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[CIDC FTP Data]
[ Seaice IDC Data on FTP]
Data Access
SMMR & SSM/I Sea Ice Concentration Data
[rule]
Readme Contents
(Based on Cavalieri et al., 1984 & 1997, and NSIDC 1995 User's Guide)
Data Set Overview
Sponsor
Original Archive
Future Updates
The Data
Characteristics
Source
The Files
Format
Name and Directory Information
Companion Software
The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data
Contacts
Points of Contact
References
[rule]
Data Set Overview
Sea ice plays an important role in the global climate system. It
serves as an effective insulator between the ocean and the
atmosphere, restricting exchanges of heat, mass momentum, and
chemical constituents. Multi-channel passive microwave radiance
measurements made from number of satellites are used to map,
monitor and study the Arctic and Antarctic polar sea ice covers.
The monthly averaged sea ice concentration data set presented here
is generated from observations made by four different space borne
microwave imagers. The data set spans over 18 years(1978-1996),
starting with the Scanning Multichannel Microwave Radiometer
(SMMR) on NASA Nimbus 7 in 1978 and continuing with the Defense
Meteorological Satellite Program (DMSP) Special Sensor
Micro-wave/Imager (SSM/I) series beginning in 1987.
Sponsor
The production and distribution of this data set are being funded
by NASA's Earth Science enterprise. The data are not copyrighted;
however, we request that when you publish data or results using
these data please acknowledge as follows:
The authors wish to thank the Distributed Active Archive
Center (Code 902) at Goddard Space Flight Center,
Greenbelt, MD, 20771, for producing the data in its
present format and distributing them. The original data
products were produced by NASA Team :Drs. Cavalieri,
Parkinson, Gloersen, and Zwally of the Oceans and Ice
Branch (code 971), Laboratory for Hydrospheric
Processes, NASA Goddard Space Flight Center in
Greenbelt. Goddard DAAC's share in these activities was
sponsored by NASA's Earth Science enterprise.
Original Archive
The monthly averaged sea ice concentration data was acquired from
the Laboratory for Hydrospheric Processes, Ocean and Ice Branch at
NASA Goddard Space Flight Center. The original data can be
obtained also from the National Snow and Ice Data Center (NSIDC).
The original data was on a polar stereographic projection with
grid elements of approximately 25x25 km. The Goddard DAAC has
resampled the data on 1x1 degree equal angle projection and
combined the data sets provided separately for North and South
polar regions into one file (filling the uncovered regions near
the equator with the masking code) for the comaptibility with the
other climate datasets in the interdisciplinay data collection.
Future Updates
The Goddard DAAC will update this data set as new data are
processed and made available at NSIDC.
The Data
Characteristics
* Parameters: Sea ice concentration (fraction of grid cell area
which is covered by sea ice) expressed as a percent.
* Units: percent ( x 10 )
* Typical Range: 0 to 1000
* Temporal Coverage: October 1978 through December 1996
Oct 78 - Aug 87 ... Nimbus-7 SMMR
Sept 87 - Dec 91 ... DMSP-F8 SSM/I
Jan 92 - Sept 95 ... DMSP-F11 SSM/I
Oct 95 - Dec 96 ... DMSP-F13 SSM/I
* Temporal Resolution: Monthly means
* Spatial Coverage: Global
* Spatial Resolution: 1 degree x 1 degree
Source
Multichannel Passive-Microwave Satellite Data Sets
For the purpose of providing a consistent long term
ice-concentration data set, brightness temperature data
from the SMMR and SSM/I sensors mapped on to a common
(the SSM/I) north and south polar grids were obtained
from NSIDC( NSIDC, 1995). The NASA Team Algorithm
(Cavalieri et al. 1984, Gloersen and Cavalieri 1986)was
used to calculate sea ice concentrations from brightness
temperatures derived from Scanning Multichannel
Microwave data, and a common land mask, recently updated
for the SSM/I grids (Martino et al., 1995), was applied.
The four satellite data sets employed and the periods
for which the data used are: the Nimbus 7 SMMR from
October 25, 1978 through August 20, 1987, the DMSP-F8
SSM/I from July 9, 1987 through December 18, 1991 (with
the exception of the data gap from December 3, 1987
through January 12, 1988), the DMSP-F11 SSM/I from
December 3, 1991 through September 30, 1995, and the
DMSP-F13 SSM/I starting from October 1995. A single
channel and two other multichannel passive microwave
satellite imagers flown in the 1970s, but not included
here, are the Nimbus 5 ESMR, the Nimbus 6 ESMR and the
SeaSat SMMR respectively. The Nimbus 5 ESMR was not used
because of the lack of overlap data with the Nimbus 7
SMMR, while the Nimbus 6 ESMR was omitted because of the
poor quality of the data. The SeaSat SMMR was omitted
because of not providing adequate coverage of the polar
regions.
Nimbus 7 SMMR
Descriptions of the SMMR instrument design, the
operating characteristics, and the procedures used to
obtain calibrated brightness temperatures and sea ice
concentrations are given by Gloersen et al. (1992). The
Scanning Multichannel Microwave Radiometer operated on
NASA's Nimbus 7 satellite for more than eight years,
from 24 October 1978 to 20 August 1987, transmitting
data every other day. Intended to obtain ocean
circulation parameters such as sea surface temperatures,
low altitude winds, water vapor and cloud liquid water
content on an all weather basis, the SMMR is a ten
channel instrument capable of receiving both
horizontally and vertically polarized radiation. The
instrument could deliver orthogonally polarized antenna
temperature data at five microwave wavelengths, 0.81,
1.36, 1.66, 2.8 and 4.54 cm. The antenna beam maintained
a constant nadir angle of 42 degrees, resulting in an
incidence angle of 50.3 degrees at Earth's surface. The
antenna was forward viewing and rotated equally +/- 25
degrees about the satellite subtrack. The 50 degree scan
provided a 780 km swath of the Earth's surface. Scan
period was 4.096 seconds.
Conversion of the raw radiometric readings to microwave
brightness temperatures involved correcting for actual
antenna patterns, including side lobe effects, as well
as separating out the horizontal and vertical
polarization components of each of ten channels of
radiometric data.
