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AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites

Summary

The AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites are a collection of products for both poles, consisting of twice-daily gridded and calibrated satellite channel data and derived parameters. Data include five Advanced Very High Resolution Radiometer (AVHRR) channels, clear sky surface broadband albedo and skin temperature, solar zenith angle, satellite elevation angle, sun-satellite relative azimuth angle, surface type mask, cloud mask, orbit mask, time of acquisition, and ice motion vectors. Data are composited onto two grids per day based on common local solar times and scan angle. Reduced-resolution data (25 km) derived from the 1.25 km data are available to assist users in selecting these data. AVHRR local area coverage (LAC) and High Resolution Picture Transmission (HRPT) level 1b data are used to generate the Polar Pathfinder products at grid spacings of 1.25 km. AVHRR Polar Pathfinder data extend poleward from 48.4 degrees north and 53.2 degrees south latitudes, from August 1993 through December 1998 for the Northern Hemisphere, and from April 1992 through January 1996 for the Southern Hemisphere. Data are in 1-byte and 2-byte integer grid format. Ice motion vectors are in ASCII text format. Data are available on 8-mm tape or by FTP.

Citing These Data

To broaden awareness of our services, NSIDC requests that you acknowledge the use of data sets distributed by NSIDC. Please refer to the citation below for the suggested form, or contact NSIDC User Services for further information. We also request that you send us one reprint of any publication that cites the use of data received from our Center. This helps us to determine the level of use of the data we distribute. Thank you.

Scambos, T., T. Haran, C. Fowler, J. Maslanik, J. Key, and W. Emery. 2000, updated 2002. AVHRR Polar Pathfinder twice-daily 1.25 km EASE-Grid composites. Boulder, CO, USA: National Snow and Ice Data Center. Digital media.

Overview Table

Table of Contents

1. Contacts and Acknowledgments
2. Detailed Data Description
3. Data Access and Tools
4. Data Acquisition and Processing
5. References and Related Publications
6. Document Information

1. Contacts and Acknowledgments

Investigator(s) Name and Title

Terry Haran and Ted Scambos
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449

William Emery, Chuck Fowler, and James Maslanik
Colorado Center for Astrodynamics Research
CCAR, 431 UCB
University of Colorado
Boulder, CO 80309-0431

Jeffrey Key
NOAA/NESDIS
1225 W. Dayton St.
Madison, WI 53706

Technical Contact

Acknowledgements

NSIDC acknowledges the valuable assistance of Dr. Jeff Key, Dr. Julienne Stroeve, Mr. Terry Haran, Mr. Tim Hutchinson, and Mr. Robert Stone in developing and testing these products. The product developers would also like to thank the considerable aid provided by NSIDC staff members, especially the writers, programmers, and operations and user services staff who have helped make these products available.

The principal investigators of this data set acknowledge the NOAA Satellite Active Archive (SAA) and Colorado Center for Astrodynamics Research (CCAR) for their assistance in providing data.

2. Detailed Data Description

The AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites provide a time series overview of each pole. This data set is useful for detail process studies in regions of special interest. The first two stages of processing (assembling and compositing the AVHRR polar 1 km level 1b scenes) comprise nearly 80% of the processing effort for the AVHRR Polar Pathfinder data. Users can now take advantage of this time savings to produce their own geophysical products from the navigated and calibrated channel data, or they may choose to work with the output from the subsequent processing methods described below.

To assist users in selecting 1.25 km data, or for applications that require a lower data volume product, reduced-resolution data (25 km data, derived from the 1.25 km data) are available for broadband albedo, clear sky skin temperature, orbit mask and information file, and cloud mask. NSIDC also distributes the AVHRR Polar Pathfinder Twice-Daily 5 km EASE-Grid Composites (with nearly 20 years of data), along with reduced-resolution data (25 km) derived from the 5 km data. Ice motion products consisting of u- and v-component ice motion vectors are also available.

The National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) Pathfinder Program is designed to provide scientists with a time series of global-scale remote sensing data for the period prior to the Earth Observing System (EOS) satellite launches. The Pathfinder concept involves careful reprocessing of existing data sets and making them readily available for global change research. The Polar Pathfinder Program has established a cooperation to maximize the scientific potential of polar remote sensing data. Polar Pathfinder data sets use a common grid structure with different resolutions, using the EASE-Grid as a standard reference system for comparative data analysis.

The AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites facilitate regional studies, climate studies, and evaluation and forcing of process models.

Format

Individual parameters are stored as 1-byte or 2-byte integer grids for each date, time, and hemisphere. Cell size is 1.25 km, with either 1 or 2 bytes per cell (see File Naming Convention below). Byte order is big-endian. (Examples of big-endian platforms include Sun/Solaris, SGI, HP, and IBM UNIX. Little-endian platforms include Windows PC and Linux platforms with Intel processors). The Northern Hemisphere grid is 7220 pixels wide by 7220 pixels high, centered on the North Pole. The Southern Hemisphere grid is 6420 pixels wide by 6420 pixels high, centered on the South Pole.

Reduced-resolution data (at 25 km resolution) derived from 1.25 km data are available for several parameters. Data characteristics are the same as for the corresponding 1.25 km product. Dimensions of the browse images are 361 pixels wide by 361 pixels high for the Northern Hemisphere grid, and 321 pixels wide by 321 pixels high for the Southern Hemisphere grid.

The ice motion vectors and the information file that accompanies the orbit mask are both in ASCII text format. Formats for 2-byte raster files are summarized in the Parameter section of this document. Formats for 1-byte raster files are summarized as follows (see also the description of bit processing):

AVHRR-derived ice mask and land/water mask (amsk):

Bit 7 (most significant): Valid data

0: invalid or missing data
1: valid data

Bit 6: AVHRR-derived ice

0: not ice
1: ice

Bit 5: SMMR- and SSM/I-derived ice

0: not ice
1: ice

Bit 3: Land/water

0: water
1: land

Bits 4, 2, 1, and 0 are always 0. For invalid or missing data, all bits are 0 (i.e., amsk=0). The amsk file is an intermediate file which is used as input to the temperature, albedo, and cloud masking procedures. Bits 6, 5, and 3 data are copied from amsk to the same bit positions in the cmsk file described below.

Cloud mask (cmsk):

Bit 7 (most significant): Cloud

0: clear
1: cloud

Bit 6: AVHRR-derived ice

0: not ice
1: ice

Bit 5: SMMR- and SSM/I-derived ice

0: not ice
1: ice

Bit 4: SSM/I LOCI-derived coastline

0: not coastline
1: coastline

Bit 3: Land/water

0: water
1: land

Bits 2, 1, 0: Region

0-7: region

Bits 5 and 4 are never both 1 for valid data. Bits 5 through 0 comprise a domain of 0 to 47, used in the cloud masking procedure. For invalid or missing data, all bits are 1 (i.e., cmsk=255).

Orbit mask (omsk) and information file (info):

Cell values range from 0 to 14. A value of 0 indicates missing data. Values of 1 to 14 specify an orbit number as defined in the corresponding information file.

