White Paper on

**Proposed**

**Development of a Joint Scientific - Operational**

**Arctic-Wide Sea Ice Product**

Prepared for

**Dr. S. Gogineni**

NASA Polar Programs

Prepared by

**K.C. Partington^1^ and K. Steffen^2^**

^1^National Ice Center,

4251 Suitland Rd.,

Washington, D.C. 20395

(kpartington@natice.noaa.gov)

and

^2^Cooperative Institute for Research In Environmental Science

Campus Box 216,

Boulder, CO, 80309-0216

(koni@seaice.colorado.edu)

14 April, 1998

**Distribution**

NIC CDR. D. Martin, Mr. D. Benner, Ms. C. Bertoia

FNMOC CAPT. D. Mantner, Dr. J Haferman, Mr. M. Clancy

NOAA Ms. H. Wood, Dr. M. Van Woert

NSF Dr. T. Pyle

NRL Dr. E. Hartwig, Dr. R. Preller, Dr. G. Poe

NASA Dr. S. Gogineni

ONR Dr. T. Curtain, Dr. D. Conley, Dr. C. Paulsen, Dr. M. Colton

NO96 Mr. R. Godin

SPAWARS Dr. S. Payne, CAPT. C. Hopkins

USARC Dr. G. Brass

NSIDC Dr. R. Barry, Dr. F. Fetterer

**Abstract**

This document presents a plan for implementation of a high quality
Arctic-wide sea-ice product, based on data assimilation techniques and
coordinated integration of newly available and planned satellite and
ground sensor observations. It is recommended strongly that a
joint-agency program be established with an oversight committee that
manages the program. The benefits of this program will include
development of a high quality geophysical product that serves both the
scientific and operational communities, pooling of scarse resources,
wider return on investment and a long-term strategy for sea ice
research.

**List of Contents**

1.  **Introduction** **2**

2.  **Objective and Benefits** **3**

3.  **Arctic-Wide Sea-Ice** **Product: The Current Situation** **4**

    3.1 Processing and Archiving Infrastructure 4

    3.2 Existing Development Strategy 5

    3.3 Existing Operational Sea-Ice Model 5

    3.4 Existing Wide Coverage Sea-Ice Observations 5

4.  **Arctic-Wide** **Sea-Ice** **Product: Requirements** **8**

    4.1 Introduction 8

    4.2 Scientific Requirements 8

    4.3 Operational Requirements 10

5.  **Arctic-Wide Sea-Ice** **Product: Proposed Implementation Plan**
    **12**

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1.  Overview of Proposed Infrastructure 12

2.  Proposed Implementation Strategy: An Operational Data Assimilation
    Scheme 13

3.  Proposed Activities 17

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6.  **Summary of Recommendations** **23**

7.  **References** **24**

8.  **Glossary** **26**

9.  Appendix: Anticipated List of Operational Sensors 27

**Acknowledgements**

This document was prompted by discussions at the meeting entitled "Sea
ice monitoring into the next millenium: workshop to evaluate new
approaches to sea ice observation", held in Boulder on 3-5 December,
1997, and by discussions with Drs. R. Thomas and S. Gogineni of NASA and
Dr. T. Curtain of ONR. It makes use of material from all participants at
that meeting, for which the authors are grateful. In particular, the
authors would like to thank the sponsors and organizers of that meeting
as well as the contributors and editors of the White Paper which
resulted from the workshop. Particular reference must be made to Dr. R.
Errico of NCAR who provided a most useful introduction to the techniques
of data assimilation.

# Introduction

Understanding the large-scale distribution and characteristics of the
sea-ice cover in the Arctic has been a major objective of both the
scientific and operational sea-ice communities for many years. For the
scientific community, the primary motivation is to define the key
factors that influence decadal-scale variations in sea-ice coverage. For
the operational community, the motivation is to obtain early season
indicators of ice conditions and to have guidance in producing
higher-resolution regional analyses. What is often overlooked is that
fact that the information requirements of these two communities overlap
to a considerable degree at this "global" scale of coverage (with a
spatial resolution of the order of 10 km). Given this strong,
broadly-based and long-standing interest, it is important to ask how far
we have come towards achieving the goal of being able to monitor the
state of the Arctic reliably and accurately.

The development of space-borne passive microwave sensors in the 1970s
represented a significant step forward in the development of this
capability, and many of the techniques which form the basis for sea-ice
models were also developed during that decade. However, although new
space-borne sensors (such as synthetic aperture radar), ground
observations (such as those provided by the International Arctic Buoy
Program) and increasingly reliable weather fields (such as those from
ECMWF and FNMOC) have appeared since then, there has been remarkably
little development in the capability to combine these resources to
provide a high quality, synthesized Arctic-wide sea-ice product. Yet,
the implementation of such a product would mark a major milestone in the
move towards maturity of the sea-ice monitoring community and would have
ramifications well beyond the field of sea-ice research.

With new sensors planned for near real time use in the next year (e.g.
MODIS and QUICKSCAT), and new Navy funding being directed towards an
upgrade of the operational Navy sea-ice model (PIPS), it is timely to
initiate a joint agency program for the development of an Arctic-wide
sea ice product. This will make use of data assimilation techniques that
have been tried and tested within numerical weather prediction, and will
provide a clearly defined route for research techniques to be migrated
into the operational environment. Only through a program of this nature
and scale can full use be made of the technologies that are available to
the sea-ice community.

The importance of such an initiative was recognized in the recent white
paper on "Sea-Ice Monitoring into the Next Millenium" (Weaver, 1998).
That white paper was aimed at pointing the way forward for wide coverage
sea-ice monitoring, and ends by saying that "...the joint development of
a scientific /operational global sea ice product is strongly encouraged
to make more efficient use of resources". This document outlines how
this recommendation may be implemented.

# Objective and Benefits

The aim of this document is to present a plan for development of an
Arctic-wide sea-ice product, and to solicit a combination of resources
and a willingness to cooperate from agencies that have an interest in
the high latitude regions. **This will build on, and integrate with, new
investment in sea-ice models which is being undertaken by the Navy.**
The desired outcome of this initiative is for the announcement of a
joint agency program that provides the funding for such a development
and for this to be underpinned by an agreement by the operational
community to support the migration and maintenance of techniques into
the operational environment.

The proposed program would result in the development of a daily,
quality-checked, comprehensive sea-ice product providing information on
total and partial sea ice concentration, lead orientation, ice motion
and ice thickness distribution. The product would be designed using
techniques developed by the research community, implemented and
maintained by the operational community, and archived at the National
Snow and Ice Data Center. It would become a major long-term resource for
the sea-ice community and would form the basis for future digital
climatologies.

If this solicitation is successful, the U.S. science and operational
sea-ice monitoring activities could be streamlined and rationalized,
with the following concomitant benefits:

-   

-   ***effective use of the wide array of data*** available for Arctic
    monitoring, through the use of data assimilation techniques, which
    have been tried and tested in numerical weather prediction;

-   ***enhanced funding base*** and ***strong program momentum***
    through multi-agency involvement;

-   ***improved return on research and operations investment*** through
    linking of technologies developed by each community and broadening
    of user base;

-   ***availability of expertise*** from both the scientific community
    and the operational community;

-   ***enhanced opportunities for product evaluation*** ***and***
    ***validation***, through near real time availability of products
    and involvement of the operational community;

-   a clear route for ***migration of science community techniques to
    the operational environment***;

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-   Provision of an ***overall strategy*** for Arctic sea-ice research.

# Arctic-Wide Sea-Ice Product: The Current Situation

## Processing and Archiving Infrastructure

For Arctic-wide sea-ice products, the scientific community is served by
the National Snow and Ice Data Center (NSIDC), which provides an
Arctic-wide SSM/I sea-ice product using the NASA Team algorithm (Steffen
*et al*., 1992). This product includes sea-ice concentration and, at
lesser accuracy, multi-year ice concentration, and is provided with a
frequency of once per day, but ordered from NSIDC on CD-ROM in blocks of
data covering 3 months. For selected field experiments, near-real time
passive microwave-derived ice concentrations can be retrieved from their
FTP server. NSIDC also provides a range of other products, such as
International Arctic Buoy Program (IABP) observations, data from
campaigns (such as AIDJEX) and historical Arctic rawinsonde
observations. Although NSIDC is the main point of contact within the
scientific community for polar ice products, it does not at present
generate or archive any assimilated Arctic-wide sea-ice product. As the
focus of NSIDC is product archiving and dissemination, it would probably
not wish to be regarded as a likely location for any operational data
assimilation scheme.