After launch, the prelaunch constants was updated by
checking against earth targets of known properties -
open, calm sea water with clear skies or light clouds,
and consolidated first-year sea ice. The brightness
temperatures were verified by comparison with brightness
temperatures obtained from airborne radiometer with all
SMMR channels during Nimbus 7 underflights. The
underflights were particularly important, since
extrapolation from the laboratory cold reference of 100
degrees Kelvin to the postlaunch value of 30 degrees K
cannot be done with complete confidence. The algorithm
to obtain sea ice concentration employs three of the ten
channels of the SMMR instrument: vertically and
horizontally polarized radiances at 18 GHz and
vertically polarized radiances at 37 GHz. Before
computing sea ice concentrations, isolated missing
brightness temperature pixels on the daily brightness
temperature maps were filled by spatial interpolation.
Larger areas of missing data were filled later by
temporal interpolation of the sea ice concentrations.
The corrections (Gloersen et al.1992), were made for a
long term drift in the SMMR data and for errors related
to ecliptic angle ( observed in the first 8.8 year data
set), full orbits of bad data, individual scans of bad
data, misplaced scans from the opposite node, and
misplaced scans from unknown origin. These were
identified by checking each daily image from both the
ascending node data and the descending node data. All of
the errors identified and considered to be sufficiently
serious to warrant exclusion were removed in the
ascending and descending node data sets separately
before averaging the data from the two nodes to provide
daily brightness temperature matrices. Finally,
additional corrections were applied to the three
channels (18 GHz H & V, 37 GHz V) of previously
corrected data used in the sea ice algorithm (Gloersen
et al. 1992), following a procedure similar to that
described in Gloersen et al. (1992), but with higher
precision. The 8.8 year drifts in these channels were
reduced to values well below the instrument noise values
given in Gloersen and Barath (1977) and lower than in
the previously corrected data.
DMSP-F8, F11, and F13 SSM/I
The DMSP Block 5D-2 F8 spacecraft flew in a near polar
sun-synchronous orbit. Launched on 18 June 1987, the
satellite accomplished 14.1 revolutions per day, with
the subsatellite ground track repeating approximately
every 16 days. F8 coverage ended 31 December 1991. The
DMSP F9 did not carry an SSM/I sensor, and the orbit of
the F10 did not allow for collection of useful polar
data, so the DMSP F11 was selected to provide the
follow-on data stream for the passive microwave polar
gridded time series. Launched on 28 November 1991, the
F11 also flew in a sun-synchronous orbit.
The SSM/I is a seven channel, four frequency, linearly
polarized, passive microwave radiometric system. The
instrument measures atmospheric/ocean surface antenna
temperatures at 19.3, 22.2, 37.0 and 85.5 GigaHertz
(GHz).
The instrument consists of a 24 x 26 inch offset
parabolic reflector fed by a corrugated, broad-band,
seven-port horn antenna. The reflector and feed are
mounted on a drum which contains the radiometers The
reflector-feed drum assembly is rotated about the axis
of the drum by a coaxially mounted Bearing And Power
Transfer Assembly (BAPTA).
The SSM/I sensor rotates at a uniform rate making one
revolution in 1.9 seconds, during which time the
satellite advances 12.5 km. The antenna beams are at an
angle of 45 degrees to the BAPTA rotational axis, which
is normal to the earth's surface. Thus, as the antenna
rotates, the beams define the surface of a cone, and,
from the orbital altitude of 833 km, make an angle of
incidence of the ground track aft of the satellite,
resulting in a scene swath width of 1394 km. The
radiometer outputs are sampled differently on alternate
scans. During the scene portion of the scans (Type A)
the five lower frequency channels are each sampled over
64 equal 1.6 degree intervals, and the two 85.5 GHz
channels are each sampled over 128 equal 0.8 degree
intervals, or approximately every 11 km along the scan.
During the alternate scans (Type B), only the two 85.5
GHz channels are sampled, at 128 equal intervals. Thus,
the five lower channels are sampled on an approximately
25 km grid along the scan and along the track. The two
85.5 GHz channels are sampled at 12.5 km both across and
along the track.
Coverage is global except for circular sectors centered
over the pole, 280 km in radius located poleward of 87
degrees North and 87 degrees South which are never
measured due to orbit inclination. The measurement
footprint size (effective field of view) is as follows:
19.3 GHz 70x45 km
22.2 GHz 60x40 km
37.0 GHz 38x30 km
85.5 GHz 16x14 km
[Scan Geometry]
SSM/I A/B Scan Geometry: Swath data consist of A/B scan
pairs. Each pair includes 256 scene stations (numbered).
Scene station numbers (parameter position numbers) are
indicated. Large circles signify all channels, small
circles stand for 85 GHz channels. Brackets indicate
scene stations lost due to antenna pattern correction.
For calibration, a small mirror and a hot reference
absorber are mounted on the BAPTA and do not rotate with
the drum assembly. They are positioned off-axis such
that they pass between the feed horn and the parabolic
reflector, occulting the feed once each scan. The mirror
reflects cold sky radiation into the feed, thus serving,
along with the hot reference absorber, as calibration
references for the SSM/I. This scheme provides an
overall absolute calibration which includes the feed
horn. Corrections for spillover and antenna pattern
effects from the parabolic reflector are incorporated in
the data processing algorithms.
The 85 GHz channels are considered experimental because
a passive microwave sensor with 12.5 km resolution has
never before been deployed on an orbital scanner.
Therefore these channels are not used in this analysis.
The SSM/I Sea Ice Algorithm Working Team (SSIAWT)
decided to retain as many of these data records as
possible, despite the anomalies that will be observed
within the 85 GHz grids. F11 antenna temperature values
are sometimes completely unphysical, ranging from 0 K to
650 K. The cause for this is unclear, but telemetry
errors are suspected. Data less than 50 K and greater
than 350 K are flagged and not processed.