Time mask (time):

Data are in increments of 0.1 hours, with cell values ranging from 0 to 244.

Ice motion vectors (icem):

Each ice motion vector file is an ASCII file that consists of a three line header followed by a series of vector lines. The first two lines of the header specify the two AVHRR scenes that were used to create the file. The third header line consists of 6 fields:

cols_in - number of vectors in a row of the vector grid
rows_in - number of vectors in a column of the vector grid
cols_image - number of columns in the source image
rows_image - number of rows in the source image
conversion - factor for converting cm/sec to pixels/day
disp_string - either "x and y displacements in cm/sec" or "x and y displacements in pixels/day" (NOTE: if disp_string is blank, then "x and y displacements in cm/sec" is implied.)

Following the 3 header lines are cols_in * rows_in lines each containing the following 5 fields:

x - the column value for the vector in image coordinates
y - the row value for the vector in image coordinates
x_disp - the x displacement, positive to the right
y_disp - the y displacement, positive up (opposite of row)
corr - the correlation value for this vector

Following is an example of the first 6 lines of a raw ice motion vector file named app_n001_1997171_0000_icem.v2:

/usr2/data/nav/north/iceimages/a14_ngc_970619_0032__a14_ngc_970619_0033.img.2
/usr2/data/nav/north/iceimages/a14_ngc_970620_0021.img.2
  478     478     7220    7220    0.691200
  29.5    29.5    0.00    0.00    0.00
  44.5    29.5    0.00    0.00    0.00
  59.5    29.5    0.00    0.00    0.00

Following are the last 3 lines of the same file:

7154.5  7184.5    0.00    0.00    0.00
7169.5  7184.5    0.00    0.00    0.00
7184.5  7184.5    0.00    0.00    0.00

A single vector corresponds to a 15 by 15 block of 1.25 km pixels. Thus, the effective resolution of the grid vectors is 18.75 km (15 * 1.25 km). The total number of lines in the file is 228487 (478 * 478 + 3). The x and y values are in units of the 7220 by 7220 1.25 km northern hemisphere grid, but start with an offset of 29.5 pixels from the upper left corner, and then step every 15 pixels from there.

File Naming Convention

File naming convention is "app_H001_YYYYDDD_TTTT_ZZZZ.vX" where:

app is AVHRR Polar Pathfinder
H is the hemisphere (n or s)
001 indicates 1.25 km resolution
YYYY is the year
DDD is the day of year
TTTT is the hour (0400 or 1400 for north files, 0200 or 1600 for south files)
ZZZZ is the file type as follows:

2-byte raster files:

albd - clear sky surface albedo
chn1 - AVHRR channel 1
chn2 - AVHRR channel 2
chn3 - AVHRR channel 3
chn4 - AVHRR channel 4
chn5 - AVHRR channel 5
sael - satellite elevation angle
solz - solar zenith angle
reaz - relative azimuth angle
temp - clear sky surface temperature

1-byte raster files:

amsk - AVHRR-derived surface type mask
cmsk - cloud mask
omsk - orbit mask
time - time of acquisition (UTC)

ASCII files:

icem - ice motion vectors
info - information file that accompanies orbit mask (omsk)

X is a version number (i.e., Version 1, Version 2, etc.)

File Size

Northern Hemisphere raster files: 104 MB (2-byte) and 52 MB (1-byte) per granule
Southern Hemisphere raster files: 82 MB (2-byte) and 41 MB (1-byte) per granule
Northern Hemisphere ice motion files: 8 MB
Southern Hemisphere ice motion files: 6.3 MB

Spatial Coverage

Spatial coverage extends from 48.4°N to 90°N, and from 53.2°S to 90°S. The actual coverage extends beyond these limits in the grid corners. The following table summarizes the values of corner pixels for each hemisphere.

For the Northern Hemisphere, the center of the tangent pixels is 48.40840 north latitude and the outer edge is 48.40237 north latitude. For the Southern Hemisphere, the center of the tangent pixels is 53.19462 south latitude and the outer edge is 53.18868 south latitude.

Spatial Coverage Map

Click on the thumbnails below to see detailed coverage maps.

Spatial Resolution

Data are regridded to the EASE-Grid projection with a 1.25 km pixel spacing.

Projection

Data are georeferenced to the EASE-Grid projection, an azimuthal equal area projection. Please review All About EASE-Grid for general information on the EASE-Grid, and Summary of NOAA/NASA Polar Pathfinder Grid Relationships for details of map projection parameters.

Grid Description

The Northern Hemisphere grid is 7220 pixels wide by 7220 pixels high, centered on the North Pole. The Southern Hemisphere grid is 6420 pixels wide by 6420 pixels high, centered on the South Pole. Cell size is 1.25 km. Grid coordinates begin in the upper left corner of the grid. Please see Summary of NOAA/NASA Polar Pathfinder Grid Relationships for more information, including grid parameters.

Temporal Coverage

Temporal coverage is from 19 August 1993 through 31 December 1998 for the Northern Hemisphere, and from 1 April 1992 through 31 January 1996 for the Southern Hemisphere.

Missing dates for Northern Hemisphere:

• 31 March 1994 - 14 July 1994
• 14 September 1994 - 7 February 1995
• 7 October 1995 - 31 December 1996

Missing dates for Southern Hemisphere:

• 1 February 1995 - 31 December 1995

Temporal Resolution

Composites are produced at target times of 0400 and 1400 local time for the Northern Hemisphere, and 0200 and 1600 local time for the Southern Hemisphere.

Parameter or Variable

Error Sources

Channel data are accurate to within approximately ± 0.2 percent based on sensor noise level of 0.4 data number (DN). Temperatures are accurate to within approximately 2 degrees Kelvin. Relative albedos in adjacent grid cells are accurate to within approximately 5 percent. However, absolute albedo values are approximate.

Accuracies for the products are difficult to determine, given the limited nature of existing case studies. Also, conditions vary substantially across the large product domains and over time. Plans are being developed to further define product accuracies for snow-covered areas, sea ice, and ice sheets. Based on studies to date, accuracies in general are approximately ± 2 degrees Kelvin for AVHRR-derived skin temperatures and ± 5 percent absolute for surface albedo. Much of this error is likely due to uncertainties in the performance of the cloud detection methods. For clear sky conditions, accuracies for albedo and temperature products are expected to be in the range noted in the Barrow test, with temperatures accurate to ± 0.5 degrees Kelvin.

For ice motion products, one pixel (at 1.1 km resolution) corresponds to a 1.27 cm/s accuracy limit on the ice motion vector, for images separated by 24 hours. Following the feature tracking validation, Emery et al. (1991) found that the x direction mean difference (bias) was 0.71 cm/s with a standard deviation of 0.62 cm/s. The y direction bias was 0.57 cm/s with a standard deviation of about 0.51. For overall magnitude, the bias was about 1.0 cm/s with a standard deviation of 0.9 cm/s. Compared with general ice velocities of 10 to 20 cm/s, these differences are quite small (5 to 10 percent) and demonstrate the validity of the MCC-derived ice velocities.

