**A Data Fusion Algorithm for Mapping Sea-Ice Concentrations**

**from Special Sensor Microwave / Imager Data**

K.C. Partington

U.S. National Ice Center

4251 Suitland Road,

Washington DC 20395

USA.

# Abstract

Ice charts from the U.S. National Ice Center are used to evaluate
published algorithms for generating sea-ice concentrations from SSM/I
data. The same ice charts, in a form that includes information derived
only from RADARSAT, OLS and AVHRR data, are used in an operational
algorithm that effectively tunes a hybrid of the Bootstrap and NASA Team
algorithms and principal components of the SSM/I data to the time and
region associated with the ice chart. This "tuned" algorithm is then
used to interpolate ice concentrations elsewhere in the ice chart where
no cloud-free, high-resolution visible, infra-red or active microwave
satellite data are available. The algorithm is designed to operate in
near real time to assist the ice analysts in their otherwise
manually-intensive task of compiling ice charts for vessels operating in
ice-infested waters.

# Key words

Sea-ice, ice charts, SSM/I, passive microwave, data fusion, algorithm

# I. INTRODUCTION

Passive microwave data are unique in providing routine hemispheric-scale
pictures of sea-ice conditions to the scientific and operational
communities, and two decades of observations have now been accumulated
by the Defense Meteorological Satellite Program (DMSP) Special Sensor
Microwave / Imager (SSM/I) and its predecessor, the Scanning
Multi-channel Microwave Radiometer. This is a sufficiently long time
series to support studies related to climate-related events, such as
El-Nino Southern Oscillation \[1\] and to extract information on
systematic changes in ice conditions that may be related to climate
change \[2\].

As well as being of scientific value, SSM/I data are also extremely
important to the operational community, which has the task of supporting
surface and sub-surface ocean-going vessels with timely and regular
information on ice conditions \[3\]. Ice analysts use SSM/I data to
prepare ice charts for regular dissemination to the operational
community, both in the USA at the National Ice Center (NIC) and
elsewhere around the world in areas where sea-ice presents a hazard.
SSM/I is important to the operational community because it provides wide
and complete (cloud-free) coverage on a daily basis: this is something
that no other data can match at present.

Although SSM/I data are important to the operational community, they do
have limitations. SSM/I-derived sea-ice products have coarse resolution
(a pixel size of approximately 25 km in the 19 to 37 GHz frequency
range) and so the data tend to be used after all higher resolution data
sources have been exhausted. Other limitations are related to the
algorithms used for converting the SSM/I brightness temperatures into
ice concentrations. In contrast to active microwave, visible and
infra-red satellite data, which are interpreted manually at the NIC,
passive microwave data are converted to a geophysical product prior to
use by the ice analysts. In general, these SSM/I algorithms do not make
use of ancillary information so they are limited by the ambiguities
inherent to passive microwave data. These ambiguities include confusion
between weather artifacts and the presence of open water in areas of
sea-ice, and confusion between melting sea-ice (for example indicated by
surface melt-ponds) and areas of lower ice concentration \[4\]. The
result of these algorithm limitations is that SSM/I data are less useful
to the operational and scientific community than would be expected from
the excellent coverage and the apparent transparency of clouds. In fact,
with the exception of the location of the sea-ice edge, SSM/I data are
used in a qualitative rather than quantitative manner in compiling ice
charts.

The purpose of this paper is to show how operational ice charts provided
by the U.S. National Ice Center can be used to both evaluate sea-ice
concentration algorithms and can form part of an operational data fusion
algorithm. Operational ice charts provide the least ambiguous form of
wide coverage reference information on ice conditions, as they are based
on an expert manual synthesis of observations from a wide variety of
sources. These include satellite observations, knowledge of sea-ice and
ocean climatology, considerations of continuity, weather predictions,
model forecasts, ice charts from other ice centers and, when available,
airborne and ship observations. Although ice charts are subject to
error, they are compiled by experts familiar with the data sources and
their ambiguities.