The operational community is served by the National Ice Center (NIC).
NIC incorporates information from the Fleet Numerical Meteorology and
Oceanography Center (FNMOC), which provides an SSM/I ice concentration
product based on the CAL/VAL algorithm (Preller, 1985). FNMOC also uses
this product to initialize the Polar Ice Prediction System (PIPS)
Version 2.0 model of the Arctic, which provides forecasts of conditions
over time intervals of between 24 and 120 hours. This model is a
dynamic-thermodynamic sea-ice model coupled with the Cox ocean model
(Preller, 1985). FNMOC is therefore already operating a rudimentary data
assimilation scheme, although they give complete weight to the SSM/I
data at the expense of the model predictions. At present FNMOC generates
and disseminates these products, but do not archive them. The Shared
Algorithm Research Panel (SARP) provides technical oversight to the
FNMOC SSM/I products, and has a mandate to recommend changes to the
algorithms installed there. This panel includes operational and
scientific members and meets periodically to review whether operational
changes need to be recommended to FNMOC.

Generation of these products by two distinct organizations represents an
unncessary separation of processing efforts by the scientific and
operational communities and the situation is illustrated in Figure 1
(compare with Figure 3 for the proposed scheme). Although there are two
separate processing chains, there is no conflict of interest. NSIDC are
primarily an archive and product dissemination facility and do not view
themselves as an operational product generation facility. Their roles
could therefore be entirely complementary in any operational data
assimilation scheme.

![](media/image1.emf)

**Figure 1.** Illustration of the current scheme used to generate
Arctic-wide sea-ice products.

## Existing Development Strategy

The current development strategy for global-scale sea-ice products is
based on single agency programs and lack of science-operations liaison
that has the following disadvantages:

-   Within the scientific community, projects tend to end with
    demonstrations of a product, with a relatively small likelihood of
    migration to an operational environment. Limitations on resources
    within the scientific community tend to result in very few
    geophysical products being made available on a routine basis to the
    scientific community. An exception was the GPS that was developed by
    JPL and based at the Alaska SAR Facility. This represented a
    relatively expensive development that had limited success in terms
    of demand from the user community.

-   Funded projects, although rated highly within the review process and
    technically strong in many cases, do not necessarily fit into any
    large-scale strategy in terms of product development.

-   Funding of operational developments can be based on an inadequate
    sampling of all available technologies. The operational community
    would benefit greatly from closer liaison with the scientific
    community and its sponsors in order to gain the benefit of their
    technical expertise and oversight.

In summary, there is no overall U.S. multi-agency strategy for
development of sea-ice monitoring technology.

## Existing Operational Sea-Ice Models

The only existing operational sea-ice model in the U.S. is the Polar Ice
Prediction System (PIPS) version 2.0. This is an implementation of the
Hibler dynamic-thermodynamic sea-ice model coupled with the Cox ocean
model (Preller, 1985), which produces estimates of ice thickness, ice
concentration and ice motion with forecasts up to 5 days. It is operated
at the Fleet Numerical Meteorology and Oceanography Center (FNMOC) and
it represents the culmination of significant ONR/Navy funding over the
last 10-12 years. It covers the Arctic region, including most major
marginal ice regions, and is updated using SSM/I-derived ice
concentration data. The ice concentration data are used to replace the
PIPS-derived ice concentration predictions on a daily basis and PIPS ice
thickness and surface temperature observations are modified in a
partially heuristic manner to make the parameters mutually consistent.
The model is a visco-plastic model which supports continuum mechanics
but does not support the development of fractures. The model therefore
has a number of deficiencies in terms of its internal physics, the
quality of the ocean model and also in terms of how it assimilates
observations. This has prompted the Navy to support development of an
improved sea-ice model (PIPS 3.0), the planning of which has only
recently got underway.

## Existing Wide Coverage Sea-Ice Observations

Satellite-based Arctic-wide products are currently based on the Defense
Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager
(SSM/I) products, and in particular on retrieval of sea-ice
concentrations. It is well recognized that these SSM/I algorithms are
far from ideal, on their own, as the basis for an Arctic-wide sea-ice
product. There are atmospheric effects which represent clutter in the
signal; there are inaccuracies in ice concentration related to inherent
variations in brightness temperatures associated with different surface
types; there are problems distinguishing melt ponds and ice-free areas
in summer, and there is geophysical cross-talk. While these products are
interpreted manually in generating regional ice chart products (taking
account of weather conditions and local climatology to "correct" product
anomalies), anomalies are fed through automatically into the sea-ice
model (PIPS 2.0). The model therefore reflects the biases of the SSM/I
product. A current activity, which has oversight from SARP, is to add
the NASA Team sea-ice product to the existing CAL/VAL sea-ice product.
This represents an interim measure prior to a more extensive review of
SSM/I algorithms, with Navy funding committed to post-doctoral research
support for this activity (based at NIC). Although this is a useful
first step in improvement of the PIPS and SSM/I sea-ice products, it
represents only a modest step forward and a more broadly-based approach
to product development is needed to make substantial improvements in the
overall product quality.

Routine ground observations are provided through the International
Arctic Buoy Program, which provides synpotic-scale pressure, temperature
and ice motion fields for the Arctic, and these are fed into a range of
scientific and operational activities (including updating of PIPS). The
program is funded through the National Ice Center and is managed by the
Polar Science Center at the University of Washington.

![](media/image2.png){width="6.802083333333333in"
height="5.709027777777778in"}

**Figure 2.** DMSP OLS image with overlying SSM/I-derived sea-ice
concentration contours (in tenths) for the Svalbard region. The figure
illustrates some of the limitations of SSM/I as the sole source of data
for an Arctic-wide sea-ice product. Erroneously low ice concentrations
can be seen where the ice surface is damp (e.g. north-north-east of 20E,
75N) and ice is missing in the SSM/I-derived contours just west of the
southern tip of Spitzbergen (center-left of picture).

Arctic-Wide Sea-Ice Product: Requirements

## Introduction

The implementation of a new product which serves both the operational
and scientific community requires agreement on a set of requirements
that meets the highest common denominator of both. In other words, it is
imperative that the needs for both communities are met. Fortunately, the
operational product requirements are met to large degree (but not
entirely) by addressing the scientific requirements, with the exception
of timeliness, and so the scientific community can take the lead in
specifying the product requirements. Operational requirements, however,
extend to cover issues related to implementation, such as the frequency
with which product upgrades can be supported and algorithm specification
requirements so operational requirements will necessarily drive the
implementation process. In this section, the product requirements are
addressed from the scientific and operational standpoints.

## Scientific Requirements

### Overview

Global sea-ice information is needed to address fundamental and critical
questions concerning the nature of climate change. The requirements
placed on the quality of the information are rigorous and reflect the
difficult nature of these questions. The overall concern is the role of
the polar regions in global climate, particularly in terms of heat and
mass fluxes, but there are a wide range of specific questions which are
being addressed by scientists. The long-term accumulation of knowledge
of specific processes will contribute towards the gradual accumulation
of an overall enhanced understanding of the role the polar environment
in the climate system. A few of these questions are as follows:

-   What is the relation of synoptic-scale circulation and meso-scale
    Arctic phenomena, such as ice edge eddies?

-   What are the ice-land boundary conditions that influence so strongly
    the behavior of the ice pack in the shelf region where much of the
    oceanic heat loss occurs?

-   What causes the equator-ward migration of polar vortices of the
    upper troposphere / lower stratosphere?

-   What is the role of lead, polynya and new ice formation in
    ocean-atmosphere heat flux?

-   What is the role of land distribution, large-scale ice and open
    water patterns and boundary layer processes on the formation and
    trajectory of polar lows?

-   What are the interactions between the ice and open water in the
    marginal ice zone, and in particular what is the effect of oceanic
    process propagation into the ice?

The answers to these and other questions have a wide range of
applicability to a number of practical issues beyond those related to
the state of the climate system. These include the transportation of
pollutants around the Arctic; statistical properties of ice cover for
the planning of polar transportation and construction and military
operations and exercises; improved weather forecasting and fisheries
protection and management.

At present, these questions cannot be addressed adequately through the
analysis of current sea-ice products, because of insufficient coverage,
ambiguities in the data, poor resolution or inadequate model physics.
All of these applications would benefit from a data assimilation scheme
which is able to deal with many aspects of the Arctic system at once
rather than a set of observing systems and models which each provide (in
a controlled, algorithm sense) unrelated geophysical parameters.

