

                where: infile = the HDF input file
                       outfile= the ascii output file containing
                                the information about the data 
                                and attributes of the infile.

   8/96 A.V. Tran, K.L. Perry
=================================================================*/
                     
/* --------------------------------------------------- */
/* NOTE: THE HDF LIBRARY MUST BE INSTALLED IN ORDER TO */
/*       RUN THIS PROGRAM                              */     
/* --------------------------------------------------- */

#include <stdio.h>     
#include <hdf.h>
                          
int main(int argc, char *argv[])
{
  FILE *fout;
  int32 fid;
  int32 status,nsds,ngattr;

  int32 sds_id;
  int32  nt,nattrs,rank = 2;
  int32  dimsizes[50];
  char name[512];

  int32 i, j;
  intn  count;

/* OPEN THE INPUT AND OUTPUT FILES */

  fid = SDstart(argv[1], DFACC_RDONLY);
  fout = fopen(argv[2], "w");

/* FIND THE NUMBER OF IMAGES and GLOBAL ATTRIBUTES (ngattr) */

  status = SDfileinfo(fid, &nsds, &ngattr);
  if(nsds + ngattr < 1) return;
   
  fprintf(fout, "Datasets\n");
  fprintf(fout, "There are %d dataset%s and %d global attribute%s in this file.\n",nsds, (nsds == 1 ? "" : "s"),ngattr,(ngattr == 1 ? "" : "s"));

/* OBTAIN INFORMATION ABOUT EACH IMAGE */

  if(nsds) {
    fprintf(fout, "Available datasets :\n");
    fprintf(fout, "\n");
    
    for(i = 0; i < nsds; i++) {
      sds_id = SDselect(fid, i);

/* NAME, RANK, DIMENSION SIZES, DATA TYPE and NUMBER OF ATTRIBUTES */

      status = SDgetinfo(sds_id, name, &rank, dimsizes, &nt, &nattrs);
      fprintf(fout, "%d %s  has rank %d ",i, name, rank);

/* PRINT THE DIMENSIONS SO THAT THE COLUMNS ARE FIRST IN THE ARRAY, */
/* AND THE ROWS ARE SECOND                                          */

      for(j = 0; j <= rank-1; j++) 
        if(j == 0)
          fprintf(fout, "[%d", dimsizes[j]);
        else
          fprintf(fout, ", %d]", dimsizes[j]);

/* GET THE DATA TYPE FROM THE SUBROUTINE GET_TYPE */

      fprintf(fout, ".  The dataset is composed of %s.\n", get_type(nt));

/* OBTAIN INFORMATION ABOUT THE LOCAL ATTRIBUTES */

      if(nattrs) {
        fprintf(fout, "It has the following attributes :\n");

        for(j = 0; j < nattrs; j++) {
          char *valstr;

/* FIND THE NAME, DATA TYPE AND NUMBER OF VALUES FOR LOCAL ATTRIBUTE */

          status = SDattrinfo(sds_id, j, name, &nt, &count);

/* CALL SUBROUTINE GET_ATTRIBUTE TO GET ATTRIBUTE VALUES */

          valstr = get_attribute(sds_id, j, nt, count);
          if(valstr == NULL) continue;

          fprintf(fout, "Attribute %s has the value : %s \n", name, valstr);

          HDfreespace((void *)valstr);
        }
        SDendaccess(sds_id);
      }
    }
  }

/* GET INFORMATION ABOUT GLOBAL ATTRIBUTES */

  if(ngattr) {
    fprintf(fout, "Global attributes :\n");

    for(i = 0; i < ngattr; i++) {

      char *valstr;

/* FIND THE NAME, DATA TYPE AND NUMBER OF VALUES */

      status = SDattrinfo(fid, i, name, &nt, &count);

/* CALL SUBROUTINE GET_ATTRIBUTE TO GET ATTRIBUTE VALUES */

      valstr = get_attribute(fid, i, nt, count);
      if(valstr == NULL) continue;

      fprintf(fout, "Attribute %s has the value : %s\n", name, valstr);

      HDfreespace((void *)valstr);
    }
  }

/* CLOSE THE INPUT AND OUTPUT FILES */

  SDend(fid);
  fclose(fout);
}

/* ================================================================== */
/*
   SUBROUTINE GET_TYPE:  a subroutine to return a buffer containing
                         the data type in ascii
*/

get_type(nt)
int32 nt;
{

    switch(nt) {

    case DFNT_CHAR   : return("8-bit characters");

    case DFNT_INT8   : return("signed 8-bit integers");
    case DFNT_UINT8  : return("unsigned 8-bit integers");
    case DFNT_INT16  : return("signed 16-bit integers");
    case DFNT_UINT16 : return("unsigned 8-bit integers");
    case DFNT_INT32  : return("signed 32-bit integers");
    case DFNT_UINT32 : return("unsigned 32-bit integers");

    case DFNT_FLOAT32  : return("32-bit floating point numbers");
    case DFNT_FLOAT64  : return("64-bit floating point numbers");
 
    default : return("unknown number type");

    }

}

/* ------------------------------------------------------------------ */
/*
   SUBROUTINE GET_ATTRIBUTE: a subroutine to read and return the value 
                             of an attribute given the attribute id,
                             index, data type and the number of values.
*/

get_attribute(id, num, nt, count)
int32 id;
int32 nt;
int32 count;
int32 num;
{

    char *tbuff;
    int32 dsize;
    int32 status;

    dsize = DFKNTsize(nt);

    if(dsize < 1) return NULL;

    tbuff = HDgetspace(dsize * (count + 1));
    if(tbuff == NULL) return NULL;

    status = SDreadattr(id, num, tbuff);

    return(buffer_to_string(tbuff, nt, count));

}

/* ------------------------------------------------------------------ */
/*  
   SUBROUTINE BUFFER_TO_STRING: a subroutine to return an ascii string
                                given a buffer, data type and the number
                                of values.
*/

buffer_to_string(tbuff, nt, count)
char * tbuff;
int32 nt;
int32 count;
{
    intn i;
    char * buffer;
    
    if(nt == DFNT_CHAR) {
        tbuff[count] = '\0';
        return tbuff;
    }

    buffer = (char *) HDgetspace(80 * count);
    if(buffer == NULL) return NULL;

    buffer[0] = '\0';

    switch(nt) {
    case DFNT_INT8   : 
    case DFNT_UINT8  : 
        sprintf(buffer, "%d", ((int8 *)tbuff)[0]);
        for(i = 1; i < count; i++)
            sprintf(buffer, "%s, %f", buffer, ((int8 *)tbuff)[i]);
        break;
    case DFNT_INT16   : 
    case DFNT_UINT16  : 
        sprintf(buffer, "%d", ((int16 *)tbuff)[0]);
        for(i = 1; i < count; i++)
            sprintf(buffer, "%s, %f", buffer, ((int16 *)tbuff)[i]);
        break;
    case DFNT_INT32   : 
    case DFNT_UINT32  : 
        sprintf(buffer, "%d", ((int32 *)tbuff)[0]);
        for(i = 1; i < count; i++)
            sprintf(buffer, "%s, %f", buffer, ((int32 *)tbuff)[i]);
        break;
    case DFNT_FLOAT32 : 
        sprintf(buffer, "%f", ((float32 *)tbuff)[0]);
        for(i = 1; i < count; i++)
            sprintf(buffer, "%s, %f", buffer, ((float32 *)tbuff)[i]);
        break;
    case DFNT_FLOAT64 : 
        sprintf(buffer, "%f", ((float64 *)tbuff)[0]);        
        for(i = 1; i < count; i++)
            sprintf(buffer, "%s, %f", buffer, ((float64 *)tbuff)[i]);
        break;
    }
    HDfreespace((void *)tbuff);
    return buffer;
}