## II. U.S. National Ice Center Ice Charts

The U.S. National Ice Center provides weekly ice charts for all
ice-covered seas and achieves this by maintaining a group of highly
qualified ice analysts, each with responsibility for generating ice
charts in one or more regions. The analysts are supported by a senior
analyst that quality controls the final product. The ice analysts
undergo formal training and follow a set procedure for generating ice
charts, which ensures that high quality input data takes precedence over
lower quality input data. This ice analysis procedure is illustrated in
Figure 1. The analyst starts by compiling background information,
including the ice chart from the previous week, digital climatology
data, weather predictions and output from the coupled ice-ocean forecast
model, PIPS \[5\]. These data are used to provide the analyst with the
best possible understanding of regional conditions from which to
interpret the data (step 0, Figure 1). The analyst then starts with the
highest available resolution of data. On occasion, this may include some
air-borne or ship-borne observations, but in general these will not be
available, in which case the analyst starts with RADARSAT data. The
analyst will map the region covered by RADARSAT data into regions of
approximately "uniform" total and partial ice concentrations. Once there
is no more available RADARSAT data, the analyst will use the next
highest resolution data source, normally Operational Line-scan System
(OLS) data from the DMSP satellite program, which will be either visible
or infra-red data depending on the time of year and hemisphere. Having
provided additional ice analysis based on OLS data, the analyst will
then move to NOAA AVHRR data (step 3, Figure 1). In the existing ice
analysis procedure, two further steps remain. In the first step, DMSP
SSM/I data are analyzed, having been translated into ice concentrations
using the NASA Team or CAL/VAL algorithm, provided by the NOAA Center
for Environmental Prediction (R. Grumbine, personal communication) and
Fleet Numerical Modeling and Oceanography Center (J. Haferman, personal
communication). These data are used to fill in remaining areas of the
new ice chart. As a final step, the ice chart is quality checked by a
senior analyst (step 5, Figure 1). An example of an ice chart, for the
Sea of Okhotsk, is shown in Figure 2 for 7 December 1998.

## III. Limitations of Conventional SSM/I ice concentration algorithms

It was shown in Figure 1 that SSM/I is the final data source used to
compile ice charts, primarily because it has the lowest spatial
resolution of satellite data, but also because it has some significant
biases. On the other hand, SSM/I data are always available and so it
remains an invaluable source of data, especially in the southern
hemisphere where satellite SAR data are currently unavailable on a
routine, operational basis.

SSM/I algorithms have been based largely on radiative transfer models
for the surface and atmosphere, together with a mixing model that
assumes that the footprint of the sensor contains only open water and
sea-ice. These models include:

-   the NASA Team algorithm and modification for thin ice (\[6\],
    \[7\]);

-   the Bootstrap algorithm \[8\];

-   the CAL/VAL (modified version of AES-York) algorithm \[9\];

-   the Bristol Hybrid algorithm \[10\].

Other algorithms have also been developed. The differences lie in the
choice of frequency and polarization combinations and in the precise
technique used to estimate concentration from the distribution of points
in the relevant feature space.

The techniques listed here all made use of one or both of the 19 and 37
GHz channels on SSM/I (with the 22 GHz channel sometimes used for data
quality checking), but they generate significant differences in
predictions of ice concentration. Steffen et al. \[4\] evaluated seven
algorithms and concluded that total ice concentration can be estimated,
at best, with an accuracy of 5-10% during non-melt conditions and 10-20%
at other times. More recently, Comiso et al. \[11\] found that both the
Bootstrap and NASA Team algorithms persistently under-estimated ice
concentrations when compared to AVHRR, LANDSAT and SAR data. A
comparison of just two algorithms is shown in Figure 3 for a random day
in the Arctic during December, 1998. Table 1 shows statistics that
summarize differences between these two algorithms in various regions
across the Arctic and Antarctic.