Known Problems with the Data

Some of the current deliverable data (Version 6) still contain time-of-acquisition errors. The script that converted Land Processes (LP) DAAC-format AVHRR level 1b files to CCAR-format files added a start of acquisition value to a corrected time value, which already existed in the original level 1b data. This resulted in an "over-corrected" start of acquisition time. This problem was inherent in all Version 2 data, but it was corrected in later versions. The exception was from 1 January through 30 September 1997 (Northern Hemisphere data). Thus, Version 6 data for these dates still contain time-of-acquisition errors.

In other cases, for example with Prince Albert and Tokyo receiving stations, the time correction value in the header file was always 0, so that the resulting navigation errors increased whenever the time correction file values increased. An example is 1 July 1997 through 16 July 1997 when the time correction values were about 1.8 seconds, resulting in a geolocation error for Prince Albert and Tokyo scenes of 11.7 km (6.5 km/sec x 1.8 sec). The remainder of the Version 2 processing period (1 January 1997 through 30 September 1997) had time correction values that ranged from -0.2 sec to 0.8 sec (corresponding to geolocation errors of -1.3 km and 5.2 km) for Prince Albert and Tokyo scenes, respectively.

All Version 3 NOAA-12 AVHRR header files contained a value of 0 for the time correction field. However, unlike the Prince Albert and Tokyo headers, the NOAA-12 headers contained a start of acquisition time that did not appear to have been time-corrected. Thus, the solution to the Version 2 time correction problem described above introduced an error in the geolocation of NOAA-12 scenes. However, most NOAA-12 data are from the Southern Hemisphere and primarily affected the morning (0200) composites. Northern Hemisphere NOAA-12 data were from Polar Star ship scenes, and very little of these data were included in the morning (0400) or afternoon (1400) composites.

Quality Assessment

Data Validation by Source

Product validation is a continuing process that takes advantage of comparative data as they become available. Comparisons were made between AVHRR Polar Pathfinder skin temperatures and surface-based measurements obtained at the South Pole over a seven-day period in 1995. These field data were collected by Robert Stone of the Cooperative Institute for Research in Environmental Sciences (CIRES) using a sled-mounted KT-19 pyrometer. Excluding observations when cloud cover was present, the agreement was generally within 0.5 degrees Kelvin. For data averaged over a four-hour period, temperatures were within 0.1 degrees Kelvin (a mean of -38.15 degrees Celsius for the AVHRR Polar Pathfinder observations, versus a mean of -38.25 degrees Celsius for the field data).

Evaluations are also in progress for retrievals over the Greenland Ice Sheet (Stroeve et al., 2000; Stroeve, 2000). Stroeve et al. (2000) compared surface albedo derived from the AVHRR Polar Pathfinder Twice-Daily 5 km EASE-Grid Composites with albedo measured at 14 automatic weather stations (AWS) around the Greenland Ice Sheet from January 1997 to August 1998. Results show that AVHRR-derived surface albedo values are, on average, 10 percent less than those measured by the AWS stations. However, station measurements tend to be positively biased by about four percent, and the differences in absolute albedo may be less (about six percent). In regions of Greenland where the albedo variability is small, such as the dry snow facies, the AVHRR albedo uncertainty exceeds the natural variability. Stroeve concluded that while further work is needed to improve the absolute accuracy of the AVHRR-derived surface albedo, the data provide temporally and spatially consistent estimates of the Greenland Ice Sheet albedo.

Analyses of the AVHRR Polar Pathfinder data, compared with data from the Surface Heat Budget of the Arctic Ocean (SHEBA) project, are in progress. See Maslanik et al. (2000) for preliminary results. The cloud masking process was assessed and refined throughout the duration of the project to optimize the algorithm for the entire areas of coverage. Comparisons of areally-averaged cloud fractions from the AVHRR Polar Pathfinder Twice-Daily 5 km EASE-Grid Composites with field observations at the SHEBA field site show that the AVHRR data were within nine percent of the cloud lidar/radar observations averaged from April to July 1998 (with Pathfinder data underestimating cloud fraction relative to the field measurements). Differences in monthly means for this period ranged from 2 percent in June to 21 percent in July (Maslanik et al., 2000). Comparison of all-sky skin temperature and albedo values derived from the AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites with SHEBA observations is described in Maslanik et al. (2000).

Other validation studies of surface temperature and albedo retrieval procedures included surface observations from a NOAA research site near Barrow, Alaska (71.32 degrees north latitude, 156.61 degrees west longitude). Daily AVHRR data from a preliminary Pathfinder data set (Meier et al. 1997) from mid-1992 to mid-1993 were used for this validation. Surface temperature estimates agreed with observations, with a correlation coefficient of 0.98, a bias of -0.97, and a RMSE of 4.70. For surface albedo, the bias (mean error) in the estimates was near zero (r=0.81, bias=0.00, RMSE=0.17), but the individual observations exhibited significant variability, attributed to surface inhomogeneity and retrieval scheme sensitivity to changes in atmospheric aerosol and water vapor amounts.

Ice motion data were validated by using the same satellite images for processing with a standard feature tracking technique that computed the ice motion between images. Feature tracking was conducted on an image processing system that allowed the operators to select ice features within any two successive image pairs, and then compute the displacements of these features. The computed subjective vectors were statistically compared with the most adjacent MCC vectors (Emery et al. 1991).

Data and related information will be updated on the NSIDC Polar Pathfinders page where appropriate.

3. Data Access and Tools

Data Access

Data are available free of charge. Users can access raw scene data through the AVHRR Polar 1 km Level 1b Data Set Search, Browse, and Order tool. As of September 2002, AVHRR 1.25 km data are no longer available through the Graphical Interface for Subsetting, Mapping, and Ordering (GISMO). Users who need composite grids should contact NSIDC User Services.

Software and Tools

NSIDC does not provide software or tools to read these data. The 1-byte and 2-byte data are easily read with most image processing and Geographic Information Systems (GIS) applications.

Related Data Collections

• AVHRR Polar Pathfinder Twice-Daily 5 km EASE-Grid Composites
• AVHRR Polar Pathfinder Twice-Daily 25 km EASE-Grid Composites
• AVHRR Polar 1 km Level 1b Data Set
• Polar Pathfinder Sampler

4. Data Acquisition and Processing

Theory of Measurements

Optical remote sensing
Remote sensing in the optical part of the electromagnetic spectrum (400-2500 nm) involves measuring the combined reflectance of solar radiation from both surface and atmosphere. There are several specific definitions of reflectance (see below), but in general, reflectance involves the diffuse scattering of light by a geometrically complex surface (Hapke 1993). Reflectance varies according to the degree of collimation, the incident irradiance, and the collimation of the detector. Collimation refers to the degree of angular diffusion of the the incident light or the size of the angular field of view of the detector. For instance, direct beam solar energy is considered highly collimated whereas the diffuse sky radiance is uncollimated; a narrow-angle field of view detector can be considered collimated while a hemispherical sensor such as a pyranometer is an uncollimated detector. The following table (modified from Diner et al. 1999) summarizes the most commonly-used terms for reflectance.