The 4.5 year F8 19-37 GHz data were found to be free of
orbit dependent (ecliptic angle) brightness temperature
variations using a technique similar to what was used
for the SMMR data (Gloersen et al., 1992). based on the
F8 experience, the F11 SSM/I was presumed also to be
free of this defect. The drift determined by the method
used for the SMMR data over the 7 year SSM/I period
resulted in brightness temperature changes below or at
the instrument noise level for the SSM/I (see Table 1.4
in Hollinger, 1989), and was therefore considered to
have no significant impact on the computed sea ice
concentrations (less than 0.5%) either for consolidated
sea ice or at the ice edge, and so were ignored. A
comparison of instruments and of the differences in
orbital parameters (Abdalati et al. 1995) between the F8
and F11 using overlapping data indicated a high degree
of correlation (greater than 0.98) between the F8 and
F11 data sets. Small variations were attributed to the
different orbital characteristics of the two satellites,
especially to the differences in data collection times.
Table 1. Sensor and spacecraft orbital characteristics
of the sensors used in generating the sea ice
concentrations.
Parameter Nimbus-7 SMMR DMSP F8 DMSP F11 DMSP F13
Launch Time Oct 24, 1978 Jun 18, 1987 Nov 28, 1991 Mar 24, 1995
Nominal
Altitude 955 km 860 km 830 km 830 km
Inclination
Angle 99.3 degrees 98.8 degrees 98.8 degrees 98.8 degrees
Orbital
Period 104 minutes 102 minutes 101 minutes 101 minutes
Ascending
Node approximately
Equatorial approximately approximately 5:00 approximately
Crossing 12:00 noon 6:00 a.m. p.m.,drifted 5:00 p.m.
(local time) to 7 p.m.
Algorithm
frequencies 18.0 & 37.0 19.4 & 37.0 19.4 & 37.0 19.4 & 37.
(GHz)
3 dB Beam
width 1.6, 0.8 1.9, 1.1 1.9, 1.1 1.9, 1.1
(degree)
Earth
incidence 50.2 53.1 52.8 52.8
angle
Based on the analysis, a set of corrections have been
applied to F11 and F13 data to maximize consistency
between the data sets.
The Files
Format
This dataset contains monthly averaged global gridded sea ice
concentration estimates. Data in each file progresses from North
to South and from West to East beginning at 180 degrees West and
90 degrees North. Thus first point represents the grid cell
centered at 89.5 degree North and 179.5 West. Grids over land are
filled by masking code ( -9999).
Format
Data Files
* File Size: 259200 bytes, 64800 data values
* Data Format: IEEE floating point notation
* Headers, trailers, and delimiters: none
* Data Missing Code:
For latitudes above 84 degree North: -999.9
For regions where ice was not expected thus data was not
reported
All the land area over the globe: -9999.0
ocean in the low and mid lattitudes: -1.0
* Image orientation: North to South
Start position: (179.5W, 89.5N)
End position: (179.5E, 89.5S)
Name and Directory Information
Naming Convention:
The file naming convention for the Global land Precipitation
Dataset is
smmr_n07.seaice.1nmego.[yymm].ddd .. (Oct 78- Aug 87)
ssmi_f08.seaice.1nmego.[yymm].ddd .. (Sept 87- Dec 91)
ssmi_f11.seaice.1nmego.[yymm].ddd .. (Jan 92- Sept 95)
ssmi_f13.seaice.1nmego.[yymm].ddd .. (Oct 95- Dec 96) ..
where
smmr_n07 = Scanning Multichannel Microwave Radiometer on
Nimbus-7
ssmi_f08 = Special Sensor Microwave Imager on DMSP - F08
ssmi_f11 = Special Sensor Microwave Imager on DMSP - F11
ssmi_f13 = Special Sensor Microwave Imager on DMSP - F13
seaice = Sea ice concentration
1 = number of levels
n = vertical coordinate, n = not applicable
m = temporal period, m = monthly
e = horizontal grid resolution, e = 1 x 1 degree
go = spatial coverage, go = global (ocean)
yy = year
mm = month number
ddd = file type designation, (bin=binary, ctl=GrADS control
file)
Directory Path
/data/inter_disc/hydrology/sea_ice/yyyy
where yyyy is year.
Companion Software
Several software packages have been made available on the CIDC
CD-ROM set. The Grid Analysis and Display System (GrADS) is an
interactive desktop tool that is currently in use worldwide for
the analysis and display of earth science data. GrADS meta-data
files (.ctl) have been supplied for each of the data sets. A GrADS
gui interface has been created for use with the CIDC data. See the
GrADS document for information on how to use the gui interface.
Decompression software for PC and Macintosh platforms have been
supplied for datasets which are compressed on the CIDC CD-ROM set.
For additional information on the decompression software see the
aareadme file in the directory:
software/decompression/
Sample programs in FORTRAN, C and IDL languages have also been
made available to read these data. You may also acquire this
software by accessing the software/read_cidc_sftwr directory on
each of the CIDC CD-ROMs
The Science
Sea ice forms through the freezing of sea water over large areas
of the polar oceans in both hemispheres and covers as much as
30,000,000 km^2 of the Earth's surface. This large expanse of ice
greatly reduces the exchange of heat, mass, and momentum between
ocean and atmosphere and decreases the amount of solar radiation
absorbed at the surface. These processes depend strongly on time
and location because of the high temporal and spatial variability
of the sea ice cover in each hemisphere.
Sea Ice exists in regions that are dark for several months of the
year and very frequently cloudy in the remaining months. The
ability of microwave sensors to view the Earth's Surface under all
weather conditions, day or night, provides the opportunity to
obtain the required sea ice and ocean observations.
The two major types of sea ice that are known (Wilheit et al.,
1972; Gloersen et al., 1973; Campbell et al., 1974) to have
distinctly different microwave emissivities are first-year ice
(ice that is at least 30 cm thick but not undergone a melt season)
and multiyear ice (ice that has survived at least one melt
season). Also new and young ice (under 30 cm thick) are known to
have distinctly different microwave emissivities from first-year
ice (Ramseier et al., 1975; Gloersen et al., 1975; Cavalieri et
al., 1986; Grenfell and Comiso, 1986; Comiso et al., 1989).