+-------------------+--------+----------+----------+-----------------+
| Region (9         | Season | Mean     | R.m.s.   | Mean 0% ice     |
| December 1998)    |        | conce    |          | concentration   |
|                   |        | ntration | conce    | edge position   |
|                   |        | di       | ntration | difference (km) |
|                   |        | fference | di       |                 |
|                   |        | (%)      | fference |                 |
|                   |        |          | (%)      |                 |
+-------------------+--------+----------+----------+-----------------+
| Central Arctic    | Winter | 2.6      | 3.34     | -71.0           |
+-------------------+--------+----------+----------+-----------------+
| East Greenland    | Winter | 8.8      | 5.4      | -23.0           |
+-------------------+--------+----------+----------+-----------------+
| Baffin Bay        | Winter | 13.5     | 6.2      | -23.0           |
+-------------------+--------+----------+----------+-----------------+
| Sea of Okhotsk    | Winter | 29.0     | 8.9      | -44.0           |
+-------------------+--------+----------+----------+-----------------+
| Ross --           | Summer | 14.5     | 5.3      | -24.0           |
| Bellinghausen     |        |          |          |                 |
| Seas              |        |          |          |                 |
+-------------------+--------+----------+----------+-----------------+
| Weddell Sea       | Summer | 15.3     | 6.2      | -21.0           |
+-------------------+--------+----------+----------+-----------------+

Table 1. Statistics related to differences in ice concentrations
estimated using the NASA Team and CAL/VAL algorithms for 9 December,
1998. A positive sign indicates that the CAL/VAL algorithm shows more
ice than the NASA Team algorithm.

It can be seen that differences in estimated ice concentration are
substantial and vary systematically from region to region. An analysis
of the different sources of error is required to understand why these
differences are regionally so large and vary systematically.

## *A. Tie-point variations*

Tie-point related errors result from natural variations in brightness
temperatures associated individual type of surface. SSM/I algorithms
make use of reference brightness temperatures associated with two ice
types (first-year ice and multi-year ice in the northern hemisphere) and
open water; these values are known as tie-points. However, all natural
surfaces have variability in their emissivity and physical temperature
characteristics. The open water brightness temperature, for example,
will vary according to wind speed, foam and atmospheric effects. In the
NASA Team algorithm, for example, the tie-point that is biased towards
calm water conditions. Ice concentrations in areas of rough water will
therefore be over-estimated and locally-calculated tie-points tend to be
larger in terms of brightness temperature than the global value used by
Cavalieri et al. \[12\]. The rationale for this is that the scheme
maintains a relatively high dynamic range. Furthermore, Steffen and
Schweiger \[13\] found that, in the northern hemisphere, the
locally-determined first-year ice tie-points tended to be lower than the
global value, which provides one explanation as to why ice
concentrations in heavy pack are generally under-estimated with the NASA
team algorithm.

Estimation of multi-year ice concentration represents a particular
problem with SSM/I. This is reflected in the widely-varying evaluations
of multi-year ice concentration derived from SSM/I. Cavalieri et al.
\[12\] evaluated the NASA Team algorithm using aircraft under-flights in
the Bering, Beaufort and Chukchi seas and found that multi-year ice
concentration was over-estimated typically by 5% +/- 4%, although in
areas of high multi-year ice concentration, the bias was usually
reversed. This they attributed to mis-classification of older first year
ice, which they suggest starts to take on the scattering characteristics
of multi-year ice later in the season (partly through the properties of
the overlying snow). Results from other studies are not consistent with
these results, however, suggesting that multi-year ice concentrations
during the freeze-up are significantly lower than end-of-summer total
ice concentrations (\[14\], \[15\]). The two estimates differ by
approximately 10-20%, which is approximately the amount of new
first-year ice at the September minimum \[16\]. Investigations at the
NIC have shown that the NASA Team estimates can both be gross
under-estimates and over-estimates of multi-year ice concentration. NIC
have found isolated areas where multi-year ice would apparently appear
and then disappear (e.g. in the south Kara Sea and in the Sea of
Okhotsk). NIC also compared the manually-derived 80% multi-year ice
concentration line derived from a RADARSAT mosaic for the
Beaufort-Chukchi sector of the Arctic during early November 1996 and
found that it corresponded to the 40 to 50% multi-year ice concentration
contour from the NASA Team algorithm. Comiso \[14\] attributes some
under-estimation of multi-year ice concentration to flooding of
multi-year floes by sea-water.

Steffen and Schweiger \[13\] and others have found that the use of local
tie-points can (in some circumstances) halve the r.m.s. difference in
ice concentrations derived from SSM/I and visible / infra-red sensors
(AVHRR and Landsat) and the use of local tie-points is particularly
important in the Arctic. However, the use of local tie-points will only
partially remove the error.