### Scientific Observation Requirements

Science requirements for sea-ice observations have been outlined in a
number of reports and need not be discussed at length here. Table 1
summarizes the requirement based on a brief review of the literature.

+-------------+---------------+-------+----------+------------+------+
| **Product** | *             | *     | **Ac     | **Spatial  | **R  |
|             | *Parameters** | *Prec | curacy** | Resolution | efre |
|             |               | ision |          | ideal      | sh** |
|             |               | ideal | **ideal  | (min.)**   |      |
|             |               | (mi   | (min.)** |            |      |
|             |               | n.)** |          |            |      |
+-------------+---------------+-------+----------+------------+------+
| Ice         | Histogram -   | n/a   | n/a      | n/a (200   | 168  |
| Thickness   | thickness     | (n/a) | (10%)    | km)        | hrs  |
+-------------+---------------+-------+----------+------------+------+
|             | Histogram -   | n/a   | n/a (3%) | n/a (200   | 168  |
|             | area          | (n/a) |          | km)        | hrs  |
+-------------+---------------+-------+----------+------------+------+
| Surface     | Total (%      | n/a   | 3% (10%) | 0.25 km    | 24   |
| Type        | cover)        | (5%)  |          | (25 km)    | hrs  |
+-------------+---------------+-------+----------+------------+------+
|             | MY ice (%     | n/a   | 3% (10%) | 0.25 km    | 24   |
|             | cover)        | (5%)  |          | (25 km)    | hrs  |
+-------------+---------------+-------+----------+------------+------+
|             | FY ice (%     | n/a   | 3% (10%) | 0.25 km    | 24   |
|             | cover)        | (5%)  |          | (25 km)    | hrs  |
+-------------+---------------+-------+----------+------------+------+
| Surface     | Temperature   | 0.1K  | 0.5K     | 1 km (100  | 168  |
| Properties  |               | (n/a) | (1.0K)   | km)        | hrs  |
+-------------+---------------+-------+----------+------------+------+
|             | Wetness       | n/a   | 1% (n/a) | n/a (n/a)  | 24   |
|             |               | (n/a) |          |            | hrs  |
+-------------+---------------+-------+----------+------------+------+
|             | Melt pond     | n/a   | 3% (n/a) | n/a (n/a)  | 24   |
|             | coverage      | (n/a) |          |            | hrs  |
+-------------+---------------+-------+----------+------------+------+
|             | Snow cover    | n/a   | n/a (5   | 0.25 km    | 24   |
|             | depth         | (n/a) | cm)      | (25 km)    | hrs  |
+-------------+---------------+-------+----------+------------+------+
|             | Surface       | n/a   | n/a      | n/a (100   | 12   |
|             | albedo        | (n/a) | (0.05)   | km)        | hrs  |
+-------------+---------------+-------+----------+------------+------+
| Ice Motion  | Gridded       | n/a   | 1 cm/s   | 5 km (25   | 12   |
|             | vectors       | (n/a) | (2 cm/s) | km)        | hrs  |
+-------------+---------------+-------+----------+------------+------+
|             | Deformation   | n/a   | (0.5%)   | n/a (25    | 12   |
|             |               | (n/a) |          | km)        | hrs  |
+-------------+---------------+-------+----------+------------+------+

**Table 1.** Science requirements for monitoring sea-ice. This does not
include non-ice products required for polar climate monitoring.
Requirements are derived from Carsey (1992), Jacobowitz (1996), JSC
Working Group (1992) and Rothrock (1997).

Table 1 defines the requirements from the point of view of the
application. In other words, the requirements are not defined to be
realistic in terms of current or future capabilities. It is important to
have this information as an absolute reference against which to measure
development of capabilities. Future goals will attempt to address the
above requirements in a partial manner, gradually reducing the gap
between capability and requirement. This gap may never be closed and
some requirements may have higher priority than others (particularly in
terms of operational needs), but data assimilation provides a framework
within which to aim at this goal.

## Operational Requirements

### Overview

Operational requirements cover both products and implementation. As well
as having a slightly different slant on product requirements, the
operational community has specific requirements in terms of product
timing and delay.

### Operational Observation Requirements

The unclassified requirements of the operational community can be
considered to include those of the scientific community, plus some
additions (which will also be of interest to the scientific community).
Table 2 identifies representative additional requirements.

+--------------+-------------+-------+----------+-----------+------+
| **Product**  | **P         | *     | **Ac     | **Spatial | **R  |
|              | arameters** | *Prec | curacy** | R         | efre |
|              |             | ision |          | esolution | sh** |
|              |             | ideal | **ideal  | ideal     |      |
|              |             | (mi   | (min.)** | (min.)**  |      |
|              |             | n.)** |          |           |      |
+--------------+-------------+-------+----------+-----------+------+
| Ice          | Modal       | n/a   | 10 deg   | n/a (10   | 12   |
| Fractures /  | orientation | (n/a) | (20 deg) | km)       | hrs  |
| Leads        |             |       |          |           |      |
+--------------+-------------+-------+----------+-----------+------+
| Directional  | Mean        | n/a   | n/a      | n/a (n/a) | 12   |
| Ambient      | intensity   | (n/a) | (n/a)    |           | hrs  |
| Noise        | as a        |       |          |           |      |
|              |             |       |          |           |      |
|              | function of |       |          |           |      |
|              | freq.       |       |          |           |      |
+--------------+-------------+-------+----------+-----------+------+

**Table 2.** Additional representative operational requirements for
monitoring sea-ice, in addition to those of Table 1.

### Product Timing

The products must be available on a daily basis for operational use and
for building up long-term climatologies.

One of the advantages of data assimilation is that the products can be
provided at a particular time of day, using the interpolation function
of the procedure. This makes it possible to provide the product at
particular times of each day for most efficient operational use (e.g. 00
GMT, 12 GMT, etc.). It is not expected that the science cmmunity will
have strong views on this precise timing.

The products should be available for operational use within a period of
6 hours (preferable) or 12 hours (acceptable) of the time associated
with the now-cast product. In practice, the extent to which the minimum
requirement is exceeded will depend on a trade-off with product quality,
as data assimilation can be used to extrapolate beyond the time
associated with the last data source. The product could therefore be
made applicable to the time at which the product is received (in effect,
a forecast product). The delay, in this case, is zero hours, but the
quality will be lower than a product that has a delay of 12 hours, but
which is based on very recent data. In practice, the trade-off position
will need to be determined through a program of evaluation and product
tuning.

### Implementation Requirements

The implementations must be sufficiently spaced apart to ensure that
yearly costs are kept to reasonable levels for sponsors of the program.
One implementation every two years is suggested as the target, with the
first being two years after the start of the program. Where practical,
implementations should be combined, which will require careful overall
management of different projects.

The algorithm implementations must be under the control of
representatives of the sponsoring agencies, plus representatives of the
scientific and operational user communities. This is important to ensure
that no single group feels disenfranchised within the process, although
the rate at which the implementation proceeds will depend on operational
resources (with an analogous situation at the R&D phase of the process).
As the program results in implementation of operational algorithms, the
standards of algorithm specification and development are very important.
The following standards are envisaged for support to the implementation
team by the algorithm developers:

-   Rigorous definition of algorithms in appropriate format in approved
    software design language;

-   Modular design for algorithms;

-   Specification of parameters and appropriate values;

-   Provision of prototype software.

The product will need to be maintained and quality-controlled. The
product will also need to be archived once this procedure is complete.

# Arctic-Wide Sea-Ice Product: Proposed Implementation Plan

## Overview of Proposed Infrastructure

The implementation plan involves a strategy for implementing the scheme
(section 5.2) and proposed activities (section 5.3). ***The scheme
described in this document is a straw-man plan***. The proposed scheme
is shown in Figure 3 (compare with Figure 1).

![](media/image3.wmf)

**Figure 3.** The proposed scheme for generation of a global ice
product.

The data assimilation scheme will involve the use of a wide array of
data, not all of it received from satellite. These data will be ingested
in near real time at FNMOC where it is proposed that data assimilation
scheme will be maintained. FNMOC is a designated microwave center of
excellence and hosts the current Navy operational sea-ice model and so
is a logical location for an operational data assimilation scheme.
However, other centers could be used if required (such as NCEP). The
data assimilation scheme will be used to generate a now-cast product and
a forecast product. These will be provided to the National Ice Center
(NIC) for quality checking and to support high resolution, near
real-time, regional ice chart analysis before being disseminated to the
NSIDC for access by scientists. The proposed roles are summarized in
Table 3.