However, the presence of these additional ice types within the
sensor field of view cannot be determined unambiguously and thus
contributes to the error of the calculated ice concentration.
Theoretical Basis of Data
The NASA Team Algorithm (Cavalieri et al, 1984)uses
three microwave channels in calculating sea ice
concentration. The channels are 19.4-GHz horizontally
(H) and vertically (V) polarized and the vertically
polarized 37.0-GHz. This algorithm is functionally the
same as the Nimbus 7 SMMR algorithm described by
Cavalieri et al. (1984) and Gloersen and Cavalieri
(1986). The radiances from each of the three channels
are first mapped onto polar stereographic grids (the
so-called SSM/I grid). The gridded radiances are then
used to calculate the grid values for the two
independent variables used in the algorithm. These are
the polarization (PR) and spectral gradient ratios (GR)
defined by
PR = [TB(19V)-TB(19H)]/[TB(19V)+TB(19H)] (1)
GR = [TB(37V)-TB(19V)]/[TB(37V)+TB(19V)] (2)
where TB is the observed brightness temperature at the
indicated frequency and polarization. From these two
parameters the first-year ice concentration (CF) and the
multiyear ice concentration (CM) are calculated from the
following equations:
CF = (a0 + a1PR + a2GR + a3PR * GR)/D (3)
CM = (b0 + b1PR + b2GR + b3PR * GR)/D (4)
where D = c0 + c1PR + c2GR + c3PR * GR (5)
The total ice concentration (CT) is the sum of the
first-year and multiyear concentrations
CT = CF + CM (6)
The coefficients ai, bi, and ci (i = 0, 3) are functions
of a set of nine TBs. These TBs, referred to as
algorithm tie points, are observed SSM/I radiances over
areas of known ice-free ocean, first-year (FY) sea ice,
and multiyear (MY) ice for each of the three SSM/I
channels. In addition to constraining the solutions to
concentrations between 0% and 100%, the algorithm also
sets the total ice concentration to 0% for those SSM/I
grid cells with GR values greater than preset
thresholds. This serves to reduce spurious ice
concentrations caused by weather-related effects over
ice-free ocean. This so-called weather filter is
discussed later.
SSM/I Tie Points
The selection of the SSM/I F8 tie points was based on an
analysis of SSM/I TBs, PR-GR distributions, histograms
of sea ice concentrations, and on comparisons with near
simultaneous measurements from the Nimbus-7 SMMR during
July and August 1987. The two sets of SSM/I tie points
(one for the Northern Hemisphere and one for the
Southern Hemisphere) represent a global set designed for
mapping global ice concentrations. While this global set
of tie points provides a uniform measure of sea ice
concentration on the large scale, improved accuracy is
obtained with the use of regionally selected tie points
(Steffen and Schweiger 1991). Please note that tie
points are the same for F8 and F11 data.
The ice-free (open water) tie points were chosen to be
near minimum ice-free ocean TBs (corresponding to near
maximum values of PR). By choosing near minimum TBs, the
PR range between open water and FY ice is about an order
of magnitude, permitting greater algorithm sensitivity
for detecting changes in ice concentration. Although the
Arctic and Antarctic open water tie points were selected
independently, the TB difference for corresponding
channels is no more than about 1 K .
The ice tie point selection was more difficult, since
the passive-microwave ice signatures depend on region
and season. This is particularly true of Arctic MY ice.
Even for a given region and season there is a certain
amount of random variability for a given ice type. Thus,
there is generally a range of TBs that could be used as
tie points. The series of SSM/I aircraft underflights
helped in this regard (Cavalieri et al. 1991). Mosaic
patterns covering several SSM/I image pixels were flown
in the central Arctic over a two-week period in March
1988. Although the mosaicked aircraft data did not
provide radiometric coverage at the SSM/I frequencies
and polarizations, it did provide a constraint on the
ice concentrations, which were calculated from passive
and active microwave imagery. This allowed adjustment of
the ice tie-points within the range of allowable values
to improve the accuracy to within a few percent
(relative to the aircraft data).
The need for different ice type tie points for the
Arctic and Antarctic results from the very different
environmental conditions of the two polar regions.
Indeed, the observed physical characteristics of
Antarctic sea ice are different from those in the Arctic
(Ackley et al. 1980, Wadhams et al. 1987), implying a
corresponding difference in microwave radiance
characteristics. In the Antarctic the two ice types
distinguished by the algorithm are identified as ice
type A and ice type B. Significant differences are found
between the Arctic first-year and Antarctic ice type A
tie points and between the Arctic multiyear ice and
Antarctic ice type B tie points. While ice temperature
differences may explain some of the observed tie point
differences for corresponding ice types, real emissivity
differences are reflected in the polarization and
spectral differences. In the Antarctic the radiometric
distinction between first-year (seasonal) ice and
multiyear (perennial) ice is lost. Unlike the Arctic,
where the predominant source of negative gradient ratios
is the volume scattering by the empty brine pockets in
the freeboard portion of multiyear ice, in the
Antarctic, the main source of volume scattering is from
sources other than multiyear ice. One very likely source
of volume scattering is the snow cover on the sea ice.
Snow cover of sufficient depth and of sufficiently large
grain size will mimic the microwave signature of
multiyear ice.
Weather Filter
A problem in mapping the polar sea ice covers in both
hemispheres has been the false indication of sea ice
over the open ocean and at the ice edge. These spurious
sea ice concentrations result from the presence of
atmospheric water vapor, nonprecipitating cloud liquid
water, rain and sea surface roughening by surface winds.
While these effects are relatively minor at polar
latitudes in winter, they result in serious weather
contamination problems at all latitudes in summer
(Cavalieri et al. 1992).