*B. Geophysical cross-talk errors*

These errors are related to the use of data sets that are correlated to
more than one geophysical parameter. In the case of sea ice
concentration estimation, for example, the error could result in
estimated sea-ice concentration being correlated to surface temperature.
The use of radiance ratios in the NASA team algorithm enables variations
in surface temperature to be removed, to first order, from having an
effect on ice concentration.

*C. Surface melt - related errors*

These errors cause most SSM/I algorithms to under-estimate ice
concentration. Errors in ice concentration tend to double to 10-20% for
algorithms during summer and the errors are systematic \[4\]. In a
sense, this is not an "error", as it reflects the fact that SSM/I
classifies surface water as "open water". Russian drift stations show
that, in mid July, as much as 45% of the ice may be covered by melt
ponds \[17\], and this will be reflected in lower estimates of ice
concentration from passive microwave data. As the sensor cannot
penetrate beneath melt ponds, this problem cannot be solved in any
simple manner. The problem will affect measurements as long as there is
liquid water in significant quantities, such as in overlying snow cover.

##### D. Thin ice -- related errors

Errors related to the presence of new and young ice types are not
explicitly accounted for in the existing operational SSM/I ice
concentration algorithms. In freeze-up conditions, there will always be
a tendency for ice concentration to be under-estimated as a result of
this effect. Figure 4 shows sea ice concentrations predicted for the Sea
of Okhotsk on the 8 December, 1998, where a high concentration of new
and young ice types (indicated by the ice chart in Figure 2) creates
major discrepancies in the predictions of ice concentrations from the
different algorithms. A tendency of the NASA Team algorithm to
significantly under-estimate ice concentration in the Sea of Okhotsk has
been noted by \[18\]. The differing sensitivities of the algorithms to
thin ice types is confirmed in Figure 5, which shows apparent ice
concentration as estimated using the four algorithms listed above with
brightness temperatures obtained from controlled observations from known
ice types. It can be seen that the algorithms have varied responses to
the presence of thin ice, pancakes and frost flowers.

The explanation for this problem is that the emissivity of new ice can
range from that of open water to first year ice (0.45 to 0.92). Steffen
and Schweiger \[13\] found that the presence of nilas and young ice
could create an under-estimate of ice concentrations from SSM/I of 9%
(with global tie-points) and 4% (with local tie-points), using the NASA
Team algorithm. The results shown here indicate that the errors can be
much larger in areas where thin ice types predominate. A thin ice
formulation of the NASA team algorithm helps to correct for these ice
types (\[7\] and Figure 4), but the algorithm cannot deal simultaneously
with multi-year ice, first-year ice and thin ice.

##### E. Snow-cover related errors

The effects of varying snow cover on ice concentration estimates do not
appear to have been studied in detail. Nevertheless, Figure 5 does
suggest that snow cover could have a marginal impact of a few percentage
points on ice concentration estimates, depending on the algorithm used.

##### F. Atmospheric transmission errors 

These errors are caused by the susceptibility of passive microwave
sensors to cloud liquid water and water vapor, integrated along the path
length of the radiation. These errors are currently impossible to
predict reliably and so SSM/I algorithms make use of weather filters to
reject ice concentrations over open ocean. Ice concentrations can be
affected by both integrated atmospheric liquid water and water vapor
content. Oelke \[19\] has used radiative transfer modeling to assess the
impact of weather systems on ice concentrations derived using the NASA
Team algorithm. He found that cloud liquid water content and water vapor
both artificially increase total ice concentrations, but decrease
multi-year ice concentration. Near surface winds tend to increase ocean
emissivity and thus add to spurious ice concentrations over the open
ocean. Sensitivity to the atmosphere has been the main reason why the 85
GHz channel has not been as popular for retrieving ice information as
its high spatial resolution would suggest (\[20\], \[21\]). Researchers
are currently working on methods to account explicitly for the
atmosphere in SSM/I algorithms \[22\].