  ---------------------- ------------------------------------------------
  Organization           Proposed Role

  Agencies (NASA, ONR,   Fund R&D.
  NSF)                   

  Navy, NOAA             Fund operational implementation.

  FNMOC                  Host, implement and maintain operational
                         product.

  NIC                    Quality control of product.

  NSIDC                  Product archive center, science community I/F.

  SARP                   Overall coordination of program, recommendations
                         to funding agencies.
  ---------------------- ------------------------------------------------

**Table 3.** Proposed roles of organizations in program. All
organizations listed currently hold SARP membership.

**5.2 Proposed Implementation Strategy: An Operational Data Assimilation
Scheme**

### Overview

It is proposed that the framework for development of an Arctic-wide
sea-ice product be provided by data assimilation. This technique has
been used within the meteorological (numerical weather prediction)
community for some years and has clearly become part of the accepted
methodology involved in weather forecasting and now-casting. Data
assimilation provides the following benefits as an overall strategy for
developing the sea-ice product:

-   It provides a single, conceptual framework for development of
    products;

-   It can make use of all available data and knowledge;

-   It makes use of forward modeling rather than inverse modeling, which
    is conceptually simpler;

-   It supports incremental advances in techniques;

-   It can provide estimated errors along with the product;

-   It supports filtering of diverse data;

-   It supports optimal interpolation of data;

-   It can provide products that are not directly observed.

Data assimilation is distinct from what is termed ***data fusion***.
Data fusion is the combination of data sets to assist in resolving
ambiguities inherent in a single data set, but data fusion does not
imply the use of any physical model. In addition, it does not require
the use of information across the temporal domain. An example of data
fusion would be the use of multi-year ice concentration from synthetic
aperture radar to constrain the NASA team algorithm estimate of sea-ice
concentration from passive microwave data (Beaven *et al*., 1996).
***Data assimilation***, on the other hand, is a broader methodology
characterized by the features listed above, by definition including a
combination of physical models, prior knowledge of a system and diverse
data sets.

The origins of data assimilation techniques derive from the 1950s.
Optimal interpolation techniques were developed first (see Eliassen,
1954, reprinted 1981) and by 1980 much of the data assimilation
methodology for current numerical weather prediction was in place (see
Bengtsson et al., 1981). Numerical weather prediction as an application
of data assimilation is closely analogous to sea-ice monitoring and the
data assimilation techniques developed by that community represent a
valuable resource waiting to be exploited by the sea-ice monitoring
community.

In this section, a brief review of the technique is given, based on the
introduction to data assimilation provided by Errico (1997).

### Data Assimilation: A Simple Case

Data assimilation is a methodology for ingesting diverse data sets and
providing output which, in some "optimum" manner, both filters and
interpolates from the input observations, and provides information on
parameters not sampled directly by the observation network. The
technique requires models for these three functions and information on
error statistics of observations in order to arrive at a judgment
regarding how to deal with the input data. Consider, as an example, an
observation of a geophysical parameter and a model prediction of the
parameter provided for the same location and time. One simple
assimilation scheme is the maximum likelihood technique, which makes use
of error statistics associated with the observation (O) and the
prediction (P), such that:

E = P + (O-P){σ~p~^2^ / (σ~p~^2^+ σ~o~^2^ )} (1)

where E is the estimated state and σ~p~^2^ and σ~o~^2^ represent the
error variances associated with the predicted and observed states,
respectively. It can be seen that if the prediction error is much larger
than the observation error, then the estimate tends towards the observed
state. If, conversely, the prediction error is much larger than the
observation error, then the estimate tends towards the prediction. Thus,
the quality of the estimate depends strongly on errors in both the
predictions (which may be model-driven) and observations (which may be
sensor-driven) and is basically a weighted mean of the two.

A range of estimation methods are available with which to determine how
to make use of error statistics, beyond that of maximum likelihood. A
Bayesian approach is conceptually useful, but rather impractical in the
sense that it requires probability density error functions for all input
data. A variational approach does not demand such complete error
statistics but does demand some sort of prior knowledge of the data
characteristics to support its use. A simple example of a variational
approach would be to model the data as a linear function of some
measurement parameter, so that the estimate can be obtained by a
straight-line fit which allows interpolation to the measurement
parameter of interest. A variational procedure can be extended to
include other types of information through a weighting procedure
(weighted sum of squares), based on relative error statistics. In the
case where the error statistics are not distributed normally, there are
estimation procedures more suited. An example is the case where the
observations are contaminated by a relatively small number of points
which have extremely large errors, thus skewing the distribution. The
choice of estimation procedure therefore depends strongly on the depth
of knowledge of error statistics (including co-variance of different
parameters) and the extent to which the functional relationships of
different parameters are known. The success of any of these techniques
can be seen to depend critically on the accuracy with which error
statistics are represented in the assimilation scheme, and specification
of errors is therefore a most critical area of research for data
assimilation.

### Data Assimilation: More Sophisticated Approaches

As we move from a simple scalar system to a complex 4 dimensional system
(with the temporal domain as the fourth dimension), the data
assimilation scheme needs to become progressively more sophisticated.
The difficulties of this more complicated scenario include the
following:

-   the observation and model points are not co-located (optimal
    interpolation required);

-   the number of variables are large (scalar-to-vector generalization
    required);

-   the observed parameters are different from the modeled parameters
    (physical model required).

Non co-location of points requires some sort of interpolation. The first
generation of numerical weather prediction schemes, introduced after the
second world war, were designed to deal with this requirement through
interpolating observed data to a regular network of grid-points. Since
then, the advent of non-synoptic data has made the procedure more
complicated. Linear interpolation can make use of the radial distance to
a measurement location plus some weight which reflects the quality of
each point. There are also non-linear schemes, many of which are
reviewed by Gustafsson (1981). With more than one variable, a
multi-variate statistical interpolation scheme is required, and in their
design these can make use of cross-correlations between the different
variables. When the observed and modeled parameters are different, it is
necessary to use physical modeling and a data assimilation scheme can
typically include models relating observed parameters to model input
parameters as well as a model for the general system behavior. An
example of this would be radiative transfer modeling.

The Kalman filter represents one practical approach for assimilating
data, and one which has been applied to assimilation of data in sea-ice
studies (e.g. Thomas and Rothrock, 1993). The following summary of the
Kalman filter approach is taken from articles by Thomas and Rothrock
(1989 and 1993), Rothrock and Thomas (1992) and Thomas *et al*. (1996).

The discrete Kalman filter makes use of a state vector which describes
the modeled system at time t(n). The state vector is a 1-D matrix
describing the state of the system, as follows:

> **X** = (X~1~, X~2~, X~3~,... X~S~)^T^ (2)

where s is the number of system conditions which are considered and
X~1~...X~S~ are parameters which define the state of the system at time
t(n) (s could, for example be defined as 4 and X~1~...X~S~ could be
defined as partial ice type concentrations). Superscript T indicates the
transpose of the matrix. The system evolves with time such that the
state of the system at time t(n+1) is modeled as:

> **X**(n+1) = **Φ**(n+1\|n) **X**(n) + **S**(n+1) + **V**(n+1) (3)

where the (s x s) transition matrix **Φ** describes the physical
processes which determine the evolutionary form of the system; the (1 x
s) matrix **S** is known as the in-homogeneous source term and the (s
x 1) vector **V** describes the system noise (often assumed to be
Gaussian white noise). The vector **V** can be thought of as physical
processes that fail to be modeled, or are modeled imprecisely within the
system. This "noise" is often further assumed to have the constraint
that the co-variance matrix for **V** is limited to terms along the
leading diagonal. A similar constraint is assumed to apply to
measurement error matrix **W**, which is included in the relationship
between the state of the system and the observed variables as a random,
additive error term, as follows:

> **Z**(n) = **H**(n) **X**(n) + **W**(n) (4)

where **Z** are the measurements, and **H** relates the observations to
the state variables. For example, equation (4) could relate brightness
temperatures from SSM/I to ice concentrations (Rothrock and Thomas,
1992).

In Rothrock and Thomas (1992), these equations are used in compiling
both predictions and filtered results. The prediction, **X**(n+1\|n), is
based on the model and on data up to time t(n). The filtered estimate,
**X**(n+1\|n+1), is based on the difference between the prediction
(**X**(n+1\|n)) and the observation at time t(n+1). If the measurement
errors are large, then the so-called gain is small and more weight is
applied to the prediction, and vice versa. This procedure is then
applied in a reverse form (stepping back in time). This latter procedure
is a useful characteristic of data assimilation schemes.