This problem was addressed for sea ice concentrations
derived from the Nimbus 7 SMMR data through the
development of a weather filter (Gloersen and Cavalieri
1986). The filter is based on the polarization (PR18)
and spectral gradient ratio (GR37/18) distribution of
ice-free and ice-covered seas. If GR(37/18) is greater
than 0.07, then the sea ice concentration is set to
zero. While this eliminates most of the unwanted weather
effects, it also eliminates sea ice concentrations less
than about 12% in FY ice regions and 8% in MY ice
regions. Applying GR(37/19) filters to SSM/I-derived sea
ice concentration maps is less successful because the
closer proximity of the 19.35 GHz SSM/I channels to the
center of the 22.2 GHz atmospheric water-vapor line
makes the 19.35 GHz channels more sensitive to changes
in atmospheric water vapor, resulting in greater
contamination problems.
A new composite weather filter has been developed
(Cavalieri et al. 1994) and implemented in the NASA Team
sea ice algorithm for routine processing of the SSM/I
data for generating sea ice concentration maps. The new
filter is a combination of the original SSM/I GR(37/19),
which effectively eliminates most of the spurious
concentration resulting from wind-roughening of the
ocean surface, cloud liquid water, and rainfall with
another GR filter based on the 22.2 GHz and 19.35 GHz
channels. The rationale for using GR(22/19) is based
partly on the sensitivity of the 22.2 GHz to water vapor
and partly on the need to minimize the effect of ice
temperature variations at the ice edge.
This new weather filter works as follows: If GR(37/19)
is greater than 0.05 and/or GR(22/19) is greater than
0.045, the sea ice concentration is set to zero. These
GR thresholds effectively eliminate most of the weather
contamination, except for winds greater than about 30
m/s, cloud liquid water more than 24 cm, water vapor
greater than 0.2 cm, and rain rates greater than 12
mm/hour. Except for a few case studies completed during
the development of this filter, the extent to which it
eliminates ice-edge concentrations in different regions
of the Arctic and Antarctic for different seasons is
unknown. Work is currently underway to determine the
overall effectiveness of the new SSM/I weather filter.
Algorithm Sensitivity
The sensitivity of the algorithm to random errors has
been described previously (Swift and Cavalieri 1985) for
the SMMR version of the algorithm. The sensitivity
analysis was redone using the SSM/I algorithm
coefficients. The results are presented in Tables 2 and
3 for the Arctic and Antarctic sets of tie points. The
sensitivity coefficients given in Table.2 were
calculated for regions of first year (FY) ice and
multi-year (MY) ice in the Arctic at three different ice
concentrations. This was repeated for the Antarctic with
ice type regions labeled A and B. Each coefficient
represents the uncertainty in concentration in units of
percent per 1 K uncertainty in TB.
These sensitivity coefficients given in Tables 2 and 3,
may be used to obtain an estimate of the error incurred
by variations in the radiometric properties of the ice
surface. For example, a random variation in ice
emissivity of 0.01 over 100% FY ice corresponds to a
variation in TB of 2.5 K (assuming a value of 250 K for
the physical temperature of the radiating portion of the
ice), which in turn corresponds to an error of 4.5%
(0.018 x 2.5) in total ice concentration, assuming all
three channels are subject to this variation.
Table 2. NASA SSM/I Algorithm Sensitivity Coefficients
for First-year and Multiyear Ice Regions of the Arctic
at Different Concentrations
First-year Ice
[ firstyear ice]
Multiyear Ice
[ multiyear ice]
*Each coefficient represents the uncertainty in
concentration in units of percent per 1-K uncertainty in
brightness temperature. The sensitivity of both the
total ice concentration(CT) and the multiyear ice
concentration (CMY) are given.
Table 3.NASA SSM/I Algorithm Sensitivity Coefficients'
for Ice Type A and Ice Type B Regions of the Antarctic
at Different Concentrations
Ice Type A Ice Type B
100% 50% 15% 100% 50% 15%
dCT dCT dCT dCT dCT dCT
dTB19H 1.2 0.9 0.9 1.2 0.8 0.6
dTB19V 0.3 0.1 0.5 0.3 0.1 0.4
dTB37V 0.8 0.8 0.9 0.8 0.8 0.8
Sqrt(Sum(dTB)2) 1.5 1.2 1.3 1.5 1.1 1.1
*Each coefficient represents the uncertainty in the
total ice concentration(CT) in units of percent per 1-K
uncertainty in brightness temperature.
The sensitivity of the calculated ice concentrations to
ice temperature variations is reduced through the use of
radiance ratios PR and GR (Cavalieri et al. 1984, Swift
and Cavalieri 1985). Except at the onset of melt, there
is no apparent correlation between PR and the increasing
TBs resulting from seasonal warming. This is not the
case for GR, which is correlated with the seasonal
variation in TB. An estimated error of 0.005 in GR
(Gloersen et al. 1992) corresponds to an uncertainty in
total ice concentration of about 1%, while the error in
MY ice concentration is about 9%. These estimated errors
are consistent with the results obtained from previously
published comparative studies (Cavalieri et al. 1991,
Steffen and Schweiger 1991).
Processing Sequence and Algorithms
The DMSP F8, F11 and F13 SSM/I data were obtained from
the National Snow and Ice Data Center (NSIDC) in
Boulder, Colorado. Data acquisition, filtering bad data,
handling geolocation errors, implementation of an
antenna pattern correction, and finally the swath to
grid conversion are all described in the NSIDC's User's
Guide (1995).
The data grids are in the polar stereographic
projection.
[Spatial Coverage Map ]
The polar stereographic projection often assumes that
the plane (i.e., the grid) is tangent to the Earth at
the pole. Thus, there is a one-to-one mapping between
the Earth's surface and grid (i.e., no distortion) at
the pole. Distortion in the grid increases as the
latitude decreases because more of the Earth's surface
falls into any given grid cell, which can be quite
significant at the edge of the northern SSM/I grid where
distortion reaches 31%. For the South Pole, the SSM/I
grid has a maximum distortion of 22%. To minimize the
distortion, it has been decided that the projection will
be true at 70 degrees rather than the poles. This will
increase the distortion at the poles by three percent
and decrease the distortion at the grid boundaries by
the same amount. The latitude of 70 degrees was selected
so that little or no distortion would occur in the
marginal ice zone. Another result of this assumption is
that fewer grid cells will be required as the Earth's
surface will be more accurately represented. This will
save about 100 megabytes per year in storage.