APPENDIX E

SCIENCE WORKING GROUP AND JPL TEAM

Members of the Pathfinder AVHRR Oceans Science Working Group

Name Affiliation
Peter Cornillon, Chair University of Rhode Island
Graduate School of Oceanography
Robert Evans University of Miami, Rosenstiel
School of Marine and Atmos. Sciences
Gene Feldman NASA/GSFC
Richard Legeckis NOAA/NESDIS
Richard Reynolds NOAA/NWS
Charles Walton NOAA/NESDIS


Members of the JPL Ocean Pathfinder Team

Jorge Vazquez, task manager Jet Propulsion Laboratory
Rosanna Sumagaysay
Kelly Perry,
Jet Propulsion Laboratory
Jet Propulsion Laboratory


For further information on the data set contact:
JPL PO.DAAC User Services Office
Jet Propulsion Laboratory, M/S 300-323
4800 Oak Grove Dr.
Pasadena, CA 91109, U.S.A.
tel: (818) 354-9890
fax: (818) 393-2718

Homepage URL: http://podaac.jpl.nasa.gov
FTP site: podaac.jpl.nasa.gov
login: anonymous
password: your complete email address



APPENDIX F

ACRONYMS

AVHRR Advanced Very High Resolution Radiometer
EOS Earth Observing System
FTP File Transfer Protocol
FAQ Frequently Asked Question
GAC Global Area Coverage
HDF Hierarchical Data Format
IMS Information Management System
JPL Jet Propulsion Laboratory
MCSST Multi-Channel Sea Surface Temperature
NASA National Aeronautics and Space Administration
NCSA National Center for SuperComputing Applications
NOAA National Oceanic and Atmospheric Administration
PMDB Pathfinder Matchup Data Base
PO.DAAC Physical Oceanography Distributed Active Archive Center
QA Quality Assurance
RSMAS Rosenstiel School of Marine and Atmospheric Sciences
SST Sea Surface Temperature
SWG Science Working Group
WWW World Wide Web
PFMDB Pathfinder Matchup Database



APPENDIX G
AVHRR Pathfinder Oceans

Sea Surface Temperature Algorithm

Version 4.0

February 6, 1998

Robert Evans and Guillermo Podestá

University of Miami

Rosenstiel School of Marine and Atmospheric Science

Foreword

This document describes the algorithm used to compute sea surface temperature (SST) values in the AVHRR Pathfinder Oceans global SST products, Version 4.0.

Introduction

The need for accurate global sea surface temperature fields has been receiving increasing attention, primarily due to its importance in understanding variability in the oceans' climate. Satellite SST measurements are attractive due to their global, repeated coverage, compared to any other type of measurements. Since 1981, the NOAA series of polar-orbiting spacecraft have carried the Advanced Very High Resolution Radiometer (AVHRR), an instrument with three infrared (IR) channels suitable for estimating SST [Schwalb, 1978]. These channels are located in the wavelength regions between 3.5µm and 4µm and between 10µm and 12.5µm, where the atmosphere is comparatively transparent.

At IR wavelengths, the ocean surface emits radiation almost as a blackbody. In principle, without an absorbing and emitting atmosphere between the sea surface and the satellite, it would be possible to estimate SST using a single channel measurement. In reality, surface-leaving infrared radiance is attenuated by the atmosphere before it reaches a satellite sensor. Therefore, it is necessary to make corrections for atmospheric effects. Water vapor, CO2, CH4, NO2 and aerosols are the major constituents that determine the atmospheric extinction of IR radiance [Minnett, 1990]. Among them, absorption due to water vapor accounts for most of the needed correction [Barton et al., 1989].

Various techniques have been proposed to account for the atmospheric absorption of surface IR radiance, and to produce accurate retrievals of SST. Anding and Kauth [1970] found that the difference in measurements at two properly selected infrared channels is proportional to the amount of atmospheric correction required. Using differences in



brightness temperatures measured by an early satellite radiometer, Prabhakara et al. [1974] estimated SST to a reasonable accuracy. In a recent review of techniques to derive SST from satellite IR measurements, Barton [1995] shows that the differential absorption is exploited in all IR SST algorithms, and that there is a basic form for most algorithms:

SST = aTi + g (Ti - Tj )+ c

where Ti and Tj are brightness temperature measurements in channels i and j, and a and c are constants. The g term is defined as

g = (1 - ti) / (ti - tj),

where t is the transmittance through the atmosphere from the surface to the satellite. In cases of weak absorption, the transmittance can be approximated by (1 - ku), where k is the mass absorption ccoefficient of the atmospheric absorbers and u is the path length [Barton, 1995].

All AVHRR algorithms share the general form described above, although various modifications have been introduced through the years to improve performance. McClain et al. [1985] developed algorithms for SST retrieval based on linear differences in brightness temperatures among AVHRR channels. This so-called MCSST algorithm assumed a constant g. The MCSST algorithm was NOAA's operational procedure for several years [McClain et al., 1985]. Subsequent improvements incorporated a correction for increased path lengths at larger satellite zenith angles [Cornillon et al., 1987]. Other improvements in the atmospheric correction involved nonlinear formulations, in which g was proportional to the brightness temperatures, as in the CPSST (cross-product SST) algorithm described by Walton [1988] and Walton et al. [1990].

The latest version of the operational NOAA algorithm is the NLSST (non-linear SST), in which g is assumed to be proportional to a first-guess SST value (which can be obtained in various ways). The AVHRR Oceans Pathfinder SST algorithm (which is used to produce the Pathfinder SST or PFSST global fields) is based on the NLSST algorithm developed by C.Walton of NOAA/NESDIS. The NLSST algorithm has the following form:

SSTsat = a + b T4+ c (T4 ­ T5) SSTguess + d (T4 ­ T5) (sec(q) ­ 1),

where SSTsat is the satellite-derived SST estimate, T4 and T5 are brightness temperatures in AVHRR channels 4 and 5 respectively, SSTguess is a first-guess SST value, and q is the satellite zenith angle. Coefficients a, b, c, and d are estimated from regression analyses using co-located in situ and satellite measurements (or "matchups"). Typically, NOAA produced a set of coefficients using matchups for a certain period; these coefficients would not be modified until there was a perceived need (e.g., after the eruption of the Mt. Pinatubo volcano in June 1991, or when a new AVHRR was launched).


The Pathfinder Match-up Database

SST algorithm coefficients based on IR measurements can be estimated in two major ways. The first alternative involves the use of a radiative transfer model and a set of atmospheric vertical profiles (temperature, humidity), which are used to simulate at-satellite brightness temperatures (BTs). The simulated BTs are subsequently regressed against in situ SST measurements in order to derive algorithm coefficients. This semi-physical approach has been adopted to develop algorithms for the Along-Track Scanning Radiometer (ATSR) onboard the ERS-1 satellite. It must be noted that this approach produces so-called "skin temperature" estimates (the skin is the uppermost layer of the ocean, responsible for the IR emission). The skin temperature may differ from the "bulk" temperature usually measured by traditional in situ instruments (e.g., buoys).