The percentage of incident solar radiation reflected back by an object is called albedo. Surface objects with a high albedo reflect more solar energy and appear as bright objects in a visible/near-infrared image. Objects with a low albedo reflect only a small portion of incident solar radiation and appear as dark objects on a visible/near-infrared image.

Figure 1 (modified from Hapke 1993) summarizes how variations of reflectance depend upon the degree of collimation:

AVHRR channels 1 and 2 measure the upwelling radiance at the top-of-atmosphere (TOA) emanating from both the Earth surface and atmospheric scattering. TOA radiance is converted to a TOA BRF using the the exo-atmospheric solar irradiance, Earth-Sun distance, solar zenith angle and spectral response functions of the AVHRR channels. (See Figure 6 for a summary of solar and satellite angles used in processing AVHRR Polar Pathfinder products.) To obtain surface albedo from TOA BRF, an atmospheric correction is applied, followed by a conversion from directional reflectance to hemispherical reflectance.

Thermal remote sensing
All objects radiate energy according to their blackbody temperature. A blackbody is a hypothetical object that absorbs all incoming thermal energy, but with none of that energy reflected or transmitted. Since no object can ideally absorb 100 percent of incident energy, a blackbody serves as a comparative measure of thermal emission. As the temperature of an object increases, the total amount of the emitted energy also increases, and the wavelength of that energy becomes shorter, as Wien's displacement law describes. According to the Stefan-Boltzman law, objects with a higher temperature give off more thermal energy per unit area than objects with a lower temperature.

Remote sensing in the thermal infrared spectral region (approximately 7 µm to 14 µm) involves measuring the radiance of objects. More specifically, thermal remote sensing measures the differences in the ability of objects to absorb shortwave energy and emit it back as longwave energy. Researchers are primarily interested in measuring the actual temperature of objects, rather than radiance. Radiance is simply a measure of the emitted energy of an object, while temperature is a measure of the kinetic (thermal) energy of an object. AVHRR channels 3-5 measure TOA brightness temperature. Please refer to the Polar Orbiter Data User's Guide for details on how radiance values are converted to surface temperature. Also refer to the Derivation Techniques and Algorithms section in this document for algorithms used in the AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites.

Sensor or Instrument Description

Source/Platform

The current series of NOAA Polar Orbiting Environmental Satellites (POES) has been operational since mid-1978. NOAA-9, -11, -12, and -14 satellite data were used for the AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites. Satellite identity is established on a per-pixel basis in each composite with the orbit mask and an associated information file. Temporal coverage is from 19 August 1993 through 31 December 1998 for the Northern Hemisphere, and from 1 April 1992 through 31 January 1996 for the Southern Hemisphere.

The primary instruments aboard this third generation of satellites (TIROS-N, and NOAA-6 through NOAA-15) are the AVHRR sensor and the TIROS Operational Vertical Sounder (TOVS).

The ascending portion of the orbit crosses the equator at local time in the afternoon (Figure 2). The satellites are placed in orbit so that the equator crossing time is about 1400 local solar time. However, as the satellite remains in orbit, the equator crossing time shifts to later in the afternoon.

Source/Platform Mission Objectives

The AVHRR sensor was originally designed for use as an imaging radiometer for meteorological purposes, rather than for quantitative radiometric sensing (Cracknell 1997). However, as new applications evolved, quantitative radiometric data became necessary. Channels 1 and 2 were designed to provide direct quasi-linear conversion between the 10-bit digital numbers and reflectance. The thermal channels were designed to provide this conversion between the digital numbers and the temperature in degrees Celsius (or Kelvin). The primary reason for the introduction of the five-channel system was the need for atmospheric correction calculations in determining sea surface temperature (Cracknell 1997).

Principles of Operation

The scan mirror collects earth observation data during a discrete part of the scan cycle. Energy from the scene is collected by a telescope and separated according to wavelength by beam splitters. Signals are amplified, filtered, and applied to the 10-bit analog/digital converter, which samples all five channels simultaneously. Wavelengths are summarized below:

• Channel 1: 0.58 µm to 0.68 µm (visible)
• Channel 2: 0.725 µm to 1.05 µm (reflected infrared)
• Channel 3: 3.55 µm to 3.92 µm (reflected/thermal infared)
• Channel 4: 10.3 µm to 11.3 µm (thermal infrared)
• Channel 5: 11.5 µm to 12.5 µm (thermal infrared)

The 10-bit resolution digital data is processed to create direct readout of High Resolution Picture Transmission (HRPT) data, Automatic Picture Transmission (APT) data, 4 km Global Area Coverage (GAC) data, and 1 km Local Area Coverage (LAC) data, to ground stations throughout the world (Cracknell 1997).

Sensor/Instrument Measurement Geometry

The AVHRR instrument scans in the across-track direction with a continuously rotating scan mirror, viewing a swath of over 100 degrees and up to 55 degrees off-nadir. Spatial resolution is approximately 1.1 km when the view is at nadir. Scanning to 55 degrees (68 degrees satellite zenith angle relative to the earth's surface) off nadir results in a ground resolution of over 2.4 km by 6.5 km at the maximum off-nadir position (Cracknell 1997).

Manufacturer of Sensor/Instrument

The first AVHRR sensor was designed and built by ITT Aerospace in 1976. Subsequent instruments were built by ITT Aerospace under contract with NASA, which procured the instruments on behalf of NOAA (Cracknell 1997).

Calibration

When the AVHRR Polar Pathfinder program began, calibrations were based on the NOAA Polar Orbiter Data (POD) User's Guide (Kidwell 1995). Since then, at least four different publications have presented different methods of calibrating AVHRR data. Even with extensive pre-launch testing and calibrations, satellite sensors change over time, and improved methods are developed. In some cases, particularly for the visible channels, the sensor characteristics are not fully predictable, and the satellite must be in orbit before changes can be detected to make the proper corrections.

Visible and Near Infrared Channels
The POD calibration method for channels 1 (visible) and 2 (near-infrared) used pre-launch values. The primary use of these channels has traditionally been for vegetation studies over land. Investigations revealed that the two channels drifted from the initial launch conditions. A set of time-varying coefficients was subsequently developed to correct for the drifting sensors, based on Rao and Chen (1994). In 1999, the calibration for NOAA-14 changed and new time-varying equations were recommended (Rao and Chen 1999). See Derivation Techniques section for more information.

Thermal Channels
At the start of the AVHRR Polar Pathfinder program, calibration of the thermal channels followed the guidelines in the POD. The methodology is as follows:

• Compute a linear slope and intercept based on the instrument count of space and internal blackbody.
• Apply the slope and intercept to calculate scene radiance.
• Convert the radiance to brightness temperature using the Plank functions.
• Apply a non-linear correction to the temperatures using a look-up table of corrections based on scene and blackbody temperature.

This method was optimized for a narrow range of AVHRR sea surface temperature products.

Walton et. al (1998) described an effective method for correcting AVHRR thermal channels 3, 4, and 5 to accommodate the wide range of temperatures in the polar regions. The NOAA/NASA Land Pathfinder group also selected this method, and it is used for the AVHRR Polar Pathfinder 1.25 km Data Set.