##### G.. Geocoding and resolution-related errors

These errors represent uncertainty in the location of data. The CAL/VAL
algorithm, for example, uses the 37GHz channel alone to estimate ice
concentrations at the ice edge and this results in an ice edge that is
generally at least 20 km pole-ward of the ice edge generated by the NASA
Team algorithm (which uses the 19 GHz channel). This is a season and
hemisphere independent result that reflects the different resolutions
associated with the 19 and 37 GHz channels, which have an effective
field of view (along-track, across-track) that is (69, 43 km) at 19 GHz
and (37, 28-29 km) at 37 GHz (Table 1).

In summary, there are significant deficiencies in the conventional SSM/I
algorithms that make the search for climate signals and operational use
problematic. The results presented here suggest that the errors are
larger for some algorithms than has been suggested by other studies, at
least on a regional basis. A mean difference of 29% in ice concentration
for the Sea of Okhotsk from two algorithms, with maximum differences in
excess of 50% is extremely large. In order to improve the estimation of
ice concentrations from SSM/I, a different approach is needed than that
provided by conventional SSM/I algorithms.

## IV. A Data Fusion Algorithm for deriving ice concentrations from SSM/I

The issues discussed above limit the utility of conventional algorithms
for operational ice monitoring and for scientific applications and it
has become increasingly clear that substantial improvements in the
quality of information derived from SSM/I can only be achieved by
bringing in ancillary information. The conclusion from Steffen et al.
\[4\] was that data assimilation / fusion and artificial intelligence
methods (possibly making use of decision-making to operate a hybrid
algorithm) offer the greatest promise for resolving ambiguities in the
passive microwave algorithms.

Some researchers have already begun to explore the possibility of
retrieving ice information from SSM/I in combination with other data or
models, as a way to reduce the ambiguities of weather effects, etc.
These include the combination of AVHRR and SSM/I to classify sea-ice,
open water, land and cloud \[23\]; the constraint of SSM/I ice
concentrations using SAR data \[24\] and the use of a physical model to
constrain ice concentration estimates from SSM/I over time \[15\]. A
data assimilation approach to the problem has particular potential, and
in the long term appears to be the most promising way forward. In the
shorter term, using other forms of data to constrain the SSM/I algorithm
in a form of data fusion is easier to implement. However, if the data
that are used to constrain the SSM/I are themselves ambiguous, as in the
case of AVHRR (through cloud-ice ambiguities) and SAR (through water-ice
ambiguities), it can be difficult to design an algorithm that is
operationally practical.

The algorithm proposed here, provisionally called the SSM/I
Interpolation (SI) algorithm, attempts to compensate for limitations in
the conventional algorithms, and biases inherent in the sensor, by
tuning the algorithm to ice concentration information available in NIC
ice charts. The aim is that this would be a near real time procedure in
which the ice analyst makes use of a tuned SSM/I ice concentration
algorithm routinely during compilation of ice charts. The proposed
context of the new algorithm is illustrated in Figure 7, which should be
compared with Figure 1. The SI algorithm would replace the stage in the
analysis procedure where a conventional SSM/I algorithm is employed. The
analyst would generate an ice chart with closed regions at step 3 in the
analysis procedure and all areas that had not been assigned ice
concentrations at that point would be given a "no data" label. This is
the so-called "partial" ice chart, which is based solely on
reconnaissance, RADARSAT and cloud-free AVHRR and OLS data. The analyst
would then run the SI algorithm on the partial ice chart and it would
optimally match calculated SSM/I attributes to the ice concentrations in
the partial ice chart using the procedure illustrated in Figure 8. The
calculated model coefficients would then be used to "interpolate" ice
concentrations for the "no data" regions remaining in the partial ice
chart. The analyst would then use these interpolated ice concentrations
from SSM/I to complete the ice chart.

This approach has the following advantages:

-   The algorithm is tuned as frequently as ice charts are generated
    (weekly in most regions and twice weekly in some);

-   The algorithm is tuned to the region being analyzed, effectively
    taking into account regional anomolies in salinity, snow conditions,
    etc.

-   The tuning parameters can be used to help define a regional and
    temporal database for future and retrospective processing of passive
    microwave data.