Calculation of Sea Ice Concentrations
The NASA Nimbus-7 SMMR Team Algorithm (Cavalieri et al.
1984, Gloersen and Cavalieri 1986) with revised tie
points (Gloersen et al. 1992, p. 27) was used to
calculate ice concentrations from the SMMR @ SSMI
brightness temperatures. Derived ice concentration grids
were stored separately for the northbound and southbound
(ascending and descending) orbital nodes. This was done
in view of the rapid changes known to take place in the
polar ice covers, and because of the nonlinear nature of
the sea ice algorithm. To be consistent with the
daily-averaged brightness temperatures derived from the
SMMR instrument, the northbound and southbound
(ascending and descending) ice concentration data were
averaged to produce a daily-averaged ice concentration
map for each SMMR data day (the SMMR sensor operated on
alternate days to conserve power). Be cautioned that
producing ice concentrations from the daily-averaged
brightness temperatures may yield slightly different
results than the ice concentration maps presented in the
published atlas.
Comparisons of sea ice concentrations calculated for
each of the sensors during overlap periods using
published algorithm tie-points reveal significant
differences. These differences may result from
differences in sensor and orbital characteristics,
differences in observation times (and therefore tidal
effects), and differences in algorithm coefficients.
Sensor and orbital characteristic differences for the
Nimbus 7 SMMR and DMSP SSM/I F8 include antenna beam
width, channel frequency, spacecraft altitude, ascending
node time, and angle of incidence. In addition, the sea
ice algorithm tie-points are significantly different.
The SSM/I F8 and F11 sensors also differ in ascending
node time, altitude, and angle of incidence. Because the
visit times of the three satellites occur during
different phases of the diurnal cycle, tidal effects may
result in differences in the ice distribution. It is
expected that any such effects would be mitigated by the
correction scheme described below. Table 1 summarizes
the sensor and orbital characteristic differences. These
differences are accommodated for each pair of sensors by
employing a self-consistent set of algorithm tie-points
determined through linear relationships between the
observed brightness temperatures during the overlap
periods.
Daily brightness temperature maps from the Nimbus 7 SMMR
and from the DMSP SSM/I F8 during their period of
overlap, July 9 - August 20, 1987, were compared for
both the Arctic and Antarctic. Unfortunately, there were
only 22 days of common coverage. A linear least squares
best fit of the cumulative data was obtained for each of
the corresponding channels. For the purpose of
eliminating spurious brightness temperatures resulting
from residual land spillover effects, an Arctic land
mask expanded 3 to 4 pixels out from the original land
mask was used in the determination of the best fit
between the two data sets. The eliminated pixels
represent only a very small fraction of the total number
of ice concentration pixels, but eliminating them helps
considerably in reducing the outliers on the scatter
plots. These linear relationships were used to generate
a set of SSM/I tie-points that are consistent with the
original SMMR sea ice algorithm tie-points (Gloersen et
al., 1992). The published SSM/I F8 tie-points (Cavalieri
et al., 1992) were not used. In addition to using these
transformations, the SSM/I F8 open water tie-points were
subjectively tuned to help minimize the differences
between the SMMR and SSM/I F8 sea ice extent and area
during the overlap period. In all cases except for the
Antarctic F8 values, the tuned amount is within one
standard error of estimate. It is suspected that the
reason for the larger tuned values results from greater
weather effects during the overlap period.
The period of overlap for F8 and F11 is even shorter
than that for Nimbus 7 and SSM/I F8, with only 16 days
of overlap of good data, from December 3-18, 1991. The
linear regression was performed for each of the
corresponding channels. The SSM/I F11 open water
tie-points were also tuned to help reduce differences in
ice extent and area as was done with the SSM/I F8
values. A further adjustment to the Antarctic 37V ice
type-B F11 tie-point was also made to reduce the ice
area difference. In this case, the amount of tuning
needed to reduce the ice extent and area differences
between the F8 and F11 values is well within one
standard error of estimate.
Land-to-Ocean Spillover Correction
The next step in preparing the data sets was the
correction for land-to-ocean spillover (often referred
to as "land contamination") and residual weather-related
effects. Land-to- ocean spillover refers to the problem
of blurring sharp contrasts in brightness temperature,
such as exist between land and ocean, by the relatively
coarse width of the sensor antenna pattern (Figure 1a).
This problem is of concern here because it results in
false sea ice signals along coastlines. (Land and ice
both have much higher brightness temperatures than
ocean.) The method used to reduce the spillover is an
extension of the method employed for the single-channel
Nimbus 5 Electrically Scanning Microwave Radiometer
(ESMR) data in Parkinson et al. (1987). The rationale
behind the approach is that a minimum observed
(generally in late summer) sea ice concentration in the
vicinity of coastlines where no ice remains offshore is
probably the result of land spillover and is thus
subtracted from the image. To reduce the error of
subtracting ice in areas of ice cover, the technique
searches for and requires the presence of open water in
the vicinity of the image pixel to be corrected.
Land-to-ocean spillover was reduced by the following
three-step procedure:
(1) A matrix M was created covering the entire grid and
identifying each pixel as land, shore, near-shore,
off-shore, or non-coastal ocean. This matrix M is
created once and then used throughout the data set.
(2) A matrix CMIN, to represent minimum ice
concentrations on a pixel-by-pixel basis throughout the
entire grid, was created for each instrument type. CMIN
was created by first constructing a matrix P containing
the minimum monthly average ice concentrations
throughout a given year, then adjusting that matrix at
off-shore, near-shore, and shore pixels. The CMIN matrix
was created once for SMMR and once for SSM/I, then used
throughout the data sets.
(3) The daily ice-concentration matrices for all three
data sets were adjusted at any off-shore, near-shore,
and shore pixels in the vicinity of open water. At any
time when the neighborhood of an off-shore, near-shore,
or shore pixel contains three or more open-water pixels
(i.e., ice concentration less than 15%), then the
calculated ice concentration at the off-shore,
near-shore, or shore pixel is reduced by the value for
that pixel in the matrix CMIN. Wherever the subtraction
leads to negative ice concentrations, the concentrations
are set to 0%. This land-spillover -correction algorithm
is clearly a rough approximation, as the contaminated
amount does not stay constant over time; but the scheme
has been found to reduce substantially the spurious ice
concentrations on the grids.