The Walton method applies a non-linear correction to the radiance, which is then converted to brightness temperature using a quadratic function with coefficients derived from pre-flight calibrations. The result, while not optimized for the narrower range needed for sea surface temperature measurements, appears to be an improvement for polar regions.

The Walton calibration method was implemented in March 1999 for thermal channels from all NOAA AVHRR satellites. Correction tables were generated for previously processed data to closely match the new calibration techniques. These corrections appear to be compatible to within 0.1 degrees.

Data Acquisition Methods

Figure 3 summarizes the steps in processing AVHRR Level 1b HRPT and LAC data, which were obtained from a variety of sources. All scenes are available from the AVHRR Polar 1 km Level 1b Data Set.

Derivation Techniques and Algorithms

Surface temperature, albedo, and cloud masking are all derived from the Cloud and Surface Parameter Retrieval (CASPR) system.

1. Channels 1 and 2

Calibration of the shortwave visible and near-infrared channels of the AVHRR (channels 1 and 2) is originally done according to Rao and Chen (1994), which corrects for sensor drift over time. Channels 1 and 2 are normalized for mean Earth-Sun distance. NOAA-14 visible channels were calibrated with an old set of calibration parameters, from 21 January 1995 through 15 August 1998. However, corrections were applied to the data set prior to distribution, and were based on methods from Rao and Chen (1999).

2. Channels 3-5

The calibration of the thermal channels (3-5) applies non-linear functions to the satellite sensor counts to obtain brightness temperatures, as described in Walton (1998). See the Calibration section for more information.

3. Skin Temperature

Refer to Figure 4. Two algorithms are employed to convert brightness temperature to skin temperature: one for high-latitude ocean and snow-covered land, and another for snow-free land (Key 1999). For retrieval of clear sky skin temperature, a simple regression model is used to correct for atmospheric attenuation. For high-latitude ocean and snow-covered land, the following equation applies:

T_S = a + bT₄ + c(T₄ - T₅) + d[(T₄ - T₅)( sec{q}- 1)]

where:

T_S = surface temperature
T₄ = brightness temperature in channel 4
T₅ = brightness temperature in channel 5
q = sensor scan angle
a, b, c, and d = regression coefficients

To determine the empirical relationship, radiosonde data from drifting ice and land stations in the Arctic and Antarctic were used with a radiative transfer model to simulate sensor brightness temperatures. Surface temperature retrieval methods for sea ice and snow, snow-free land, and open ocean are described in detail in Key et al. (1997). The coefficients are a function of temperature range, hemisphere, and the NOAA satellite that acquired the data. Algorithms are provided in the source code and header file.

Bare land is treated separately with:

T_S = a + bT₄ + cT₅ + e*emiss4 + f*emiss5

where:

emiss4 = surface emissitivity in channel 4 (0.985)
emiss5 = surface emissitivity in channel 5 (0.975)

4. Broadband Albedo

Refer to Figure 4. The retrieval of surface albedo involves four steps:

1. Normalize channels 1 and 2 with respect to solar zenith angle
2. Convert channels 1 and 2 narrowband reflectance to a broadband reflectance
3. Correct the TOA broadband reflectance for anisotropy
4. Convert the TOA broadband reflectance to a surface broadband albedo

The general methodology in steps #2-4 was used by Csiszar and Gutman (1999) for global land studies. The albedo provided here is a directional-hemispherical, apparent albedo, where apparent albedo is measured by up- and down-looking radiometers in the field.

Step 1: Normalize channels 1 and 2 with respect to the solar zenith angle:

P_1,toa= C₁ * cos(zen)
P_2,toa= C₂ * cos(zen)

where:

C₁ = percent reflectance for channel 1
C₂ = percent reflectance for channel 2
zen = solar zenith angle
p_1,toa = channel 1 reflectance
p_2,toa = channel 2 reflectance

Step 2: Convert the narrowband reflectances in channels 1 and 2 to a broadband reflectance. The narrow-to-broadband conversion takes the form:

p_toa = a + bp_1,toa + cp_2,toa

where:

p_1,toa = channel 1 reflectance
p_2,toa = channel 2 reflectance
p_toa = broadband TOA reflectance
a, b, c = regression coefficients

To develop the regression relationship, the radiative transfer model Streamer (Key and Schweiger 1998) is used to simulate the TOA reflectances over a broad range of viewing and illumination angles, atmospheric conditions, and surface types and albedos. Separate sets of coefficients are determined for different surface types. See the source code and header file.

Step 3: Correct for the dependence of the sun-satellite-surface geometry on reflectance. This is done with data presented in Suttles et al. (1988), who used Earth Radiation Budget Experiment (ERBE) and Geostationary Operational Environmental Satellite (GOES) data to determine TOA anisotropic reflectance factors (ARFs) for the broad shortwave band over various surfaces. To convert the directional reflectance to albedo, the ERBE/GOES ARFs are used:

a_toa = p_toa / f

where:

p_toa = reflectance observed at the sensor (simulated by Streamer in step 1)
f = anisotropic reflectance factor a_toa = TOA albedo. The f factor is derived from a tri-linear interpolation of two tables: rmatrx and albmn.

rmatrx: Anisotropic reflectance values based on ERBE and GOES. Dimension is (3,10,7,8) with three scene types, ten solar zenith bins, seven viewing zenith bins, and eight relative azimuth bins.

albmn: Normalizing factors for albmn. Dimension is (3,10) with three scene types and ten solar zenith bins.

Preliminary results show that, under certain circumstances, the reflectance of mixed pixels of open ocean and ice could have incorrect values. A channel 1 value less than 0.0 indicates open ocean, and a value greater than 0.3 is pure ice. Values between these limits indicate pixels with mixed surface types. The final value is a weighted average of the two. Algorithms are provided in the source code and header file.

Step 4: Finally, the apparent clear sky surface broadband albedo is estimated with a regression relationship of the form:

surface_albedo = (a_toa - a) / b

where  a and b are a function of water vapor, aerosol amount, and solar zenith angle. The coefficients were determined with Streamer for a variety of surface and atmospheric conditions.

For open ocean, a simpler approach is taken:

surface_albedo = a + b*a_toa + c*cos(zen) + d*pw + e*aertau

where:

a, b, c, d, e = coefficients determined empirically using modeled albedos
pw = precipitable water
zen = solar zenith angle
aertau = atmospheric aerosols

Details are provided in the source code and header file.

For both cases, the aerosol optical depth is set to 0.06. Also, for both cases, the water vapor is estimated from channels 4 and 5 using the formula:

PW = exp[b0 + b1(T4-T5) + b2(T5)] cos(theta)

where:

pw = precipitable water
theta = scan angle

b0, b1, b2 = coefficients determined over a range of surface temperatures and water vapor amounts using AVHRR radiances modeled with LOWTRAN-7. Arctic rawinsonde data were employed.

b0= -10.4974
b1= 0.751008
b2= 0.0453005

Details are provided in the source code and header file.