The SI algorithm is a statistical least squares procedure that takes
attributes derived from the SSM/I brightness temperatures and generates
linear mapping coefficients that allow the SSM/I attributes to be mapped
to total ice concentration, with the latter extracted from the ice
charts. A critical element of the algorithm is the choice of attributes,
as inappropriate attributes will make the mapping function inefficient.
The attributes selected for implementation include the partial ice types
calculated by the NASA Team and Bootstrap algorithms and selected
principal components of the SSM/I data. The rationale for including
these attributes is as follows:

-   The NASA Team and Bootstrap algorithms provide independent estimates
    of two partial ice type concentrations. In the northern hemisphere
    these correspond to first-year and multi-year ice. The partial ice
    types from both algorithms, added together as components of a linear
    function, are expected to be efficient measures of ice types of
    young age and older (Figure 5). However, note that within the SI
    algorithm no assumption is made about what particular ice types
    these partial concentrations refer to. The fact that these partial
    concentrations are multiplied by linear coefficients should enable
    the SI algorithm to compensate for under-estimation of ice
    concentration in the summer melt period. The NASA Team and Bootstrap
    partial concentrations were selected because other algorithms are
    less well evaluated or do not provide as much information. The
    CAL/VAL algorithm, for example, generates ice concentration
    predictions that saturate at 100% over most of its ice extent, so
    was felt likely to be inefficient as an attribute for the SI
    algorithm. However, additional or different algorithm predictions
    could be added to the SI algorithm if required.

-   The lower variance principal components of the SSM/I data tend to be
    related to the presence of thin ice. This has been demonstrated by
    Wensnehan et al. \[25\] and Grenfell et al. \[26\], following
    earlier principal components analysis \[27\]. Hence, these
    attributes are included to counter the known tendency of the NASA
    Team and Bootstrap algorithms to under-estimate ice concentrations
    in areas of thin ice. Figure 6 shows that the 3^rd^, 4^th^ and 5^th^
    principal components of data from 9 December 1998 are all strongly
    related to the presence of new ice. The 3^rd^, 4^th^ and 5^th^
    principal components of the data (in order of decreasing variance)
    are included as a way of picking out conditions that the NASA Team
    and Bootstrap algorithms fail to detect, including the presence of
    thin ice and snow depth effects. The 1^st^ and 2^nd^ principal
    components are not included as they express the magnitude and
    spectral gradient of the data, respectively, and are not expected to
    add to the overall level of explanation provided by the algorithm.
    The SI algorithm will ensure that if these low variance principal
    components do not add to the overall ability of the algorithm to
    predict ice concentration, then they will not be used (the
    coefficients will be very small).

Although these so-called "partial" ice charts are not routinely retained
at the National Ice Center, for the purposes of this study, partial ice
charts were collected for the Barents Sea for the period 2^nd^ November
1998 to 14^th^ December 1998. In order to make use of the NIC partial
ice charts, the ice chart data had to be converted, in most cases, from
a range of concentrations to a single concentration value. Ice analysts
may enter a range of ice concentrations for each region of the ice chart
as they are not always able to determine ice concentrations to the
nearest tenth. The conversions were carried out as follows:

  ---------------------------------- ------------------ ------------------
  Ice concentration range (tenths)   Assigned value     Estimated error

  0 to 1                             5%                 5%

  0 to 2                             10%                10%

  2 to 4                             30%                10%

  4 to 6                             50%                10%

  6 to 8                             70%                10%

  8 to 10                            90%                10%

  9 to 10                            95%                5%
  ---------------------------------- ------------------ ------------------

**Table 2**. Conversions for ranges of ice concentrations provided in
ice charts to discrete ice concentration values.

In addition, the data were converted to a raster format and polar
stereographic projection consistent with the SSM/I antenna temperature
data provided by the NOAA Center for Environmental Prediction (R.
Grumbine, personal communication). In the partial ice charts, ice
concentrations were indicated by a number between 0 and 100 (indicating
ice concentration as a percentage of complete ice-cover), and areas of
no data were indicated by a value of --99. Implicit in this mapping
procedure is the need to reduce the resolution of the ice chart data.
This is achieved by a local averaging of the concentrations provided in
the ice charts.