WeatherRelated Corrections
A correction for residual weather effects was made based
on monthly climatological sea surface temperatures
(SSTs) from the NOAA Ocean Atlas (Levitus and Boyer,
1994). These data, originally on a 2 by 2 degree grid,
were remapped onto the SSM/I grid. Because the SST data
did not extend to the SSM/I coastline, the data were
extrapolated to the coastline once regridded onto the
SSM/I grid. The SST maps were used as follows: In the
Northern Hemisphere, in any pixel where the monthly SST
is greater than 278 K, the ice concentration is set to
zero throughout the month; in the Southern Hemisphere,
wherever the monthly SST is greater than 275 K, the ice
concentration is set to zero throughout the month. The
higher threshold SST value was needed in the Northern
Hemisphere because the 275 K isotherm used in the South
was too close to the ice edge in the North. In a few
instances, corrections to the regridded SST data were
needed, because otherwise we were losing actual sea ice.
Filling Data Gaps
In each of the data sets, there are instances of missing
data. In some cases whole days (or weeks or months) are
missing. In other cases, large swaths or wedges of
missing data exist within an image, along with scattered
pixels of missing data throughout the grid. The
scattered pixels of missing data, resulting generally
from mapping the orbital radiance data to the SSM/I
grid, were filled by applying a spatial linear
interpolation scheme on the brightness temperature maps.
The larger areas of missing data, resulting from gaps
between orbital swaths (generally at low latitudes on
daily maps) or from partial coverage or missing days,
were filled by temporal interpolation on the ice
concentration maps. No data at all were available for
the period from December 2, 1987 through January 12,
1988. This gap was not filled by temporal linear
interpolation, instead being left as missing data. Table
4 lists the SSM/I dates containing bad data, which were
subsequently corrected through interpolation.
Table 4. SSM/I Days Containing Bad Orbits or Bad Scans
102/1992 103/1992 351/1992 323/1993 330/1993
331/1993 334/1993 346/1993 352/1993 357/1993
007/1994 009/1994 011/1994 012/1994 014/1994
017/1994 019/1994 022/1994 023/1994 025/1994
028/1994 031/1994 042/1994 167/1994 182/1994
350/1994 358/1994 003/1995 039/1995 059/1995
077/1995 081/1995 082/1995 086/1995 097/1995
105/1995 231/1995 232/1995
There are usually at least 14 days of coverage per
month, although major data gaps occur in August: in
August, 1982, the 4th, 8th, and 16th are missing for
both polar regions; in August, 1984, the 13th through
the 23rd are missing for both polar regions.
Resampling of data from Polar Stereo projection grid to
1x1 degree equal angle grid
The original data are provided on a rectangular grid
placed over a polar stereographic projection, with the
projection plane cutting the globe at a latitude of 70
degrees. On the north polar grid 31 degrees N is the
lowest latitude. The north polar grid size is 304 x 448
grid cells. The pole is located at x,y = 154, 234,
(referenced to x,y = 0,0 at the upper left corner), the
common corner of four adjacent pixels. In the southern
hemisphere, the projection is the same as the Northern
Hemisphere, with a grid size of 316 x 332, and the pole
located at x,y = 158, 174. The grid cells are 25 x 25 km
polar stereographic projection.
For inclusion in the Interdisciplinary data collection
the data are resampled onto a 1x1 degreee qual angle
grid(array dimension 360x180). For both North and South
hemisphere regions the locations of the grid centers are
transformed into geodetic latitude and longitude using
the Program Locate available in Fortran language
(NSIDC,95). North and South polar region data files are
merged in one file, data is binned onto 1x1 degree
grids,and finally the data is reoriented such that first
data points represents (89.5N,179.5W) In this process,
the masking of the original data is maintained. However
the coastal mask value '-10000' was assigned the land
mask '-9999'. The data from pole to 85 degree North is
assigned a fill value of -999.9. The scattered gaps
created in Arctic and Antarctic polar region due to grid
transformation are filled by spatial interpolation using
neighbouring points. If in a grid, fraction of sea
exceeded the land, averaged sea ice value was assigned.
The ocean near equator is assigned a value '-1' for the
fill value since no data was reported to differentiate
with the value '0' used by the data producer for the
polar sea regions which were investigated and no ice was
found.
The regridded data were visually examined to ensure
consistency with the original data.
Scientific Potential of Data
Passive microwave observations of polar oceans have
become essential to the tracking of ice edges, for
estimating sea ice concentrations, and for classifying
sea ice types. Global data, immediately practical for
use in shipping and petroleum development activities,
have broader implications from the standpoint of adding
to the meteorological foundations used in understanding
and modeling climate change.
Sea ice has many roles in the global climate system. For
one, it serves as an effective insulator between the
ocean and the atmosphere, restricting exchanges of heat,
mass momentum, and chemical constituents.
Brightness temperatures are used to derive sea ice
concentrations. Among the many possible applications for
sea ice concentrations, researchers use them to map ice
extent, actual ice area, and the amount of open water
within ice packs. The latter is used to monitor
occurrences, impact and persistence of polynyas, in the
calculation of heat and salinity fluxes between ocean
and the atmosphere in the polar regions.
Another important role is how it effects surface albedo.
The list below gives the albedo for varying sea
conditions:
Ice-free ocean 10% - 15% (Lamb, 1982)
Sea ice 80% (Grenfell, 1983)
Fresh snow cover on ice 98% (Vowinkel and Orvig,
1970)
Melt ponds on ice 20% - 60% (Grenfell and Maykut,
1977)
Sea also has a direct affect on oceanic circulations by
the rejection of salt to the underlying ocean during ice
growth. The result of this is an increase in the density
of the water directly under the ice, which produce
convections that tend to deepen the mixed layer. The
convection contributes to driving the thermohaline
circulation of the ocean (Bryon et al., 1975).