The calculated surface albedo is an apparent albedo, one that is measured with radiometers and which varies with changes in atmospheric conditions, particularly for bright surfaces.

5. AVHRR-Derived Surface Type Mask

The AVHRR surface type mask (amsk) file is created such that bit 7 is set to 1 (indicating valid AVHRR data) and, for valid AVHRR data, bits 6 and 3 contain the AVHRR-derived ice bit and the land bit, respectively. All other bits are set to 0. Invalid or missing AVHRR data is indicated by all bits set to 0. This amsk file then serves as input to the cloud mask script where bits 6 and 3 are copied first into the domain mask, and from there into the cloud mask. A technical description of processing is provided here.

6. Cloud Mask

The AVHRR 1.25 km cloud mask (cmsk) is based on input from a clear sky statistics file (stat_file), channels 1, 3, and 4 reflectance files (chn1, chn3rf, chn4, respectively), and a domain mask (dmsk). The algorithm reads the statistics file and creates a table of clear sky statistics for each domain. For each pixel in the grid, the dmsk value is copied to the cmsk value. If the dmsk value indicates missing data (255), no further processing is performed on this pixel. See the technical description of processing. Also see figure 5 for a flowchart of input masks and files used to process the cloud mask.

7. Orbit Mask and Information File

An ASCII information file (info) accompanies the orbit mask (omsk). The algorithm determines which AVHRR level 1b scenes were used to produce a given composite and assigns byte values to the orbit mask for each scene. A value of 0 indicates missing data. Values of 1 to 14 specify an orbit number. The info file lists the byte value codes found in the omsk file with the corresponding scenes from which the composites were derived. For example, the contents of the file 'app_n001_1997132_1400_info.v6' are as follows:

97132970512     -10
       1      a14_lcn_970512_0227.mask
       2      a14_lcn_970512_0409.mask
       3      a14_ngc_970512_0047.mask
       4      a14_ngc_970512_1114.mask
       5      a14_ngc_970512_1254__a14_tms_970512_1240.mask
       6      a14_ngc_970512_1436__a14_tms_970512_1424.mask
       7      a14_ngc_970512_1616.mask
       8      a14_ngc_970512_2113.mask
       9      a14_pas_970512_1748.mask
      10      a14_tms_970512_0239.mask
      11      a14_tms_970512_0920.mask
      12      a14_tms_970512_1059.mask

The first line is a header line consisting of the date in both julian and calendar formats and an hour offset. Each of the following lines contains a byte value from the omsk, followed by the name of the corresponding AVHRR Level 1b scene. Lines with two scenes indicate a "stitched" scene where two scenes that were acquired on the same orbit were stitched together to make one big scene. Thus, every pixel in the omsk file having the value 1 was taken from the scene 'a14_lcn_970512_0227', and every pixel having the value 5 was taken from either 'a14_ngc_970512_1254' or 'a14_tms_970512_1240'.

8. Time Mask

An acquisition time is provided for each pixel. The time is stored in an 8-bit (1-byte) integer grid as hour (UTC), scaled by ten. Thus, the acquisition time is known to a precision of ± 0.05 hours (± three minutes) for each pixel.

9. Ice Motion

Measurement of sea ice movement is accomplished with frequent repeat coverage of remotely-sensed imagery. The AVHRR sensor, with its daily repeat coverage and overlap of passes, provides researchers with the capability to track changes in ice movement over time. Ice motion computed from satellite imagery represents the displacement between the acquisition times of two images with the same spatial coverage. Researchers identify a feature (such as an ice floe) on two registered images and measure its pixel displacement. Ice velocity vectors are computed based on the pixel resolution and time span between images. A more automated method is to measure the correlation of groups of pixels between image pairs. A small target area in one image is correlated with several areas of the same size in a search region of the second image. The displacement of the ice is then defined by the location in the second image where the correlation coefficient is the highest. This spatial correlation method is used in producing the ice motion vectors included in the AVHRR Polar Pathfinder 1.25 km Twice-Daily EASE-Grid Composites data set. This approach is generally valid over short distances away from ice edges where the ice is less constrained. Spatial correlation methods cannot, however, find matches between images where a complete knowledge of ice dynamics is needed; for example, in areas where ice is deforming or in the ice margins near the open ocean where ice can deform and rotate (Emery, Fowler, and Maslanik 1995).

Two successive AVHRR satellite images are navigated to a Lambert azimuthal projection. AVHRR channels 2 (daylit) and 4 (dark) are used to compute ice motion vectors, based on a maximum cross-correlation (MCC) technique. The algorithm locates the maxima of two-dimensional cross correlations in windowed portions of the satellite images, which represent the end points of motion vectors that originate at the center of the window in the search area. The first and second image are subdivided into smaller square arrays ("search windows") of 15 pixels by 15 pixels. The search window is that portion of one image within which the MCC is computed by moving about a smaller template window from the other image. The overall size of this search window, along with the interval between the images, sets the magnitude of the ice velocities calculated by the MCC algorithm. The result of applying the MCC method to every window pair is an array of displacement vectors that represents the field of motion over the time interval between the two images (Emery et al. 1991, Ninnis, Emery, and Collins 1986).

10. Reduced-Resolution Products

To assist users in selecting 1.25 km data, or for applications that require a lower data volume product, reduced-resolution data are available for the five AVHRR channels, clear sky surface broadband albedo and skin temperature, average albedo and temperature, valid fraction file, solar zenith angle, satellite elevation angle, sun-satellite relative azimuth angle, surface type mask, cloud mask, cloud fraction files, and Universal Coordinated Time (UTC) of acquisition. These products are subsampled to a grid resolution of 25 km by 25 km. Please see the AVHRR Polar Pathfinder Twice-Daily 25 km EASE-Grid Composites documentation for details and ordering.

11. Discussion of Broadband Albedo and Skin Temperature Algorithms

These algorithms are appropriate for clear-sky conditions only. Nonetheless, the algorithms have been applied to all pixels, not just to pixels flagged as clear-sky. The reasoning for this is three-fold: first, the cloud detection algorithm continues to evolve and improve, so different versions of the cloud mask can be provided to users without negating the value of the surface products for clear-sky pixels. Second, users are encouraged to apply their own cloud detection procedures (including manual interpretation), which can be tuned to maximize performance for specific regions and case studies. These custom cloud masks can then be applied to the AVHRR Polar Pathfinder 1.25 km skin temperature and surface albedo product, eliminating the need for users to implement their own surface product algorithms. Third, some users may wish to investigate the usefulness of the surface products under marginal cloud conditions that may be detected by AVHRR as being cloudy, but which may actually consist of very thin cloud or diamond dust precipitation. Since the CASPR surface algorithms do not work properly for cloudy pixels, the AVHRR Polar Pathfinder 1.25 km clear-sky skin temperature and albedo grids contain inaccurate values. As noted above, users need to apply the cloud mask provided in this data set or use their own cloud detection method.