The Kalman filter, as in any data assimilation scheme, can provide an
estimate of the error in the estimated state of the system, as well as
providing the estimate itself. However, the value of the technique
depends sensitively on the assumptions and knowledge regarding error
terms and the physical model. In general, the error associated with the
model (**V**) is less easy to determine than the measurement error
(**W**), as models are notoriously difficult to evaluate. Any invalid
assumptions, or poor estimates of error, will serve to bias the output
from the Kalman filter. Rothrock and Thomas (1992) show that the error
in the simple case of a single variable reduces, in the case of the
Kalman filter, to:

1/σ~T~^2^ = 1/(2R) + sqrt{1/(4(σ~O~^2^ )^2^) + 1/(σ~P~^2^ σ~O~^2^)} (5)

where σ~T~^2^ is the filter error covariance, σ~P~^2^ is the model error
covariance and σ~O~^2^ is the measurement error covariance. It can be
seen that if the measurement error is very small compared to the model
error, then the filter error tends towards the measurement error. If the
converse is true, then the model can reduce significantly the error from
that attributable to the measurements (to {σ~O~^2^ sqrt(n)}, where n is
the number of observations). Thus, the Kalman filter has similarities,
in the limit, to the simple Maximum Likelihood scheme described earlier,
and all data assimilation methods basically weight the use of data in
inverse proportion to their relative errors.

### Data Assimilation: Application to Sea-Ice Studies

Much important initial work in applying data assimilation techniques to
sea-ice studies has been carried out by Rothrock and Thomas. Thomas and
Rothrock (1989) incorporated both SMMR observations and buoy data into a
sea-ice model using the discrete Kalman filter. This was an early
example of data assimilation applied to the problem of realistic
modeling of sea-ice behavior, building from earlier work by Thorndike
(1988). Thomas and Rothrock specified the measurement model **H**
include the two major principal components from the SMMR data (which
describe 99% of the variation in the 10 channel SMMR data set), modified
by surface temperatures determined from the buoy data (a linear
regression model), and based also on season (see equations 2-4).

> **H** = \[H~1,OW~ H~1,FY~ H~1,SY~ H~1,MY~\]
>
> \[H~2,OW~ H~2,FY~ H~2,SY~ H~2,MY~\] (6)

where the first subscript indicates the principal component and the
second subscript represents the surface partial ice type concentration.
H~1,FY~, for example, will vary as a function of season and surface
temperature.

The physical model, as specified in the transition matrix** Φ**, is used
to determine the system evolution between time-steps, as follows:

> **Φ** = \[1-g m~FY~ 0 0\]
>
> \[g 1-a- m~FY~ 0 0\]
>
> \[0 a 1-a 0\]
>
> \[0 0 a 1\] (7)

where g is the ice growth parameter (1 during winter, 0 during summer,
with open water assumed to take 1 day to freeze over), m is melt of ice
(assumed to be during summer and to affect first-year ice only) and a is
the again parameter (a=1 during freeze-up, 0 at other times). This
transition matrix allows for such processes as changing first-year ice
to multi-year at the end of summer. Thomas and Rothrock extend this
model to include the case of melt-pond formation. Their approach is
practical for the Arctic Ocean, where buoy data are available. However,
they point to the difficulty of applying this approach to the Antarctic
and the marginal ice regions in the northern hemisphere where data are
sparse.

Thomas and Rothrock (1993) extended their work to apply data
assimilation to the problem of determining the mass balance of the
Arctic Ocean, using 7 years of buoy-derived ice motion fields and
satellite passive microwave observations. They use 7 cells in the Arctic
Ocean in order to account for regional variations in climatology and
dynamics / thermodynamics. Their transition matrix accounts for ice melt
and growth, as before. Their transition matrix is extended, however, to
account for ice convergence (which will reduce the concentration of
first-year ice) and general advection based on buoy data. The advection
and convergence observations are determined via an optimal interpolation
procedure, weighted between the buoy observations and free drift based
on geostrophic winds (modified by a seasonal drag factor and turning
angle). Where the error in the buoy interpolation is estimated to be
large, the free drift estimates are used. Thomas and Rothrock used this
technique to evaluate regional differences in climatology and dynamics
and significant sources of error. SSM/I-derived ice concentrations were
identified as a major source of uncertainty in interpretation of the
results. The same technique was applied further by Thomas et al. (1996),
who looked at ice thickness distributions across the Arctic.

## Proposed Activities

### Overview

Research activities in support of the proposed program will not consist
entirely of new initiatives. Much existing and past work has contributed
to the development of techniques that are relevant, although many of
these may need to be modified or extended to make them applicable.
These, and areas of research which are more completely lacking, will
require development funds. In this section, a review of the application
of data assimilation techniques to sea-ice monitoring is provided, along
with recommended development plan for future work.

### An Improved Coupled Ocean-Ice Model

The existing operational ice model, PIPS 2.0, has several weaknesses
that would severely limit its value as a potential cornerstone of an
operational data assimilation scheme. In order to support data
assimilation and provide output that more closely meets the requirements
listed in section 4, the model needs to undergo a program of upgrade in
several key areas. Before addressing these, it is worth summarizing the
history of the Navy ice modeling program.

The U.S. operational sea-ice modeling program is relatively new, and is
based on modeling design work carried out in the late 1970s. The
operational community, with a requirement for short-term forecasting of
sea-ice conditions, recognized the need for an operational sea-ice model
in the 1980s and also recognized that this would need to be constantly
initialized and updated by observations. The model, run at FNMOC and
called the Polar Ice Prediction System (PIPS), is an implementation of
the Hibler dynamic-thermodynamic sea-ice model coupled with the Cox
ocean model (Preller, 1985), which produces estimates of ice thickness,
ice concentration and ice motion with forecasts up to 5 days.

The PIPS model uses daily information on ocean currents, oceanic heat
flux, incoming solar radiation, sensible heat flux, surface air
temperatures, geostrophic wind stresses and ice concentration. The
oceanic variables are based on monthly mean values derived from the Cox
ocean model (Hibler and Bryan, 1987) and the ocean model has 15 levels
which increase in thickness with depth (the top layer, 30 m in
thickness, is the mixed layer). The grid cells have a size of 17-33 km
(depending on latitude). The ice and ocean models in the current version
of PIPS (2.0) are coupled through exchange of salinity, heat and
momentum (wind-ice-current stresses). The model is initialized using
climatological temperature and salinity data and is initialized with an
"average" ice thickness and ice concentration for winter. The model also
includes freshwater input from eight major rivers that flow into the
Arctic. Daily forcing, in terms of heat fluxes and surface winds, is
provided by the Navy Operational Global Atmospheric Prediction System
(NOGAPS).

PIPS 2.0 is updated on a daily basis with SSM/I ice concentrations
derived from the CAL/VAL ice concentration algorithm (Preller, 1985*)*.
The assimilation procedure is carried out by replacing the modeled ice
concentration estimates by the SSM/I-derived estimates. This procedure
results in some areas being assigned an increased ice concentration and
some areas being assigned a decrease in ice concentration. The ice
thickness field is made consistent with the replacement ice
concentration field, in a partially heuristic manner such that adjusted
ice thicknesses are changed in proportion to the change in ice
concentration. The surface temperature fields are likewise modified to
ensure that areas of increased ice concentration are set at freezing
point and areas of reduced concentration are set above freezing point
(to avoid immediate refreezing). Thus, while SSM/I observations are
assimilated into the PIPS 2.0 coupled ice-ocean model, the assimilation
procedure is crude and, effectively, applies full weight to the SSM/I
observations at the expense of the model predictions.

The ice concentrations which are assimilated into PIPS are obtained
using the CAL/VAL algorithm, which is known to be weak in some important
respects (such as in over-estimating ice concentration in winter pack
ice). These weaknesses are fed into the ice model, with no weighting
procedure to offset any algorithm bias. Ice concentrations derived from
NIC ice charts have less bias, as they are based on a wide variety of
data and expert knowledge of local climatology and weather patterns. A
comparison of the model output generated using initialization with the
daily SSM/I ice concentrations against the weekly NIC ice charts
demonstrates that the improved accuracy and spatial resolution afforded
by the NIC ice charts is more than offset by their poor temporal
resolution. However, improvement of the PIPS 2.0 output is envisaged, by
replacing the CAL/VAL algorithm with the NASA Team algorithm in the
short term. A more thorough evaluation and improvement of the SSM/I ice
concentration algorithm is planned in the medium term.