It has been used in producing ice edge product
(NSIDC,95) that could be integrated into a sea surface
temperature (SST) algorithm being developed as part of
the NOAA-NASA AVHRR Pathfinder (Oceans Group)
activities. The ice edge product would be used as a
filter, to mask known areas of sea ice from the AVHRR
SST retrieval algorithm and minimize any possible
contamination of an SST product by the presence of ice.
While a review of NSIDC-archived data sets determined
that no satisfactory digital sea ice product existed
that met those requirements, it was apparent that a
monthly averaged sea ice concentration product could be
generated from DMSP- F8 SSM/I daily sea ice
concentration grids. Monthly averaged sea ice
concentration grids would provide the necessary ice
extent information and could additionally be useful for
model comparisons and inputs.
Validation of Data
Errors in the derived sea ice concentrations arise from
several sources. Sources of sea ice concentration error
in decreasing order of importance are:
* inability of the algorithm to discriminate among
more than two radiometrically different sea ice
types,
* seasonal variations in sea ice emissivities,
* nonseasonal variations in sea ice emissivities,
* weather effects at ice concentrations greater than
8%-12%, and
* random and systematic instrument error.
The largest source of error is the inability of the
algorithm to discriminate among more than two
radiometrically different sea ice types (including
different surface conditions). The broad categories of
radiometrically different sea ice types are new and
young ice, FY ice, and MY ice types. Since the algorithm
allows for both FY and MY ice types, the largest source
of error in total ice concentration is caused by the
presence of newly forming sea ice. New and young ice,
most commonly found in leads and coastal polynyas during
winter, are characterized by polarization differences
intermediate between open water and thick FY ice
(Cavalieri et al. 1986). PR for thin ice will vary in
proportion to ice thickness (Grenfell and Comiso 1986)
and will increase in proportion to the fraction of new
ice filling the SSM/I field of view. For example, if it
is assumed that an FOV contains 10% new ice (PR = 0.14)
and 90% FY ice (PR = 0.03), then the increase in PR
results in an underestimate of about 10% in total ice
concentration. Larger areas of new ice within the sensor
FOV will result in proportionally larger underestimates
by the algorithm. Recently, a new thin ice algorithm has
been developed (Cavalieri et al. 1994) which mitigates
this problem in seasonal sea ice zones and also permits
the mapping of new and young ice types.
Seasonal variations in sea ice emissivities can be
extremely large. MY ice, for example, loses its
characteristic microwave spectral signature (negative
GR) during spring and summer and becomes
indistinguishable from FY ice. Another condition
resulting in large errors in total ice concentration is
the formation of melt ponds on the ice surface, making
the ponded region indistinguishable from open water.
While the areal extent of ponding is not well known,
unpublished data reported by Carsey (1982) show that for
the summer of 1975, 20% or less of the Arctic ice pack
was covered by ponds and that ponding reached maximum
areal extent in early July. For an area of the Beaufort
Sea (AIDJEX triangle) during August 1975, Campbell et
at. (1984) report that the average ponding was 30%. The
percent coverage of melt ponds varies spatially and
temporally across the Arctic and the extent to which
they influence summer ice concentrations remains
uncertain.
Nonseasonal variations in sea ice emissivity include
local variations, resulting from fluctuations in the
physical and chemical properties of sea ice, and
regional variations resulting from environmental
differences. Regional and hemispheric variability may be
considerable, as indicated by previous studies (Comiso
1983, Ackley 1979). Differences between Arctic and
Antarctic sea ice microwave signatures noted above
result in different sets of algorithm tie-points for
each hemisphere. Algorithm errors can be reduced by
using locally and seasonally chosen algorithm tie
points.
While weather effects resulting from atmospheric water
vapor, cloud liquid water, rain, and sea surface
roughening by near-surface winds on the calculated sea
ice concentrations are greatly reduced over open ocean
at polar latitudes by the algorithm weather filter
described previously, they may nevertheless contribute
to the sea ice concentration error at concentrations
greater than about 15%. Presuming that the atmospheric
contribution is nearly zero over consolidated FY ice and
that the contribution at the open water end results
totally from atmospheric effects estimated to be up to
15%, then the error resulting from atmospheric effects
for any intermediate concentration may be estimated by a
linear interpolation. While the effects of weather on
high total ice concentrations are small, there is the
potential for significant reductions in multiyear ice
concentrations (Maslanik 1992).
Finally, errors in ice concentration also result from
random and systematic instrument errors. Except for the
85-GHz channels, over the two years of SSM/I operation,
no instrument drifts are apparent. Based on prelaunch
measurements and on observed radiances over relatively
stable targets where temporal and spatial geophysical
variability is small, the error for each of the three
SSM/I channels used in the algorithm is less than 1 K,
and the absolute accuracy is estimated at 3 K (Hollinger
1989). Assuming a 1-K level of random instrument noise
in each channel, an upper limit to the rss uncertainty
in the calculated concentrations, which depends on
surface type and concentration, ranges from about 1% to
1.8% for total ice concentration and from 4.5% to 6% for
MY ice concentration.
Comparisons of aircraft SAR and ESMR mesoscale ice
concentration maps with concurrent SMMR maps have given
agreements to within 10%-20% (Campbell et al., 1987).
Comparison of data from the Airborne Multichannel
Microwave Radiometer (AMMR) transects within SMMR
footprints with SMMR data has yielded agreements within
10% for both total Sea ice and multiyear sea ice
(Gloersen and Campbell, 1988).
Contacts
Points of Contact
For information about or assistance in using any DAAC data,
contact
EOS Distributed Active Archive Center(DAAC)
Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)
To order the original Sea Ice Concentration data set, please
contact NSIDC DAAC:
National Snow and Ice Data Center
Campus Box 449
University of Colorado
Boulder, CO 80309-0449
303-492-6199 (voice)
303-492-2468 (fax)
For algorithm questions related to original data,please contact
the data producer:
Dr. Donald J. Cavalieri
Laboratory for Hydrospheric Processes
Ocean and Ice Dynamics Branch, Code 971
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: don@cavalieri.gsfc.nasa.gov
301-286-2444 (voice)
301-286-0240 (fax)
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