A second alternative for estimating SST algorithm coefficients is a regression between in situ SST measurements and nearly-coincident satellite observations (matchups). This produces a statistical algorithm, tuned to bulk SST measurements. Differences between skin and bulk SST algorithms are discussed by Wick et al. [1992]. The statistical approach has been followed for the estimation of coefficients for the Pathfinder SST (PFSST) algorithm.

One highlight of the AVHRR Pathfinder Oceans program is that, for the first time, a set of coincident in situ and satellite measurements, used for algorithm development and testing, is being distributed together with the global SST products. A complete description of the Pathfinder Matchup Database (PFMDB), including information on how to obtainthe matchup files, can be found in http://www.rsmas.miami.edu/~gui/v19/matchups.v19.0.html . For the purpose of algorithm estimation, there are two relevant points regarding the matchups. First, the Pathfinder matchups have tight space-time constraints: in situ and satellite observations are deemed coincident if they occur within ±30minutes and ±0.1° of latitude and longitude of one another. Second, the PFMDB has been screened carefully to identify most cloud-contaminated matchups, so they could be excluded from the coefficient estimation stage. Subsequently, any discussion of matchups in the context of algorithm estimation and testing will imply the use of cloud-screened matchups, unless otherwise indicated.


The Pathfinder SST Algorithm

The NLSST formulation developed by C. Walton (formerly at NOAA/NESDIS) and described in a manuscript to appear in the Journal of Geophysical Research was adopted as the basis for the AVHRR Oceans Pathfinder SST algorithm (PFSST) because of its adequate performance and its operational nature in NOAA products. Nevertheless, a few minor modifications were introduced to NOAA's NLSST; these modifications are described in the following paragraphs.

Separate coefficients for two (T4 - T5) regimes

Various diagnostics performed on residuals (defined as observed minus predicted SSTs, or in situ minus satellite SSTs) from earlier versions of the Pathfinder SST algorithm suggested that the association between the (T4 ­ T5) values (hereafter referred to as T45) and the bias of the atmospheric correction was somewhat different for dry and moist atmospheres. For instance, there seemed to be a consistent positive bias in SST residuals at low T45 values; this was true for all satellite zenith angle values. Also, we attempted to find optimal empirical transformations to linearize associations between predictand and predictor variables prior to regression. The estimated empirical transformations showed a change in their shape at T45 values around 0.7­1.0°C. This suggests a change in the underlying functional form of the association between T45 and SST. A full discussion of the possible physical reasons behind this change is beyond the scope of this document. Nevertheless, it is likely that the balance between various sources of radiance sensed by the AVHRR instrument (e.g., radiance emitted by the ocean surface vs. atmospheric radiance) changes as a function of atmospheric moisture. Furthermore, the effects of ocean surface emissivity, air-sea temperature differences, and atmospheric absorbers other than water vapor become more relevant in drier atmospheres.

As an empirical approach to capturing the change in the nature of the functional association between AVHRR radiances and SST, we implemented a piece-wise fit to algorithm coefficients. Algorithm coefficients were estimated separately for (a) low, and (b) intermediate to high T45 values. The chosen boundary between the two regimes was T45=0.7°C. To avoid discontinuities in the global PFSST fields as the computation switches from one set of coefficients to another, we implemented a transition in the SST calculation, which is described in detail below.

Coefficients estimated for monthly periods

Earlier versions of the PFSST algorithm had been estimated with a single set of coefficients per T45 regime for the entire span of an AVHRR's lifetime. Initial diagnostics, however, suggested the presence of temporal trends in the algorithm performance. The trends included a variety of temporal scales, from seasonal (e.g., higher bias and rms in SST


residuals during Northern Hemisphere summers) to interannual. The interannual trend was of unclear origin and could be due to changes in the radiometric sensitivity of an AVHRR as it ages, or changes in the operating conditions. For instance, during the later stages of NOAA-9 and NOAA-11's operational lifetimes, the baseplate on which the onboard calibration targets are mounted was operated at a significantly higher temperature than during previous years (also, at higher temperatures than those used for pre-launch sensor characterization).

To reduce the presence of trends in the SST estimates, the PFSST coefficients were estimated on a month by month basis. We used a window of five months of matchups centered on the month for which coefficients were being estimated. Matchups for each period in the window were weighted differently: the central month (e.g., month N) was assigned a weight of 1.0, for adjacent months (N ­ 1 and N + 1) the weights were 0.8, and weights of 0.5 were used for the ends of the 5-month window (months N ­ 2 and N + 2). In selecting these weights, no attempt was made to reflect the statistical structure (i.e., temporal correlation) of the SST values. Instead, the main goal was to ensure greater statistical weight for the matchups from the central month. Also, note that the weights do not add up to 1.0, as they are intended to convey an idea of the relative temporal weighting. The weights are normalized so they add up to 1.0 during the weighted regression procedure.

The weighting scheme for the ends of the data series for a given instrument was defined differently. For the first and last months of an AVHRR series, we used a 3-month window, and for the second and next-to-last months we used a 4-month window. The matchup windows used for each month in a series, together with their respective weights, are illustrated in Figure 1. In all cases, the temporal weights were subsequently combined with robustness weights derived from residuals from a first-estimate of SST values; more on this below.

For the Pathfinder algorithms estimated to date, there were two major exceptions to the scheme described in Figure 1. First, the NOAA-11 data set was treated as two separate series, with the separation corresponding to the main eruption of Mt. Pinatubo (approximately, June 15, 1991). This implies that (a) two sets of coefficients were estimated for June 1991 corresponding to pre- and post-Pinatubo conditions, and (b) the first and second halves of June 1991 were treated, respectively, as the end and the beginning of two series (the weighting schemes described above for the ends of a series were used). The second exception was the NOAA-9 data used to fill the gap (September 1994­January 1995) between the demise of NOAA-11 and the beginning of the NOAA-14 operational period. Because of the short span of this data series, a single set of algorithm coefficients was estimated for each T45 regime for the entire NOAA-9 gap, without using temporal weighting. For the beginning of NOAA-14, the first period (nominally labelled February 1995) included a few days of January 1995.


Figure 1. Temporal window used in the estimation of PFSST algorithm coefficients for a given month or period. The figure illustrates the number of months used and the weights asigned to matchups for each month for (a) the first month in a series, (b) the second month in a series, and (c) months in the middle of a series. For data at the end of a series, the coefficients are estimated using the mirror images of cases (a) and (b). The dark bar indicates the month (or period) for which coefficients are estimated.

Algorithm Coefficient Estimation

Before matchups are used for coeficient estimation, one needs to exclude those records likely to be cloud-covered or cloud-contaminated. Several methods have been proposed in the literature to identify cloud-covered pixels in AVHRR imagery; a few examples include the work of Saunders and Kriebel [1988], Derrien et al. [1993], Luo et al. [1995] and Cayula and Cornillon [1996]. The procedures used to identify cloud contamination in the Pathfinder matchups are described in detail in the Pathfinder Matchup Database documentation. Briefly, the matchups cloud-flagging involves a series of tests based on thresholds of differences between brightness temperatures in two different channels, and spatial homogeneity tests.