The features requiring upgrade are as follows:

-   Replacement of climatological ocean input by a more accurate ocean
    model which assimilates observational data, plus more precision in
    terms of vertical resolution in the ocean model (especially better
    treatment of the mixing layer), and improved bathymetric spatial
    resolution. Also, replacement of the Bryan-Cox primitive equation by
    a free surface formulation.

-   Replacement of the crude, and partially heuristic, data assimilation
    methodology. A physical model which supports data assimilation
    should be implemented within the framework of a data assimilation
    schema, where observational inputs do not routinely replace model
    predictions, but are weighted in their use, based on some measure of
    their accuracy. A simple example would be weighting of SSM/I ice
    concentration data based on weather filter values.

-   Replacement of continuum mechanics (based on viscous-plastic
    rheology), which cannot support fractures, by an anisotropic,
    elastic-viscous-plastic rheology with fracture mechanics.

-   Improvement of grid spacing to about 10 km.

-   Addition of ambient noise prediction module, for operational
    requirements.

-   Assimilation of a wider array of data, including (initially) SSM/I,
    synoptic winds and heat flux, and extending to include radiation
    (COAMPS), ice motion vectors from the IABP and 85 GHz SSM/I, and
    eventually ice surface parameters from QUICKSCAT and MODIS.

-   More efficient implementation using distributed shared memory
    architecture. This type of consideration is important in order to
    support the intensive computational demands of a data assimilation
    scheme.

These improvements are already being initiated through Navy investment,
with a planned PIPS 3.0 implementation.

### Assimilation of Ice Concentration

Passive microwave observations are, at present, the primary source of
data on global-scale sea-ice coverage. The passive microwave brightness
temperatures are applied to an inverse modeling scheme, but all these
schemes contain major biases, to varying degrees. These include the
following:

-   Errors resulting from natural variations in brightness temperatures
    associated with reference areas of open water and sea-ice types.

-   Geophysical cross-talk errors. In the case of sea ice concentration
    estimation, for example, a manifestation of this error is sea-ice
    concentration being correlated, erroneously, with surface
    temperature.

-   Surface melt - related errors, which cause most SSM/I algorithms to
    under-estimate ice concentration. Errors in ice concentration tend
    to double to 10-20% for algorithms (Steffen et al, 1992) during
    summer and the errors are systematic. Russian drift stations show
    that, at times, as much as 45% of the ice may be covered by melt
    ponds in mid July (Thomas, 1993), and this will be reflected in
    lower estimates of ice concentration from passive microwave data.

-   Errors related to the presence of new and young ice types, which are
    not explicitly accounted for in the operational SSM/I ice
    concentration algorithm. In freeze-up conditions, there will always
    be a tendency for ice concentration to be under-estimated as a
    result of this effect, as the emissivity of new ice can approach
    that of open water (0.45). Steffen and Schweiger (1991) found that
    the presence of nilas and young ice could create an under-estimate
    of ice concentrations from SSM/I of 9% (with global tiepoints) and
    4% (with local tiepoints), using the NASA Team algorithm.

-   Atmospheric transmission 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 fairly crude weather filters to reject ice
    concentrations over open ocean.

In a data assimilation scheme, this inverse modeling approach would be
replaced by a forward modeling scheme, in which the brightness
temperatures are predicted based on estimated surface (and maybe
atmospheric) conditions and a forward modeling scheme could be developed
which attempts to reduce the errors listed above. Forward modeling
starts with a specification of the surface (e.g. partial ice
concentrations) and attempts to predict the observation parameter (e.g.
brightness temperature). Inverse algorithms attempt the reverse.

Steffen and Schweiger (1991) 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 beneficial
in the Arctic, where there are marked regional differences in ice
climatology. Routine incorporation of local tiepoints in a forward
modeling procedure (using other data sources) would improve the
estimation procedure considerably. Some of the atmospheric artifacts
present within passive microwave observations could be treated more
appropriately by applying a weather filter that weights the use of the
SSM/I data in an assimilation scheme, rather than replaces the
observations (as conventionally happens). Geophysical cross-talk is a
characteristic of inverse modeling which would be avoided by forward
modeling. Surface melt-related errors could also be addressed through
the assimilation procedure. During summer, greater weight could be
placed on the model predictions than on the observations, to reduce the
impact of "erroneous" low, summer ice concentrations suggested by the
passive microwave data (modifying the gain factor referred to in section
5.2.1). The problem of new ice has received some recent attention. A
thin ice formulation of the NASA team algorithm has recently been
published, for example, and could be investigated as a potential
addition to the existing source of information (Cavalieri, 1994). Many
of these issues are planned to be addressed through Navy funding of an
NIC post-doctoral research position.

Perhaps a more productive long-term approach to improving the
assimilation of ice concentration would be to assimilate other data
besides SSM/I. One such data source is synthetic aperture radar (SAR).
SAR has only recently become available on an operational basis through
the Canadian RADARSAT program (previous SAR sensors had coverage which
weas too limited to be operationally useful). Follow-on missions include
the European Space Agency ENVISAT platform and RADARSAT II. Wide swath
modes of these sensors provide extensive coverage and Lagrangian
tracking (using the RGPS) has been used to map discrete fracture zones
that have been shown for the first time to extend across wide regions of
the Arctic. Automated processing of these data has proved more difficult
and to date these data have not been used operationally in any automated
fashion. Even ice tracking, the most mature SAR technique, includes a
manual procedure. However, SAR is the focus of intensive efforts to
develop automated products and the advent of dual polarization SAR data
ENVISAT may well result in development of a successful ice
classification algorithm, although wide coverage dual polarization data
is not planned to be available within the current generation of
spaceborne SAR sensors. Thus SAR has some potential, but a number of
limitations related to data coverage, processing demands and
ambiguities.

In contrast, scatterometer data offers considerable promise for wide
coverage observations of sea-ice. QUICKSCAT is planned for launch later
this year and these data will be available in near real time.
Scatterometer data have a major advantage over SSM/I data in being
insensitive to atmospheric artifacts, and demonstrations of the
potential of scatterometer data for sea-ice monitoring have proved
persuasive using the ERS-1 scatterometer and NSCAT, on-board ADEOS. The
nominal spatial resolution is somewhat lower than that of SSM/I, but
resolution enhancement techniques can provide comparable resolution.
Assimilation of QUICKSCAT data should be a key objective of a data
assimilation scheme.

MODIS too has potential, given the possibility of an effective ice/cloud
filter. If this proves successful, then MODIS could provide an ice/water
classification product.

It can therefore be appreciated that, since the advent of SSM/I, there
has been a major growth in the range and operational value of data-sets
which are now available to the sea-ice community. When combined with
sensors expected to be available on an operational basis within the next
2-3 years, it becomes clear that reliance on SSM/I alone for routine
Arctic-wide sea-ice monitoring becomes indefensible, particularly given
its known weaknesses (see Appendix A). As a result, it is important to
plan now for development of an operational scheme that will include at
least QUICKSCAT and possibly also MODIS and wide-swath SAR sensors.

### Assimilation of Ice Motion Vectors

The International Arctic Buoy Program has been providing observation
across the Arctic since 1979. Although these buoys are used as an
important source of information for modeling of the Arctic through
providing source data for gridded temperature / wind fields (which
"drive" PIPS 2.0), the buoy motion information is not used in PIPS 2.0.
The motion vectors provide sparse spatial distribution but high temporal
resolution and use of these data could be crucial to realistic modeling
of ice dynamics in the Arctic. At any one time, there are an average of
about 15 buoys in service and these provide data in near real time. This
represents a valuable resource for a data assimilation scheme.

Ice motion fields from satellite data complement ice motion data from
buoys (by providing high spatial resolution, but relatively lower
temporal resolution) and ice motion vectors represent perhaps the most
mature sea-ice product to be derived from satellites. These were first
derived manually in the early 1980s using Seasat SAR data. Later, the
technique was partially and fully automated in various forms, but the
most successful of these algorithms has been based on a hierarchical
cross-correlation technique developed by Fily and Rothrock (1987). A
range of other techniques have been designed, with the aim of being
applied to the marginal ice zones where the Fily and Rothrock technique
is less successful (e.g. Peddada and Chang, 1996). However, the Fily and
Rothrock technique has proved to be remarkably robust, and has formed
the basis for the design of the Geophysical Processor System (GPS) at
ASF; the new Radarsat GPS (RGPS) system currently being evaluated prior
to installation at ASF, as well as the basis for the tracker components
of commercial sea-ice packages. These motion vectors are typically
derived for a period of up to a "few" days, representing the period of
revisit of satellite sensors that give reasonable or good coverage of
the surface at polar latitudes. The Fily and Rothrock technique has been
applied successfully to SSM/I 85 GHz data (where the spatial resolution
is better than other passive microwave channels), AVHRR data and SAR
data. The algorithm that is used varies from implementation to
implementation, in details of filtering, correlation window size, etc.,
but remains relatively generic. The Lagrangian approach of the RGPS is
potentially useful for an operational scheme, but the manual procedure
associated with correcting "erroneous" tie-points would need to be
automated and the processing burden of using basin-wide RADARSAT data in
near real time is considerable. A similar scheme based instead on SSM/I
85 GHz data (perhaps controlled using buoy observations) would be worth
investigating.