Figure 9 shows the results of applying the SI and conventional
algorithms to the Barents Seas, together with the partial ice chart used
to tune the SI algorithm. The subtle differences between the algorithms
are difficult to see and so statistics have been derived for the SI
algorithm and the conventional algorithms, by comparing the algorithms
to the partial ice charts. These statistics are illustrated in figures
10 and 11. Figure 10 shows the mean difference between the partial ice
chart and each algorithm as a function of date. This shows that the SI
algorithm is successful in significantly removing much of the overall
bias that is prevalent in the conventional algorithms. The mean bias
reduces from a range of --15 to +5% for the conventional algorithms to a
range between --5 and 0% for the SI algorithm. Figure 11 shows the mean
absolute difference between the ice chart and each algorithm as a
function of date. It can be seen in this case that the SI algorithm
reduces the mean absolute difference from around the typical 10-15% of
the conventional algorithms to between 5-10%. The variance due to the
partial concentrations of the principal components (as opposed to the
NASA Team and Bootstrap partial concentrations) varies from a minimum of
7.64% to a maximum of 48.5% and is not particularly consistent from week
to week.

These results indicate that the SI algorithm is successful at tuning an
SSM/I algorithm to ice chart data. Furthermore, the technique does not
exhibit any obvious "unstable" behavior within the "no data" region of
the partial ice chart (in other words, outside the area that was used to
calculate the model coefficients). However, there are certain
limitations to the technique and its evaluation reported here that need
to be made explicit:

1.  There has been no independent verification of the performance of the
    technique. It has been shown that the technique is successful at
    closing the gap between ice chart--derived ice information and
    SSM/I-derived ice information. The SI algorithm will only be as good
    as the quality of the ice chart.

2.  The SI algorithm will become less stable as the proportion of the
    area in the partial ice chart labelled "no data" increases.

3.  The performance of the SI algorithm depends on a good match in time
    between the concentrations derived from the cloud-free visible and
    SAR data. If the data that are used to generate concentrations in
    the partial ice chart are spread over a period of more than perhaps
    two consecutive days, then the performance of the algorithm will
    degrade significantly. In the ideal situation, the algorithm would
    be used in helping to compile daily ice charts where there is a
    reliably close match in time between the ice chart and the SSM/I
    data. With the current weekly ice charts from NIC, there will be
    some error introduced into the SI algorithm by using SSM/I data
    recorded on one day.

## V. Conclusions and Recommendations

The SI algorithm is a practical technique for use by the operational
community and generates significantly improved performance over
conventional algorithms when compared against ice chart information. In
addition, the technique is practical for near real-time ice monitoring.
The algorithm represents a new approach to the use of SSM/I data for
operational ice monitoring, and has the following advantages:

-   Automated tuning of the algorithm to the date and region of
    interest;

-   No manual intervention required (beyond production of the ice chart
    itself);

-   Some correction for sensing biases such as under-estimation of ice
    concentration during summer and weather effects;

-   Flexibility in terms of the attributes used in the program. New
    attributes can be added to increase the model fit achieved by the
    algorithm.

The disadvantage of the algorithm is that it is only as good as the ice
chart from which it is derived. Any biases in the analysis procedure
will be inherited by the algorithm. This algorithm will tend to
re-inforce any such biases by trying to include them through the linear
coefficients. Thus, it is important that the analysis procedure is
carefully monitored to minimize such effects.

The following recommendations are made:

-   The algorithm should be tested with different attributes to see
    whether there are systematic differences in the level of
    performance;

-   The algorithm should be tested for summer conditions when it is
    expected that algorithm will have some success in compensating for
    the more severe under-estimation of ice concentrations by
    conventional SSM/I algorithms;

Operational testing of the algorithm is planned at the U.S. National Ice
Center.

**Acknowledgements**

This work would not have been possible without support from Mary-Ruth
Keller, Adnan Trakic, Kyle Dedrick, Cheryl Bertoia and Paul Seymour of
the U.S. National Ice Center. SSM/I data for the study were kindly
provided by Robert Grumbine and Paul Chang of the National Oceanographic
and Atmospheric Administration. Jeff Haferman of Fleet Numerical
Modeling and Oceanography Center provided ice concentration products
generated using the CAL/VAL and NASA Team algorithms.