Despite the cloud-flagging tests, there are always a few matchups that lead to large SST residuals (i.e., large differences between observed and estimated SSTs). This maybe due to a failure of the cloud tests, or also to problems with the in situ values (e.g., a miscalibrated buoy). Outliers (matchups with high SST residuals) can unduly influence coefficient estimates, so they need to be excluded from the estimation procedure. In an earlier version of the algorithm estimation, we had excluded matchups with absolute value of residuals>2°C (with respect to a first guess SST). Given the relatively good performance of the cloud flagging tests, very few extreme values were excluded by the ±2°C test, and thus this test should not have influenced significantly our earlier asessments of algorithm performance (despite the fact that a threshold was used to exclude large residuals). Nevertheless, we explored other alternatives that would not involve a fixed threshold for exclusion of matchups leading to high SST residuals. Such a procedure was implemented in Version 4.0 of the Pathfinder products.

In the current Pathfinder protocol, algorithm coefficient estimation is a three-stage process. In the first stage, those matchups which passed the cloud flagging tests (see description of Pathfinder matchups) are used to estimate a first-guess set of coefficients using a resistant regression procedure, in which coefficient estimates are relatively insensitive to large outliers. The first-guess coefficients are used to compute first-guess SST residuals. In a second stage, the first-guess residuals are used to compute robustness weights, to decrease the influence of large residuals in the final coefficient estimation. In the third and last stage, robustness weights and temporal weights are used in a weighted least squares regression. That is, matchups with large first-guess residuals have a lower weight in the final coefficient estimation. The third stage produces the operational set of coefficients. This procedure is repeated for each period for which coefficients are estimated (usually, a month), and for each T45 regime. We discuss each of the three stages in more detail below.

Estimation of First-guess SST Residuals

All matchups that passed the cloud-flagging tests were used to estimate a first-guess set of algorithm coefficients. Because cloud-contaminated matchups may remain after cloud-flagging tests, we used a modern resistant regression procedure, in which large outliers do not unduly influence the first-guess coefficient values. We used a procedure called Least Trimmed Squares or LTS [Rousseeuw and Leroy, 1987], which returns a regression estimate that minimizes the sum of the smallest half of the squared residuals. The LTS method has a very high breakdown point. In statistical terms, the breakdown point indicates the proportion of outliers that can be present in a data set before estimates are strongly influenced; the higher the breakdown point, the more resistant the procedure [Lanzante, 1996]. The resistant regression was repeated for each T45 regime and for each period for which algorithm coefficients were estimated.

The first-guess algorithm coefficients were used to compute first-guess SST residuals (in situ SST minus first-guess SST). These first-guess residuals were then used to derive


robustness weights used as input to a subsequent stage of coefficient estimation. The goal was to assign reduced weights to large first-guess residuals (for instance, those due to unidentified cloud contamination) in order to reduce their influence on subsequent coefficient estimation.

Computation of Robustness Weights

To derive robustness weights from the first-guess SST residuals, we followed a sequence of steps. First, we estimated, for each period and T45 regime, the median of the absolute values of first-guess residuals; this quantity is designated MAD, which stands for median absolute of deviations. Second, we used the bisquare function to compute robustness weights. The bisquare function B(u) , where u denotes the function's argument, has a value of (1 ­ u2)2 for | u | < 1, otherwise it is zero. The first-guess residuals (denoted as e), their corresponding MAD (for a given period and T45 regime), and the bisquare function B were used to compute robustness weights r as

r = B [ e / (6 * MAD) ] .

The previous equation indicates that the robustness weights have a value of zero for matchups with first-guess residuals greater than ±6 * MAD. The factor of 6 multiplying the MAD was selected so that, if the first-guess residuals have an underlying Gaussian distribution, this threshold is approximately equivalent to rejecting first-guess residuals beyond ±4 standard deviations. In most cases, MAD values ranged betwen 0.3° and 0.4°C: this implies that residuals with absolute values greater than 1.8° to 2.4°C were excluded (i.e., had weights equal to zero). Robustness weights are illustrated in Figure 2.

Figure 2. Robustness weights computed using the bisquare function and MAD values of 0.3°C (solid line) and 0.4°C (dashed line). See text for explanation.


Weighted Least-squares Estimation

The last stage of the coefficient estimation procedure involves a weighted least squares procedure. The robustness weights derived in the previous stage were multiplied by the temporal weights (see "Coefficients estimated for monthly periods"), and the resulting values were the final weights used as input to a weighted least squares regression. The weight assigned to a particular matchup for coefficient estimation, therefore, was a function of (a) its first-guess SST residual, and (b) its temporal separation from the month for which coefficients were being estimated. The coefficients estimated by the weighted least squares regression were used to process the global Pathfinder SST fields. The coefficients for each AVHRR, month, and T45 regime are shown in the Appendix to this document.

What is the advantage of using the approach described in previous paragraphs? In earlier versions, the PFSST coefficients showed temporal fluctuations at scales of months to years. Although fluctuations in the coefficient for one term of the algorithm usually were compensated somewhat by the values of coefficients for other terms, it was unclear if this had consequences on algorithm performance. To assess the advantages of the resistant procedure, we first estimated a set of coefficients using unweighted least squares on the matchups that passed the cloud-flagging tests (Procedure A). A second set of coefficients (Procedure B) was estimated by imposing a ±2°C limit on SST residuals derived from Procedure A, and re-estimating coefficients (using unweighted least squares) excluding residuals higher than that limit. Finally, we performed the coefficient estimation using the resistant regression followed by weighted least squares, as described in previous paragraphs (Procedure C).

Figure 3 shows the values of the first two algorithm coefficients (the constant term and the term multiplying T4) in the PFSST algorithm for NOAA-9 matchups. The three lines correspond to coefficients estimated following Procedures A, B and C, as described above. It is clear that the temporal stability of the coefficients is much greater, and this is conceptually attractive. These results may make one consider whether separate monthly coefficients are necessary. Although the resistant regression estimation seems to reduce considerably seasonal fluctuations in the coefficients, they have not disappeared entirely. Furthermore, still there are unexplained low-frequency trends in coefficient values. Therefore, for the current version of the Pathfinder fields we still use monthly coefficients.


Figure 3. Time series of the first two algorithm coefficients for NOAA-9, 1985­1988. Coefficient 1 is the constant term, and Coefficient 2 is the T4 multiplier. The coefficients correspond to matchups with T45 values > 0.7°C. The open circles indicate coefficient values estimated using unweighted least squares regression on all matchups that passed the cloud flagging tests (Procedure A in text). The asterisks denote coefficients estimated by (i) using straight least squares as described in previous sentence, (ii) excluding residuals with absolute values greater than 2.2°C, and (iii) reestimating coefficients using straight least squares (Procedure B in text). The solid circles indicate coefficient values estimated via a resistant regression, followed by a weighted least squares regression (Procedure C in text).


SST Computation at T45 Regime Transition

The separate estimation of coefficients for two T45 regimes (above and below T45 = 0.7°C) could result in discontinuities in the global PFSST fields as the computation switches from one set of coefficients to another. To avoid these effects, we implemented a transition in the calculation which was used in the processing of the Pathfinder SST fields. To assess algorithm performance using the matchups, the transitional calculation was used for the matchups.

Basically, the procedure involves the computation of two intermediate PFSSTs for each matchup, respectively using coefficients corresponding to either T45 regime in the period. The final PFSST is computed as the weighted sum of the two intermediate SSTs, where the weight is a function of the T45 value. That is,

PFSST = w1 * PFSST1 + (1 ­ w1) * PFSST2

where PFSST is the Pathfinder SST, PFSST1 and PFSST2 are the SSTs computed using the algorithm coefficients for low and high T45 regimes, respectively, and w1 is a weighting factor which varies as a function of T45 as follows:

· For T45 0.5°C, w1 = 1.0

· For 0.5°C < T45 < 0.9°C, w1 = 1 - ((T45 - 0.5°) / (0.9° - 0.5°))

· For T45 0.9°C, w1 = 0.0

That is, for T45 0.5°C, the PFSST is computed using only the coefficients for low T45 regimes. Similarly, for T45 0.9°C, only the coefficients for high T45 regimes are used. For T45 values between 0.5°C and 0.9° (i.e., a ±0.2° interval around the 0.7°C boundary between T45 regimes), the final SST is a linear combination of the SSTs computed from both sets of coefficients.