Evaluation of the CASPR surface products and the cloud detection approach is ongoing, and has identified several shortcomings of the existing products. The CASPR algorithms are optimized for performance over sea ice. Tests indicate that performance of the standard algorithm over ice sheets warrants improvement. The accuracy of the model simulations that form the basis of the CASPR albedo algorithm decreases with low solar zenith angles (e.g., the sun low on the horizon). Thus, albedos calculated in spring and autumn are likely to be less accurate than those for periods with higher sun angles. Also, parameterizations used to address bidirectional reflectance are not equally representative of all snow conditions for a full range of sun and satellite viewing angles.

The performance of the skin temperature algorithms was evaluated using surface observations from an ice camp in the Beaufort Sea north of Alaska during March and April, 1992, and from an annual cycle of data from a land site near Barrow, Alaska (Key et al. 1997). The bias and RMSE at the ice camp were -0.30 degrees and 0.61 degrees, respectively. At the land site, the bias and RMSE were -0.97 degrees and 4.70 degrees, respectively.

The surface type used to calculate broadband albedo is the same as that used to calculate skin temperature. Values for albedo are neglected where the solar zenith angle is greater than or equal to 85 degrees. An annual cycle of data from Barrow, Alaska was used to validate the broadband albedo (Key et al. 1997). The bias and RMSE are 0 percent and 17 percent, respectively, at the land site. However, errors are expected to be smaller over ocean, sea ice, and snow.

Processing Steps

Figure 3 is a flowchart that summarizes the generation of AVHRR 1.25 km Polar Pathfinder products.

1. Calibration and Navigation

The level 1b data must be calibrated and navigated (geolocated) to the EASE-Grid. Because the data were obtained from several sources and different calibrations were performed, the calibrations were redone to conform to the most current methods. Satellite ephemeris data, rather than level 1b embedded coordinates, are used to determine true latitude and longitude. This ensures that common methods are used throughout the full data set. Calibration yields raw TOA reflectance values for channels 1 and 2, and TOA brightness temperatures for channels 3-5. Calibrated data are then navigated to earth coordinates using an orbital ephemeris model with orbit corrections (Rosborough et al. 1994). Satellite ephemeris data are obtained from the Naval Space Surveillance Center (NAVSPASUR). An inverse navigation approach is used to assign pixels to individual grid cell locations. With this approach, the geographic latitude and longitude locations of the grid are mapped to the corresponding scan line and sample in the input data. No interpolation or averaging in time or space is applied to the resulting gridded data. Geolocation accuracy is typically 2 to 3 km for the 1.25 km processing; however, geolocation errors can be as large as 10 to 12 km.

As part of the navigation step, a set of viewing and illumination angles is provided for each pixel. These angles can be used to investigate angular effects, and to test or apply alternative bidirectional reflectance distribution function adjustments for derived products. All angles are stored in a 16-bit integer as degrees times ten. Thus, each angle is known to a precision of ± 0.05 degrees for each pixel. Solar zenith angle is the angle of the sun from the vertical. Relative azimuth angle is the absolute value of the difference of the solar azimuth angle and the satellite azimuth angle. Satellite elevation angle is the angle of the satellite above the horizon. See Figure 6 for a graphical summary of these angles.

As a part of the navigation step, an acquisition time is provided for each pixel. Time is stored in an 8-bit (1-byte) integer grid as hour (UTC), scaled by ten. Thus, the acquisition time is known to a precision of ± 0.05 hours (± 3 minutes) for each pixel.

2. Composites

Level 1b data from the AVHRR Polar 1 km Level 1b Data Set are loaded from tapes for a single processing day. Swaths with overlapping acquisition times are "stitched" together into a single swath. There are typically 14 satellite passes each day. At the higher latitudes, there is multiple coverage at each ground location. Data availability is determined by the location and distribution of receiving stations. Both poles are within the data swath during each orbit. Available scenes from each orbit are combined into twice-daily composites. Data are extracted from navigated imagery to match a set of decision criteria based on the satellite scan angle and the acquisition time in relation to "target times." These criteria are selected to choose data that minimize scan angles (to reduce algorithm errors due to atmospheric path length and bidirectional reflectance). The selected data for the five AVHRR channels are then assembled into two composites per day, for local times of 0200 and 1600 for the Southern Hemisphere, and 0400 and 1400 for the Northern Hemisphere. The precise local time for all of the cells in each grid is within three hours of the target time, and all of the cells are acquired with scan angles less than 46 degrees.

In general, compositing is similar for the 1.25 km and 5 km products. However, relative to the GAC data on which the 5 km products are based, the 1.1 km LAC and HRPT coverage is sparser and more variable. To maximize the higher-resolution coverage, scan angle and local-time-proximity requirements are relaxed to 46 degrees and three hours, respectively, for the 1.25 km compositing. Where every orbit of a region is available, the pattern is identical to the 5 km compositing. However, where swaths are missing, data are partially compensated for by missing data is made by including data with scan angles as great as 46 degrees and with local time differences (from the composite's target local time) of as much as three hours.

NOTE: The orbital data are not binned and averaged as with some polar satellite data, such as SSM/I. This would have made algorithms for cloud masking, albedo, and surface temperatures much more difficult, if not impossible, to implement.

3. Albedo and Surface Temperature

Described in Algorithm section.

4. AVHRR-Derived Surface Type Mask

Described in Algorithm section.

5.  Cloud Mask

Described in Algorithm section.

6. Time Mask

Described in Algorithm section.

7. Orbit Mask and Information File

Described in Algorithm section.

8. Ice Motion

Described in Algorithm section.

9. Latitude/Longitude Values

Grids of latitude and longitude coordinates are provided and represent the estimated center location of each 1.25 km pixel. Each value is stored in a 16-bit (2-byte) integer grid as degrees, scaled by 100. The resulting image is organized with the same dimensions as the Northern and Southern Hemisphere grids. For each user order, a separate set of grids is provided for latitude and longitude.

Processing Changes

The AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites consist of several versions. The current version number for the AVHRR 1.25 km composites is Version 6.

Version 2: Corrections to start of acquisition time errors from level 1b data

Version 3: Changes in methods for correction of V2 time problem

Version 4: Changes in thermal calibration and methods for correcting V3 time problem for NOAA-12 data

Version 5: Changes in NOAA-14 visible channel calibration, surface type determinations, and surface albedo algorithm

Version 6: Corrections to thermal and visible calibration. All data are currently delivered in this version.

Calculated Variables

The AVHRR Polar Pathfinder Twice-Daily 1.25 km EASE-Grid Composites contain the following parameters:

• Channel 1 top of the atmosphere (TOA) reflectance
• Channel 2 TOA reflectance
• Channel 3 TOA brightness temperature
• Channel 4 TOA brightness temperature
• Channel 5 TOA brightness temperature
• Clear sky surface broadband albedo
• Clear sky surface skin temperature
• Solar zenith angle
• Satellite elevation angle
• Sun-satellite relative azimuth angle
• Surface type mask
• Cloud mask
• Orbit mask and information file
• Universal Coordinated Time (UTC) of acquisition
• Ice motion

To assist users in selecting 1.25 km data, or for applications that require a lower data volume product, reduced-resolution data (25 km data, derived from the 1.25 km data) are available for broadband albedo, clear sky skin temperature, orbit mask and information file, and cloud mask. NSIDC also distributes the AVHRR Polar Pathfinder Twice-Daily 5 km EASE-Grid Composites (with nearly 20 years of data), along with reduced-resolution data (25 km) derived from the 5 km data. Ice motion products consisting of u- and v-component ice motion vectors are also available.