**Illustrations**

**Figure 1.** The existing ice analysis procedure at the National Ice
Center, showing the use of SSM/I data processed using a conventional
algorithm. Numbers indicate steps in the ice analysis procedure referred
to in the text. Defense Meteorological Satellite Program (DMSP) SSM/I
ice concentrations are generated using both the NASA Team algorithm,
which has been supplied by both the NOAA Environmental Prediction Center
(NCEP) and the Fleet Numerical Modeling and Oceanography Center (FNMOC),
and the CAL/VAL algorithm (FNMOC).

**Figure 2.** Example of an NIC ice chart, disseminated to users on a
weekly basis for all ice-covered seas. This example shows the western
Sea of Okhotsk for 7-11 December, 1998.

**Figure 3.** Difference between the CAL/VAL and NASA Team algorithms
for 9 December 1998, in the northern hemisphere.

**Figure 4.** Algorithm predictions of total ice concentration for the
western Sea of Okhotsk on 9 December 1998. The corresponding ice chart,
compiled using nearly cloud-free OLS data, is shown in Figure 2. Total
concentrations indicated by the ice chart are generally above 9/10
within the ice edge and partial concentrations of new and young ice
types make up a significant percentage of the total concentration.

**Figure 5.** Algorithm predictions of total ice concentration as a
function of ice types, with brightness temperatures derived from
controlled observations. Observations of new ice (with thicknesses
indicated in centimeters) are derived from Wensnehan et al. \[25\],
recorded over growing saline ice. Observations from frost flowers,
pancakes, young and multi-year ice were recorded by a ship-borne
radiometer sensor operating during the North Water experiment in Baffin
Bay in May, 1998 (ref.). First-year ice data with different snow
thicknesses were taken from Grenfell \[28\]. Given that the footprints
in each case contained 100% of the labeled surface type, the "true"
total concentrations are 0% for open water and 100% for all ice types.
All observations correspond to freezing conditions.

**Figure 6.** Principal components of data from controlled observations
(a sub-set of those in Figure 5), where the eigen-vectors are generated
using principal component analysis from the entire Arctic for 9 December
1998. The intensities are normalized from 0 to 100.

**Figure 7.** The proposed new ice analysis procedure, showing the
formation of a partial ice chart and its use in the SSM/I Interpolation
algorithm.

**Figure 8.** Flow diagram showing details of the SSM/I Interpolation
algorithm. "PC" refers to principal component and the associated number
refers to the level of overall variance explained, so that 3 would
indicate the component with the 3^rd^ highest level of variance.

**Figure 9.** Maps showing predictions of ice concentration from the
NASA Team, Bootstrap, CAL/VAL, Bristol Hybrid and SI algorithm, for the
Barents Sea. Also shown (lower right), is the partial ice chart used to
"train" the SI algorithm. Blue indicates no data, red indicates 100% ice
concentration and green indicates 0% ice concentration. Land is shown in
white. North is towards the lower right of each map.

**Figure 10.** Mean difference between ice concentrations from
algorithms and partial ice charts as a function of date for the Barents
Sea. A positive value indicates that the algorithm predicts more ice
than the ice chart.

**Figure 11.** Mean absolute difference between ice concentrations from
algorithms and partial ice charts as a function of date for the Barents
Sea.

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![](media/image1.png){width="0.4340277777777778in"
height="0.6388888888888888in"}

![](media/image9.png){width="4.305555555555555in"
height="5.740277777777778in"}

![](media/image10.png){width="3.8368055555555554in"
height="4.934027777777778in"}

![](media/image11.png){width="4.930555555555555in"
height="6.381944444444445in"}

![](media/image12.wmf)

![](media/image1.png){width="0.4340277777777778in"
height="0.6388888888888888in"}

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height="6.798611111111111in"}

![](media/image15.wmf)

![](media/image16.wmf)

Figure 12.

Figure 13.

Ice

Concentration

(tenths)

Normalized

Intensity

Surface type

Surface type

![](media/image17.wmf){width="4.3069444444444445in"
height="5.561111111111111in"}Extra figure.