In order to make use of these ice motion data two main developments are
required. The most important was mentioned earlier and is a conceptual
issue related to how to ingest ice motion information into a dynamic ice
model in such a way as to influence the behavior of the model in a
realistic manner. A crude, instantaneous forcing of the model upon each
ingestion of ice vectors, as has been tried with PIPS, is undesirable.
This problem is non-trivial and requires some careful consideration.

The second required development is to provide an ice tracker which is
generic enough to operate on any data which meets criteria of revisit
time and sampling capability. In principle, a cross-correlation tracker
could be designed as a batch processor to operate on image pairs which
are of a certain type (Radarsat, 85GHz SSM/I, AVHRR), cover the same
area and were recorded within a certain number of days of each other.
Any manual element of an algorithm would need to be removed and the
operating parameters associated with the algorithm would need to be
tuned for each sensor. The constant supply of motion vectors from IABP
buoy data and from a range of satellite sensors would improve
considerably the gross behavior of the model. Initially, it would make
sense to focus efforts on ingestion of 85 GHz SSM/I ice motion and
IABP-derived motion. At a later date, motion vectors can be added from
SAR and AVHRR and any other satellite sensors if the product quality is
good enough and the algorithm can be fully automated.

### Assimilation of Radiative Flux

The potential exists to obtain radiative flux from space-borne sensors.
The benefits of incorporating this information into a data assimilation
scheme are considerable, as these fluxes are essential to any realistic
modeling of sea-ice thermodynamics. According to one study, a 10% change
in the long-wave radiative flux can change the computed mean ice mass by
some 36%. Radiative flux would also assist in the forward modeling of
data from other spaceborne sensors, particularly SSM/I.

Key *et al*. (1996) focused attention onto work preparatory to the
assimilation of radiative fluxes. Whilst existing climatologies may be
used to prescribe fluxes, modified perhaps using relatively recent
satellite-based observations of cloud and surface conditions,
parameterizations of the long-wave and short-wave radiative fluxes are
more appropriate for a data assimilation scheme where short term
variability in fluxes are important to consider. In these
parameterizations, the short-wave flux is usually taken as a function of
the solar constant and zenith angle, with parameters modified to be
appropriate for the polar regions. In some cases, near surface vapor
pressure is used as an input variable. When cloud is present, the
short-wave flux is often multiplied by a simple cloud factor, which can
be given as a function of latitude and may be non-linear as a function
of cloud cover, to account for the influence of low cloud (which affects
short-wave radiation the most and tends to increase in cover as high
cloud cover increases). The most accurate parameterization for
short-wave radiative flux was judged by Key et al. to be one which
included surface albedo and cloud optical depth. The long-wave radiative
flux is taken to be a function (to the fourth power) of near surface air
temperature. When cloud is present, the fractional cover needs to be
taken into account. Some parameterizations are more sophisticated than
others. An example is a parameterization of long-wave radiative flux
which accounts for cloud base temperature, which is not readily observed
and so is of limited utility for a data assimilation scheme. Key et al.
identify parameterizations of radiative flux which appear to be most
successful in comparison with in situ measurements.

Some workers, including Key, have already demonstrated that radiative
flux can be derived from AVHRR. The challenge with AVHRR is to derive
this information in an operational manner. The launch of MODIS in 1998
should lead to a significant enhancement in the availability of routine
observations related to radiative flux in the polar regions. Products
that are planned to be obtained routinely, in near real time, include
surface albedo, cloud fraction and ice surface temperature. MODIS could,
potentially, advance sea-ice remote sensing significantly, although at
the present time it is not clear how successful these products will be.

A clear objective should of a data assimilation scheme should be to make
use of MODIS in supporting development of an improved ice growth and
decay model within PIPS 2.0. These radiative flux observations should
also be used to develop a reliable forward modeling algorithm for SSM/I
data.

### Assimilation of Sea-Ice Thickness

Ice thickness is perhaps the parameter of greatest interest to the
scientific community, as it provides the key (along with ice
concentration) to energy exchange between the ocean and atmosphere and
yet has proved to be the most difficult parameter to derive through the
use of measuring tools alone. The best hope for derivation of this
parameter with significant coverage appears to lie in the application of
a data assimilation approach. Although the methodology behind the
Radarsat Geophysical Processor System appears to offer some potential
for retrieval of ice thickness, it would depend on near real time
provision of these products to FNMOC. Such a possibility appears to be
remote.

Ice thickness is currently provided as output from the PIPS 2.0 model,
but it is not constrained except through some relatively crude
climatologically-based rules. There is considerable room for improvement
of model output by incorporation of ice thickness observations, even if
these are relatively sparse. The benefits of implementing such a scheme
are considerable in terms of the accuracy with which energy balance
calculations may be carried out for the Arctic, and hence the extent to
which the role of the Arctic in global climate is established.

Assimilation of ice thickness data for the region of the SHEBA test
site, again using the Kalman filter, is the subject of a current study
led by R. Lindsay (personal communication). This represents a good test
case for the assimilation of diverse data sets, including linear ice
thickness tracks from submarine, point observations (moored buoys), 2D
spatial data (RGPS-derived ice thickness histograms) and other data. A
physical dynamic-thermodynamic model, which uses SHEBA test site
observations of radiative flux and other parameters, is used to evolve
the system. This project should result in an improved model for ice
growth and decay based on radiative flux, which should be useful for
PIPS 2.0 and other thermodynamic sea-ice models and could be tied in to
work carried out on incorporating MODIS observations in a data
assimilation scheme.

The SHEBA ice thickness assimilation project could be used as the basis
for the design of an operational assimilation scheme, although it would
need to be distinct from the existing SHEBA ice thickness assimilation
scheme by relying solely on data that are available in near real time.
Unfortunately, many of the data sources used in the SHEBA ice thickness
assimilation project are unlikely to be available in near real time, and
so assimilation of ice thickness observations is unlikely to be possible
in the near future. Instead, the best hope seems to be to improve
constraint on the ice thickness model in PIPS through assimilation of
radiative flux and improved assimilation of ice concentration and ice
motion.

###  {#section .unnumbered}

##  {#section-1 .unnumbered}

#   {#section-2 .unnumbered}

##  {#section-3 .unnumbered}

# Summary of Recommendations

In summary the following recommendations are made:

-   NASA should undertake to lead a program of Arctic-wide sea-ice
    product development that would be coordinated with the operational
    community through ONR. This program should ideally be a joint agency
    program involving NSF and NOAA;

-   ONR should undertake to liaise with NASA to ensure that the planned
    PIPS 3.0 model development is consistent with NASA plans and meets
    scientific requirements.

-   The U.S. Navy, through FNMOC, should undertake to support the
    implementation of a data assimilation scheme, subject to successful
    demonstration of techniques.

-   A working group, oversight committee, or some other formal liaison
    mechanism should be established to manage the program.

# References

1.  Beaven, S.G., Gogineni, S. and Carsey, F.D., 1996, "Fusion of
    satellite active and passive microwave data for sea-ice type
    concentration estimates", IEEE Trans. Geosci. Rem. Sens., Vol 34,
    No. 5, pp. 1172-1183.

2.  Bengtsson, L., Ghil, M. and Kallen, E. (eds.), 1981, "Dynamic
    Meteorology: data assimilation methods", publ. Springer-Verlag
    (series: Applied Mathematical Sciences Vol. 36).

3.  Carsey, F.D. (ed.), 1992, "Microwave Remote Sensing of Sea-Ice",
    American Geophysical Union, Monograph No. 68.

4.  Cavalieri, D., "A microwave technique for mapping thin sea-ice",
    Jnl. Geophys. Res., vol. 99, no. C6, pp. 12,561-12,572, 1994.