5. References and Related Publications

Cracknell, A.P. 1997. The Advanced Very High Resolution Radiometer. London: Taylor & Francis.

Csiszar, I. and G. Gutman. 1999. Mapping global land surface albedo from NOAA AVHRR. Journal of Geophysical Research 104(D6):6215-6228.

Diner, D.J., J.V. Martonchik, C. Borel, S.A.W. Gerstl, H.R. Gordon, Y. Knyazikhin, R. Myneni, B. Pinty, M.M. Verstraete. 1999. MISR level 2 surface retrieval Algorithm Theoretical Basis. Jet Propulsion Laboratory.

Emery, W.J., C.W. Fowler, and J.A. Maslanik. 1995. Satellite remote sensing of ice motion, in Oceanographic Applications of Remote Sensing, M. Ikeda and F.W. Dobson eds. Boca Raton: CRC Press, pp. 367-379.

Emery, W.J., C.W. Fowler, J. Hawkins, and R.H. Preller. 1991. Fram Strait satellite image-derived ice motions. Journal of Geophysical Research 96(C3): 4751-4768.

Goodison, B.E. 1989. Determination of areal snow water equivalent on the Canadian prairies using passive microwave satellite data. IGARSS '89 Proceedings 3:1243-1246.

Gustafson, G.B. et al. 1994. Support of Environmental Requirements for Cloud Analysis and Archive (SERCAA), Phillips Laboratory, Hanscom Air Force Base, Scientific Report No. 2, PL-TR-94-2114, 100 pp.

Hutchinson, T.A. and T.A. Scambos. 1997. High-resolution polar climate parameters derived from 1-km AVHRR data. Proceedings of the Eighth Symposium on Global Climate Change Studies, American Meteorological Society, Long Beach, CA, pp. 284-289.

Hapke, B. 1993. Theory of reflectance and emittance spectroscopy. Cambridge: Cambridge University Press.

Key, J. 1999. The cloud and surface parameter retrieval (CASPR) system for polar AVHRR. Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison.

Key, J. and A.J. Schweiger. 1998. Tools for atmospheric radiative transfer: Streamer and FluxNet. Computers and Geosciences 24(5):443-451.

Key, J., J. Collins, C. Fowler, and R.S. Stone. 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of the Environment 61:302-309.

Kidwell, K.D. 1995. NOAA Polar Orbiter Data User's Guide, U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, NESDIS.

Maslanik, J.A., J. Key, C. Fowler, T. Nguyen, 2000. AVHRR-derived regional cloud and surface conditions during SHEBA and FIRE-ACE. Journal of Geophysical Research. In press.

Maslanik, J., C. Fowler, J. Key, T. Scambos, T. Hutchinson, and W. Emery, 1998. AVHRR-based Polar Pathfinder products for modeling applications. Annals of Glaciology 25:388-392.

Meier, W.N., J.A. Maslanik, J.R. Key, and C.W.W. Fowler. 1997. Multiparameter AVHRR-derived products for Arctic climate studies, Earth Interactions, Vol. 1.

Ninnis, R.M., W.J. Emery, and M.J. Collins. 1986. Automated extraction of pack ice motion from advanced very high resolution radiometer imagery. Journal of Geophysical Research 91(C9): 10,725-10,734.

Rao, C.R.N. and J. Chen. 1999. Revised post-launch calibration of channels 1 and 2 of the Advanced Very High Resolution Radiometer on board the NOAA-14 spacecraft. on-line version

Rao, C.R.N. and J. Chen. 1994. Post-launch calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer on NOAA-7, -9, and -11 Spacecraft. NOAA Technical Report NESDIS 78, 22 pp.

Rao, C.R.N. 1993. Nonlinearity corrections for the thermal infrared channels of the AVHRR: Assessment and recommendations. NOAA Technical Report, NESDIS 69, 31 pp.

Rosborough, G.W., D.G. Baldwin, and W.J. Emery. 1994. Precise AVHRR image navigation. IEEE Transactions in Geosciences and Remote Sensing 32(3):644-657.

Saunders, R.W. and K.T. Kriebel. 1988. An improved method for detecting clear sky and cloudy radiances from AVHRR data. International Journal of Remote Sensing 9(1):123-150.

Schweiger, A., C. Fowler, J. Key, J. Maslanik, J. Francis, R. Armstrong, M.J. Brodzik, T. Scambos, T. Haran, M. Ortmeyer, S. Khalsa, D. Rothrock, and R. Weaver. 1999. P-Cube: A multisensor data set for polar climate research. Proceedings on the 5th Conference on Polar Meteorology and Oceanography, American Meteorological Society, Dallas, TX, 15-20 Jan., 136-141.

Schweiger, A.J. and J.R. Key. 1992. Arctic cloudiness: Comparison of ISCCP-C2 and Nimbus-7 satellite-derived cloud products with a surface-based cloud climatology. Journal of Climate 5(12):1514-1527.

Stowe, L.L., E.P. McClain, R. Carey, P. Pellegrino, and G.G. Gutman. 1991. Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data. Advances in Space Research 11(3): 51-54.

Stroeve, J.C., J.E. Box, C. Fowler, T. Haran, and J. Key. 2001. Intercomparison between in Situ and AVHRR polar pathfinder-derived surface albedo over Greenland. Remote Sensing of the Environment 75(3) (March): 360-374.

Stroeve, J.C. 2002. Assessment of Greenland albedo variability from the AVHRR Polar Pathfinder data set. Journal of Geophysical Research 33: 989-1034.

Suttles, J.T., R.N. Green, P. Minnis, G.L. Smith, W.F. Staylor, B.A. Wielicki, I.J. Walker, D.F. Young, V.R. Taylor, and L.L. Stowe. 1988. Angular radiation models for Earth-Atmosphere system. Volume I-Shortwave radiation, NASA Reference Publication 1184, 144 pp.

Walton, C.C., J.T. Sullivan, C.R.N. Rao, and M.P. Weinreb. 1998 Corrections for detector nonlinearities and calibration inconsistencies of the infrared channels of the advanced very high resolution radiometer. Journal of Geophysical Research 103(C2):3323-3337.

Yamanouchi, T., K. Suziki, and S. Kawaguchi. 1987. Detection of clouds in Antarctica from infrared multispectral data of AVHRR. Journal of Meteorological Society Japan 65(6):949-961.

6. Document Information

Acronyms and Abbreviations

The following acronyms and abbreviations are used in this document.

Document Creation Date

November 2000

Document Revision Date

January 2003, February 2001

Document Review Date

November 2000

Document URL

http://nsidc.org/data/docs/daac/nsidc0065_avhrr_1.25km.gd.html