Algorithm Coefficients ­ Pathfinder SST Fields Version 4

The algorithm coefficients estimated in the manner described above, and used to compute the AVHRR Ocean Pathfinder global SST fields denoted as Version4 are listed in AppendixA.

Algorithm performance

A detailed characterization of algorithm performance is beyond the scope of this document. However, to give potential users of the Pathfinder SST fields a feel for the variability in SST estimates, we provide boxplots of SST residuals (in situ SST minus Pathfinder SST) for the AVHRRs operating during the period 1985­1995 (Figure 4). For each operational


AVHRR, boxplots are shown for four latitudinal bands: 40°­20°S, 20°S­20°N, 20°­40°N, and 40°­60°N. No results are shown for latitudes below 40°S or above 60°N due to the paucity of matchups in those regions.

In most cases, each box and whiskers in a panel corresponds to a month. Exceptions include the June 1991 period for NOAA-11, separated into pre- and post-Pinatubo (Figure 4b). The first month of NOAA-14 includes data only for a few days of January 1995 (Figure 4c). For each period, the dot represents the median of SST residuals in a period and the box encompasses the central 50% of the residuals (i.e., data between the 25-percentile and the 75-percentile). The whiskers indicate residuals within 1.5 times the width of the box. Individual dashes are extreme outliers (beyond the whiskers' length). The dashed vertical lines in each panel indicate -0.2°, 0° and 0.2°C. In general, each box and whiskers (corresponding to a given month and latitudinal band) contains at least 100 matchups, therefore statistics are considered stable.

One important feature is the bias introduced by aerosols from the Mt. Pinatubo main eruption (June 1991). This is particularly noticeable in the tropical latitudes, and it takes several months for residuals to approach normal. Another important point is that, in general, the tropical band shows a negative bias of about 0.1°­0.2°C, that is, SST algorithms are under-correcting. In contrast, the band between 20°N and 40°N, where most matchups occur, tends to show a positive bias (over-correcting). It is remarkable that the central half of the residuals (denoted by the boxes) shows a fairly tight distribution.

We stress that the residuals used to produce Figure 4 were computed using the actual in situ SST as the first-guess value in the algorithm. In contrast, the first-guess SST in the Pathfinder field calculations was the Reynolds Optimally Interpolated SST (see document on Pathfinder Matchups for description). The SST residuals computed using the values on the global fields, then, can be slightly different.


Figure 4a. Boxplots of Pathfinder SST residuals for NOAA-9, January 1985 to November 1988. See text for description of the plots.


Figure 4b. Boxplots of Pathfinder SST residuals for NOAA-11, November 1988 to September 1994. See text for description of the plots.


Figure 4c. Boxplots of Pathfinder SST residuals for NOAA-9 gap period, September 1994 to March 1995. See text for description of the plots.


Figure 4d. Boxplots of Pathfinder SST residuals for NOAA-14, February 1995 to December 1995. See text for description of the plots.


Global vs. Regional Algorithms

The main challenge in developing a Pathfinder global SST algorithm is to achieve relatively uniform performance throughout a wide range of atmospheric and oceanic conditions. As Barton [1995] pointed out, SST algorithms assume a first guess of the state of the atmosphere (e.g., a typical shape of water vapor and temperature profiles). A similar statement can be made about typical oceanic conditions (e.g., a certain average structure of the ocean's uppermost layer is assumed in comparisons with in situ SST measurements). When conditions deviate from the implicit first guess in atmosphere and ocean conditions, errors arise in SST retrievals. Deviations from implicit first-guess conditions are more likely in a global algorithm than in regionally-tuned algorithms, and this should be kept in mind when evaluating global SST estimates. Furthermore, in the case of statistically-derived global SST algorithms, the first-guess conditions will be the average of conditions at all the matchup locations and times used in coefficient estimation. We stress that this average will be weighted by the relative distribution of matchups, likely to change in time. The performance of an SST algorithm for a given set of atmospheric and oceanic conditions, therefore, depends not only on how close those conditions are to the average state, but also on how well represented are those conditions in the matchup set used to derived the algorithm coefficients.

References

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Barton, I.J. and R.P. Cechet. 1989. Comparison and optimization of AVHRR sea surface temperature algorithms. Journal of Atmospheric and Oceanic Technology 6: 1083­1089.

Cayula, J.-F. and P. Cornillon. 1996. Cloud detection from a sequence of SST images. Remote Sensing of the Environment 55: 80­88.

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Appendix A

Pathfinder V4 SST Algorithm coefficients

A.1 Coefficients for NOAA-9. The second and third columns show the beginning and end dates for which each set of coefficients should be used (dates expressed as year-day of year). For each period, two rows are shown, one for coefficients used in low water vapor regimes (T4­T5 0.7°C) and the other one for coefficients used in medium/high water vapor regimes (T4 - T5 > 0.7°C). The coefficients a, b, c and d correspond to the algorithm form shown in the text.

AVHRR     T4 - T5     Begin         End     a     b     c     d
     regime        date           date
NOA9      0.7°     85001     85031     1.220     0.965     0.127     1.191
NOA9     > 0.7°     85001     85031     1.487     0.980     0.077     1.085
NOA9      0.7°     85032     85059     1.199     0.957     0.142     1.182
NOA9     > 0.7°     85032     85059     1.513     0.979     0.077     1.080
NOA9      0.7°     85060     85090     1.244     0.941     0.161     1.203
NOA9     > 0.7°     85060     85090     1.506     0.976     0.080     1.061
NOA9      0.7°     85091     85120     1.180     0.934     0.182     1.230
NOA9     > 0.7°     85091     85120     1.499     0.978     0.079     1.078
NOA9      0.7°     85121     85151     1.160     0.936     0.176     1.251
NOA9     > 0.7°     85121     85151     1.471     0.976     0.081     1.083
NOA9      0.7°     85152     85181     1.147     0.939     0.160     1.375
NOA9     > 0.7°     85152     85181     1.432     0.973     0.084     1.016
NOA9      0.7°     85182     85212     1.301     0.922     0.154     1.278
NOA9     > 0.7°     85182     85212     1.496     0.967     0.083     0.988
NOA9      0.7°     85213     85243     1.136     0.943     0.132     1.226
NOA9     > 0.7°     85213     85243     1.453     0.963     0.086     0.958
NOA9      0.7°     85244     85273     1.316     0.927     0.140     1.061
NOA9     > 0.7°     85244     85273     1.346     0.965     0.087     0.915
NOA9      0.7°     85274     85304     1.148     0.938     0.147     0.672
NOA9     > 0.7°     85274     85304     1.248     0.971     0.087     0.911
NOA9      0.7°     85305     85334     1.316     0.919     0.166     0.307
NOA9     > 0.7°     85305     85334     1.235     0.978     0.084     0.930
NOA9      0.7°     85335     85365     1.386     0.919     0.163     0.191
NOA9     > 0.7°     85335     85365     1.282     0.975     0.084     1.011
NOA9      0.7°     86001     86031     1.361     0.931     0.152     0.083
NOA9     > 0.7°     86001     86031     1.316     0.983     0.079     1.070
NOA9      0.7°     86032     86059     1.346     0.935     0.147     0.363
NOA9     > 0.7°     86032     86059     1.370     0.981     0.079     1.071
NOA9      0.7°     86060     86090     1.368     0.936     0.141     0.959
NOA9     > 0.7°     86060     86090     1.408     0.979     0.080     1.056


AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NOA9      0.7°     86091     86120     1.363     0.929     0.148     1.176
NOA9     > 0.7°     86091     86120     1.388     0.976     0.083     1.071
NOA9      0.7°     86121     86151     1.384     0.920     0.157     1.219
NOA9     > 0.7°     86121     86151     1.409     0.975     0.083     1.130
NOA9      0.7°     86152     86181     1.401     0.915     0.159     1.305
NOA9     > 0.7°     86152     86181     1.477     0.976     0.080     1.149
NOA9      0.7°     86182     86212     1.282     0.915     0.168     1.376
NOA9     > 0.7°     86182     86212     1.421     0.983     0.077     1.146
NOA9      0.7°     86213     86243     1.186     0.918     0.175     1.141
NOA9     > 0.7°     86213     86243     1.392     0.983     0.077     1.095
NOA9      0.7°     86244     86273     1.246     0.911     0.181     0.768
NOA9     > 0.7°     86244     86273     1.311     0.983     0.079     1.068
NOA9      0.7°     86274     86304     1.417     0.887     0.201     0.695
NOA9     > 0.7°     86274     86304     1.341     0.974     0.082     1.034
NOA9      0.7°     86305     86334     1.605     0.879     0.200     -0.203
NOA9     > 0.7°     86305     86334     1.442     0.962     0.087     0.929
NOA9      0.7°     86335     86365     1.551     0.912     0.152     0.569
NOA9     > 0.7°     86335     86365     1.585     0.952     0.089     0.865
NOA9      0.7°     87001     87031     1.505     0.920     0.147     0.605
NOA9     > 0.7°     87001     87031     1.629     0.948     0.090     0.871
NOA9      0.7°     87032     87059     1.456     0.908     0.173     0.725
NOA9     > 0.7°     87032     87059     1.647     0.950     0.089     0.890
NOA9      0.7°     87060     87090     1.415     0.901     0.193     0.558
NOA9     > 0.7°     87060     87090     1.672     0.956     0.086     0.882
NOA9      0.7°     87087     91120     1.398     0.896     0.204     0.390
NOA9     > 0.7°     87087     91120     1.611     0.963     0.084     0.922
NOA9      0.7°     87121     87151     1.379     0.899     0.199     0.297
NOA9     > 0.7°     87121     87151     1.501     0.975     0.082     0.952
NOA9      0.7°     87152     87181     1.182     0.917     0.199     0.507
NOA9     > 0.7°     87152     87181     1.514     0.979     0.079     0.974
NOA9      0.7°     87182     87212     1.248     0.911     0.194     0.326
NOA9     > 0.7°     87182     87212     1.569     0.973     0.080     0.953
NOA9      0.7°     87213     87243     1.251     0.908     0.191     0.363
NOA9     > 0.7°     87213     87243     1.476     0.972     0.081     0.855
NOA9      0.7°     87244     87273     1.399     0.894     0.199     0.059
NOA9     > 0.7°     87244     87273     1.462     0.965     0.083     0.800
NOA9      0.7°     87274     87304     1.508     0.901     0.176     0.216
NOA9     > 0.7°     87274     87304     1.419     0.963     0.085     0.760
NOA9      0.7°     87305     87334     1.585     0.874     0.211     -0.241
NOA9     > 0.7°     87305     87334     1.452     0.962     0.085     0.755
NOA9      0.7°     87335     87365     1.537     0.894     0.183     -0.047
NOA9     > 0.7°     87335     87365     1.475     0.959     0.087     0.730


AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NOA9      0.7°     88001     88031     1.500     0.910     0.164     -0.414
NOA9     > 0.7°     88001     88031     1.488     0.958     0.087     0.764
NOA9      0.7°     88032     88060     1.509     0.907     0.161     -0.375
NOA9     > 0.7°     88032     88060     1.561     0.951     0.089     0.748
NOA9      0.7°     88061     88091     1.506     0.915     0.148     -0.246
NOA9     > 0.7°     88061     88091     1.556     0.950     0.089     0.762
NOA9      0.7°     88088     92121     1.487     0.905     0.168     -0.277
NOA9     > 0.7°     88088     92121     1.545     0.954     0.088     0.784
NOA9      0.7°     88122     88152     1.489     0.907     0.163     -0.175
NOA9     > 0.7°     88122     88152     1.504     0.965     0.083     0.842
NOA9      0.7°     88153     88182     1.468     0.898     0.179     0.156
NOA9     > 0.7°     88153     88182     1.531     0.969     0.080     0.872
NOA9      0.7°     88183     88213     1.325     0.918     0.165     0.081
NOA9     > 0.7°     88183     88213     1.569     0.968     0.080     0.851
NOA9      0.7°     88214     88244     1.489     0.905     0.172     -0.669
NOA9     > 0.7°     88214     88244     1.647     0.968     0.079     0.767
NOA9      0.7°     88245     88274     1.500     0.919     0.154     -0.998
NOA9     > 0.7°     88245     88274     1.693     0.962     0.081     0.671
NOA9      0.7°     88275     88305     1.642     0.909     0.161     -1.436
NOA9     > 0.7°     88275     88305     1.796     0.950     0.084     0.595
NOA9      0.7°     88306     88335     1.638     0.918     0.152     -1.418
NOA9     > 0.7°     88306     88335     1.840     0.943     0.085     0.560


A.2 Coefficients for NOAA-11. The second and third columns show the beginning and end dates for which each set of coefficients should be used (dates expressed as year-day of year). For each period, two rows are shown, one for coefficients used in low water vapor regimes (T4­T5 0.7°C) and the other one for coefficients used in medium/high water vapor regimes (T4 - T5 > 0.7°C). The coefficients a, b, c and d correspond to the algorithm form shown in the text.
AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NO11      0.7°     88306     88335     0.979     0.920     0.171     0.720
NO11     > 0.7°     88306     88335     1.146     0.964     0.081     0.941
NO11      0.7°     88336     88366     1.120     0.895     0.193     0.643
NO11     > 0.7°     88336     88366     1.226     0.960     0.081     0.950
NO11      0.7°     89001     89031     1.213     0.886     0.198     0.707
NO11     > 0.7°     89001     89031     1.261     0.958     0.081     0.962
NO11      0.7°     89032     89059     1.307     0.895     0.178     0.760
NO11     > 0.7°     89032     89059     1.374     0.952     0.082     0.952
NO11      0.7°     89060     89090     1.374     0.901     0.167     0.782
NO11     > 0.7°     89060     89090     1.455     0.948     0.081     0.954
NO11      0.7°     89091     89120     1.455     0.900     0.162     0.719
NO11     > 0.7°     89091     89120     1.541     0.944     0.081     0.954
NO11      0.7°     89121     89151     1.508     0.887     0.181     0.529
NO11     > 0.7°     89121     89151     1.58:     0.947     0.079     0.966
NO11      0.7°     89152     89181     1.592     0.876     0.186     0.386
NO11     > 0.7°     89152     89181     1.654     0.951     0.075     0.986
NO11      0.7°     89182     89212     1.771     0.855     0.175     0.614
NO11     > 0.7°     89182     89212     1.56:     0.959     0.073     0.991
NO11      0.7°     89213     89243     1.645     0.873     0.147     0.886
NO11     > 0.7°     89213     89243     1.369     0.969     0.072     0.986
NO11      0.7°     89244     89273     1.221     0.906     0.145     0.819
NO11     > 0.7°     89244     89273     1.206     0.974     0.073     0.972
NO11      0.7°     89274     89304     0.908     0.93:     0.139     0.732
NO11     > 0.7°     89274     89304     1.079     0.977     0.074     0.975
NO11      0.7°     89305     89334     0.891     0.948     0.134     0.808
NO11     > 0.7°     89305     89334     1.125     0.971     0.077     0.960
NO11      0.7°     89335     89365     0.967     0.950     0.127     0.959
NO11     > 0.7°     89335     89365     1.250     0.961     0.079     0.930
NO11      0.7°     90001     90031     1.117     0.942     0.124     1.088
NO11     > 0.7°     90001     90031     1.387     0.955     0.079     0.918
NO11      0.7°     90032     90059     1.214     0.932     0.130     1.091
NO11     > 0.7°     90032     90059     1.503     0.950     0.079     0.920
NO11      0.7°     90060     90090     1.114     0.960     0.105     1.535
NO11     > 0.7°     90060     90090     1.543     0.949     0.079     0.936


AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NO11      0.7°     90091     90120     1.004     0.984     0.075     1.926
NO11     > 0.7°     90091     90120     1.533     0.950     0.078     0.944
NO11      0.7°     90121     90151     0.925     0.991     0.066     2.148
NO11     > 0.7°     90121     90151     1.465     0.957     0.077     0.968
NO11      0.7°     90152     90181     0.885     0.993     0.060     2.148
NO11     > 0.7°     90152     90181     1.392     0.962     0.076     0.994
NO11      0.7°     90182     90212     0.961     0.985     0.064     1.836
NO11     > 0.7°     90182     90212     1.301     0.969     0.074     1.004
NO11      0.7°     90213     90243     1.197     0.965     0.073     1.225
NO11     > 0.7°     90213     90243     1.341     0.965     0.074     1.001
NO11      0.7°     90244     90273     1.551     0.904     0.131     0.635
NO11     > 0.7°     90244     90273     1.333     0.963     0.075     0.982
NO11      0.7°     90274     90304     1.460     0.911     0.132     0.526
NO11     > 0.7°     90274     90304     1.374     0.958     0.077     0.941
NO11      0.7°     90305     90334     1.404     0.917     0.126     0.670
NO11     > 0.7°     90305     90334     1.382     0.955     0.079     0.906
NO11      0.7°     90335     90365     1.361     0.917     0.129     0.997
NO11     > 0.7°     90335     90365     1.397     0.950     0.081     0.895
NO11      0.7°     91001     91031     1.356     0.921     0.120     1.233
NO11     > 0.7°     91001     91031     1.411     0.949     0.082     0.905
NO11      0.7°     91032     91059     1.328     0.928     0.113     1.391
NO11     > 0.7°     91032     91059     1.462     0.946     0.082     0.946
NO11      0.7°     91060     91090     1.363     0.929     0.108     1.410
NO11     > 0.7°     91060     91090     1.465     0.947     0.080     0.987
NO11      0.7°     91091     91120     1.380     0.927     0.113     1.229
NO11     > 0.7°     91091     91120     1.483     0.948     0.079     0.992
NO11      0.7°     91121     91151     1.403     0.929     0.112     1.122
NO11     > 0.7°     91121     91151     1.474     0.951     0.078     1.004
NO11      0.7°     91151     91166     1.389     0.923     0.129     0.910
NO11     > 0.7°     91151     91166     1.474     0.954     0.076     1.005
NO11      0.7°     91167     91181     1.586     0.897     0.162     0.933
NO11     > 0.7°     91167     91181     1.454     0.969     0.077     0.962
NO11      0.7°     91182     91212     1.622     0.915     0.133     1.454
NO11     > 0.7°     91182     91212     1.528     0.965     0.078     0.952
NO11      0.7°     91213     91243     1.762     0.908     0.130     1.443
NO11     > 0.7°     91213     91243     1.502     0.966     0.080     0.950
NO11      0.7°     91244     91273     1.864     0.906     0.124     1.414
NO11     > 0.7°     91244     91273     1.528     0.963     0.083     0.934
NO11      0.7°     91274     91304     1.936     0.896     0.137     1.287
NO11     > 0.7°     91274     91304     1.575     0.959     0.086     0.933
NO11      0.7°     91305     91334     1.942     0.897     0.145     1.357
NO11     > 0.7°     91305     91334     1.616     0.962     0.086     0.962


AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NO11      0.7°     91335     91365     1.947     0.902     0.148     1.327
NO11     > 0.7°     91335     91365     1.621     0.968     0.084     1.020
NO11      0.7°     92001     92031     1.928     0.919     0.134     1.516
NO11     > 0.7°     92001     92031     1.783     0.965     0.082     1.044
NO11      0.7°     92032     92060     1.950     0.929     0.122     1.658
NO11     > 0.7°     92032     92060     1.920     0.959     0.081     1.062
NO11      0.7°     92061     92091     1.992     0.934     0.113     1.690
NO11     > 0.7°     92061     92091     2.058     0.952     0.080     1.083
NO11      0.7°     92092     92121     2.043     0.935     0.108     1.639
NO11     > 0.7°     92092     92121     2.212     0.944     0.080     1.108
NO11      0.7°     92122     92152     2.103     0.934     0.105     1.604
NO11     > 0.7°     92122     92152     2.317     0.93:     0.079     1.125
NO11      0.7°     92153     92182     2.177     0.926     0.106     1.521
NO11     > 0.7°     92153     92182     2.275     0.945     0.077     1.125
NO11      0.7°     92183     92213     2.234     0.912     0.118     1.493
NO11     > 0.7°     92183     92213     2.200     0.949     0.076     1.115
NO11      0.7°     92214     92244     2.226     0.901     0.128     1.360
NO11     > 0.7°     92214     92244     2.083     0.951     0.076     1.065
NO11      0.7°     92245     92274     2.212     0.886     0.146     1.200
NO11     > 0.7°     92245     92274     2.072     0.947     0.077     0.995
NO11      0.7°     92275     92305     2.180     0.883     0.149     0.991
NO11     > 0.7°     92275     92305     2.100     0.93:     0.079     0.933
NO11      0.7°     92306     92335     2.182     0.886     0.138     0.991
NO11     > 0.7°     92306     92335     2.150     0.934     0.079     0.905
NO11      0.7°     92336     92366     2.158     0.894     0.123     1.131
NO11     > 0.7°     92336     92366     2.155     0.934     0.078     0.902
NO11      0.7°     93001     93031     2.088     0.901     0.118     1.267
NO11     > 0.7°     93001     93031     2.164     0.933     0.076     0.896
NO11      0.7°     93032     93059     2.034     0.914     0.105     1.336
NO11     > 0.7°     93032     93059     2.176     0.930     0.077     0.872
NO11      0.7°     93060     93090     2.006     0.914     0.109     1.271
NO11     > 0.7°     93060     93090     2.173     0.928     0.077     0.856
NO11      0.7°     93091     93120     1.972     0.911     0.117     1.070
NO11     > 0.7°     93091     93120     2.128     0.929     0.078     0.845
NO11      0.7°     93121     93151     1.910     0.912     0.119     0.950
NO11     > 0.7°     93121     93151     2.060     0.933     0.077     0.858
NO11      0.7°     93152     93181     1.828     0.908     0.130     0.777
NO11     > 0.7°     93152     93181     1.980     0.936     0.077     0.856
NO11      0.7°     93182     93212     1.693     0.907     0.133     0.812
NO11     > 0.7°     93182     93212     1.897     0.938     0.077     0.806


AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NO11      0.7°     93213     93243     1.714     0.901     0.137     0.625
NO11     > 0.7°     93213     93243     1.805     0.938     0.078     0.712
NO11      0.7°     93244     93273     1.833     0.889     0.140     0.505
NO11     > 0.7°     93244     93273     1.866     0.931     0.080     0.606
NO11      0.7°     93274     93304     1.955     0.878     0.144     0.089
NO11     > 0.7°     93274     93304     1.997     0.921     0.081     0.508
NO11      0.7°     93305     93334     2.090     0.869     0.144     -0.377
NO11     > 0.7°     93305     93334     2.130     0.913     0.082     0.440
NO11      0.7°     93335     93365     2.140     0.880     0.127     -0.674
NO11     > 0.7°     93335     93365     2.178     0.912     0.083     0.399
NO11      0.7°     94001     94031     2.077     0.891     0.124     -1.179
NO11     > 0.7°     94001     94031     2.148     0.916     0.082     0.396
NO11      0.7°     94032     94059     1.910     0.912     0.119     -0.473
NO11     > 0.7°     94032     94059     2.119     0.919     0.081     0.405
NO11      0.7°     94060     94090     1.828     0.910     0.137     -0.263
NO11     > 0.7°     94060     94090     2.071     0.924     0.080     0.460
NO11      0.7°     94091     94120     1.792     0.908     0.147     -0.094
NO11     > 0.7°     94091     94120     2.030     0.928     0.078     0.528
NO11      0.7°     94121     94151     1.725     0.912     0.150     0.156
NO11     > 0.7°     94121     94151     2.004     0.932     0.077     0.604
NO11      0.7°     94152     94181     1.706     0.914     0.153     0.287
NO11     > 0.7°     94152     94181     2.100     0.929     0.076     0.626
NO11      0.7°     94182     94212     1.691     0.916     0.150     0.495
NO11     > 0.7°     94182     94212     2.154     0.926     0.075     0.627
NO11      0.7°     94213     94243     1.722     0.918     0.144     0.708
NO11     > 0.7°     94213     94243     2.245     0.921     0.075     0.597
NO11      0.7°     94244     94273     1.722     0.922     0.135     1.259
NO11     > 0.7°     94244     94273     2.261     0.921     0.075     0.581


A.3 Coefficients for NOAA-9, gap period. The second and third columns show the beginning and end dates for which each set of coefficients should be used (dates expressed as year-day of year). For each period, two rows are shown, one for coefficients used in low water vapor regimes (T4­T5 0.7°C) and the other one for coefficients used in medium/high water vapor regimes (T4 - T5 > 0.7°C). The coefficients a, b, c and d correspond to the algorithm form shown in the text. Note that in this case only one set of coefficients (per T45 regime) was used for the entire period (i.e., no monthly coefficients were used).
AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NOA9g      0.7°     94244     95120     1.439     0.902     0.188     0.913
NOA9g     > 0.7°     94244     95120     1.656     0.970     0.079     1.043


A.4 Coefficients for NOAA-14. The second and third columns show the beginning and end dates for which each set of coefficients should be used (dates expressed as year-day of year). For each period, two rows are shown, one for coefficients used in low water vapor regimes (T4­T5 0.7°C) and the other one for coefficients used in medium/high water vapor regimes (T4 - T5 > 0.7°C). The coefficients a, b, c and d correspond to the algorithm form shown in the text.
AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NO14      0.7°     95001     95059     1.013     0.931     0.108     0.832
NO14     > 0.7°     95001     95059     1.273     0.951     0.074     0.894
NO14      0.7°     95060     95090     1.050     0.921     0.121     1.220
NO14     > 0.7°     95060     95090     1.242     0.955     0.072     0.909
NO14      0.7°     95091     95120     1.049     0.907     0.141     1.218
NO14     > 0.7°     95091     95120     1.235     0.957     0.072     0.911
NO14      0.7°     95121     95151     1.045     0.904     0.152     1.114
NO14     > 0.7°     95121     95151     1.258     0.956     0.072     0.905
NO14      0.7°     95152     95181     1.084     0.899     0.152     1.094
NO14     > 0.7°     95152     95181     1.250     0.959     0.071     0.921
NO14      0.7°     95182     95212     1.062     0.901     0.149     1.068
NO14     > 0.7°     95182     95212     1.286     0.958     0.070     0.897
NO14      0.7°     95213     95243     0.978     0.908     0.142     1.161
NO14     > 0.7°     95213     95243     1.299     0.956     0.070     0.894
NO14      0.7°     95244     95273     0.949     0.913     0.134     1.195
NO14     > 0.7°     95244     95273     1.325     0.951     0.071     0.894
NO14      0.7°     95274     95304     0.989     0.912     0.129     1.355
NO14     > 0.7°     95274     95304     1.335     0.948     0.072     0.906
NO14      0.7°     95305     95334     1.077     0.912     0.117     0.896
NO14     > 0.7°     95305     95334     1.350     0.949     0.071     0.892
NO14      0.7°     95335     95365     1.163     0.905     0.117     0.971
NO14     > 0.7°     95335     95365     1.372     0.946     0.073     0.873
NO14      0.7°     96001     96059     1.013     0.931     0.108     0.832
NO14     > 0.7°     96001     96059     1.273     0.951     0.074     0.894
NO14      0.7°     96060     96090     1.050     0.921     0.121     1.220
NO14     > 0.7°     96060     96090     1.242     0.955     0.072     0.909
NO14      0.7°     96091     96120     1.049     0.907     0.141     1.218
NO14     > 0.7°     96091     96120     1.235     0.957     0.072     0.911
NO14      0.7°     96121     96151     1.045     0.904     0.152     1.114
NO14     > 0.7°     96121     96151     1.258     0.956     0.072     0.905
NO14      0.7°     96152     96181     1.084     0.899     0.152     1.094
NO14     > 0.7°     96152     96181     1.250     0.959     0.071     0.921
NO14      0.7°     96182     96212     1.062     0.901     0.149     1.068
NO14     > 0.7°     96182     96212     1.286     0.958     0.070     0.897


AVHRR     T4 - T5     Begin     End     a     b     c     d
     regime     date     date
NO14      0.7°     96213     96243     0.978     0.908     0.142     1.161
NO14     > 0.7°     96213     96243     1.299     0.956     0.070     0.894
NO14      0.7°     96244     96273     0.949     0.913     0.134     1.195
NO14     > 0.7°     96244     96273     1.325     0.951     0.071     0.894
NO14      0.7°     96274     96304     0.989     0.912     0.129     1.355
NO14     > 0.7°     96274     96304     1.335     0.948     0.072     0.906
NO14      0.7°     96305     96334     1.077     0.912     0.117     0.896
NO14     > 0.7°     96305     96334     1.350     0.949     0.071     0.892
NO14      0.7°     96335     96365     1.163     0.905     0.117     0.971
NO14     > 0.7°     96335     96365     1.372     0.946     0.073     0.873