5.  Comiso, J. and Kwok, R., 1996, "Surface and radiative
    characteristics of the summer Arctic sea-ice cover from multi-sensor
    satellite observations", Jnl. Geophys. Res., Vol. 101, 28,397-28416,
    Dec. 15.

6.  Daley, R., 1997, "Atmospheric data assimilation", in "Data
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    "Assimilation of observations in meteorology and oceanography", Met.
    Soc. Japan, 13-17 March, 1995, Tokyo, Japan, pp. 209-219.

7.  Eliassen, A., 1981, "Provisional report on calculation of spatial
    covariance and auto-correlation of the pressure field", in Bengtsson
    *et al*. (1981), pp. 319-330.

8.  Errico, R., 1997, "Introduction to data assimilation", presented at
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    evaluate new approaches to sea ice observation", Boulder, Colorado,
    3-5 December 1997.

9.  Fily, M. and Rothrock, D.A., 1987, "Sea ice tracking by nested
    correlations", IEEE Tran. Geosci. Rem. Sens., Vol. GRS-25, pp.
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10. Ghil, M. and Malanotte-Rizzoli, P., 1991, "Data assimilation in
    meteorology and oceanography", Adv. Geophysics, Vol. 33, pp. 141-
    266.

11. Gustafsson, N., 1981, "A review of methods for objective analysis",
    in Bengtsson *et al*. (1981), pp. 17-76.

12. Hibler, W.D., 1979, "A dynamic-thermodynamic sea-ice model", Jnl.
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13. Hibler, W.D., 1980, "Modeling a variable thickness sea-ice cover",
    Monthly Weather Review, Vol. 108, pp. 1943-1973.

14. Hibler, W.D. and Bryan, K., 1987, "A diagnostic ice-ocean model",
    Jnl. Phys. Oceanogr., Vol. 17, pp. 987-1015.

15. Jacobowitz, H. (ed.), 1996, "Climate measurement requirements for
    the National Polar-orbiting Operational Environmental Satellite
    System", Workshop Report, University of Maryland, College Park,
    27-29 February, 1997, NOAA Contract Report, No. NA57GPO576.

16. JSC Working Group, 1992, "Scientific Concept of the Arctic Climate
    System Study (ACSYS)", WMO/TD No. 486.

17. Key, J.R., 1995, "The cloud and surface parameter retrieval (CASPR)
    system for polar AVHRR: user's guide", version 1.0A, Cooperative
    Institute for Research in Environmental Sciences report, University
    of Colorado, 71 pp..

18. Key, J.R., Silcox, R.A. and Stone, R.S., 1996, "Evaluation of
    surface radiative flux parameterizations for use in sea-ice models",
    Jnl. Geophys. Res., Vol. 101, C2, pp. 3,839-3,849.

19. Maslanik, J.A. and Maybee, H., 1994, "Assimilating remotely-sensed
    data into a dynamic-thermodynamic sea-ice model", Proc. IGARSS 1994,
    Pasadena, CA, pp. 1306-1308.

20. Peddada, S. and Chang. T., 1996, "Bootstrap confidence region
    estimation of the motion of rigid bodies", Jnl. of American Stat.
    Assoc., Vol. 91, No. 433, pp. 231-241.

21. Preller, R.H., 1985, "The NORDA/FNOC Polar Ice Prediction System
    (PIPS) - Arctic: A technical description", NORDA Report 108, 60 pp.,
    Naval Ocean Research and Development Act., NSTL, Miss.

22. Rothrock, D.A., 1997, Table on Polar Sea Ice Data Requirements -
    v.5.3, 27 May 1996, in Weaver, R., "Data Sets for Sea Ice
    Monitoring", presented at workshop on "Sea Ice Monitoring into the
    next Millenium: Workshop to evaluate new approaches to sea ice
    observation", Boulder, Colorado, 3-5 December 1997.

23. Rothrock, D.A. and Thomas, D.R., 1992, "Ice modeling and data
    assimilation with the Kalman Smoother", in Carsey, F.D. (ed.)
    "Microwave remote sensing of sea-ice", publ. American Geophysical
    Union, pp. 405-418.

24. Steffen, K., Key, J., Cavalieri, D., Comiso, J., Gloersen, P., St.
    Germain, K. and Rubinstein, I., 1992, "The estimation of geophysical
    parameters using passive microwave algorithms", in Carsey, F (ed.)
    "Microwave Remote Sensing of Sea Ice", Geophysical Monograph 68,
    AGU, 1992.

25. Steffen, K., Schweiger, A., "NASA team Algorithm for sea ice
    concentration retrieval from Defense Meteorological Satellite
    Program Microwave Imager: comparison with Landsat Satellite
    Imagery", Jnl. Geophys. Res., 96, 21,971-21,987, 1991.

26. Talagrand, O., 1997, "Assimilation of observations: an
    introduction", Proc. 2^nd^ symp. on "Assimilation of observations in
    meteorology and oceanography", Met. Soc. Japan, 13-17 March, 1995,
    Tokyo, Japan, pp. 209-219.

27. Thomas, D., "Arctic sea-ice signatures for passive microwave
    algorithms", Jnl. Geophys. Res., 98, C6, pp. 10,037-10,052, 1993.

28. Thomas, D., Martin, S., Rothrock, D.A. and Steele, M., 1996,
    "Assimilating satellite concentration data into an Arctic sea-ice
    mass balance model, 1979-85", Jnl. Geophys. Res., C9, pp.
    20849-20,868.

29. Thomas, D.R. and Rothrock, D.A., 1989, "Blending sequential scanning
    multi-channel microwave radiometer and buoy data into a sea ice
    model", Jnl. Geophys. Res., Vol. 94, C8, pp. 10,907-10,920.

30. Thomas, D.R. and Rothrock, D.A., 1993, "The Arctic Ocean Ice
    Balance: A Kalman Smoother Estimate", Jnl. Geophys. Res., Vol. 98,
    C6, pp. 10,053-10,067.

31. Thorndike, A.S., 1988, "A naïve zero-dimensional sea-ice model",
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32. Weaver, R., (ed.), 1997, White Paper on "Sea ice monitoring into the
    next millenium: new approaches to sea ice observation", Boulder,
    Colorado, 3-5 December 1997.

# 8. Glossary {#glossary .unnumbered}

AMSR Advanced Microwave Scanning Radiometer

ASF Alaska SAR Facility

ASL Arctic Submarine Laboratory

AVHRR Advanced, Very High Resolution Radiometer

AVS Application Visualization System

CCRS Canadian Centre for Remote Sensing

CIS Canadian Ice Service

DAAC Data Active Archive Center

DERA Defence Evaluation and Research Agency (UK)

DMI Danish Meteorological Institute

ERS ESA Earth Resources Satellite

ESA European Space Agency

ESIP Environmental Science Information Partner

FNMOC Fleet Numerical Meteorology and Oceanography Center

IIP International Ice Patrol

JPL Jet Propulsion Laboratory

KU University of Kansas

NASA National Aeronautics and Space Administration

NCEP National Center for Environmental Prediction

NDRE Norwegian Defense Research Establishment

NERSC Nansen Environmental Remote Sensing Centre, Norway

NESDIS National Environmental Satellite, Data and Information Service

NIC National Ice Center

NOAA National Oceanic and Atmospheric Agency

NOW North Open Water (north Baffin Bay)

NPGS Naval Postgraduate School, Monterey

NRL Naval Research Laboratory

NSCAT Japanese ADEOS satellite wind scatterometer

NSIDC National Snow and Ice Data Center

NSIPS Navy Satellite Image Processing System

NWS National Weather Service

OOAR Office of Oceanic and Atmospheric Research

OLS Operational Linescan System

ONR Office of Naval Research

ORA Office of Research Applications

PIPS Polar Ice Prediction System

RGPS RADARSAT Geophysical Processor System

SAR Synthetic Aperture Radar

SHEBA Surface Heat and Energy Balance of the Arctic (campaign)

SSC Stennis Space Center

SSM/I Special Sensor Microwave Imager

USARC United States Arctic Research Commission

USCG United States Coast Guard

> UW University of Washington

**Appendix A. List of anticipated operational sensors**

  ----------- ------------ ----------- ------------- --------- --------- ----------------
  Satellite   Sensor       Frequency   Spatial Res'n Country   Launch    Anticipated
                           band        meters                  date      operational
                                                                         status

  TIROS       AVHRR        VIS/IR      1100 - 4000   USA       past      operational

  DMSP        SSM/I SSMIS  MW MW       12,500 -      USA       past,     operational
                                       25,000                  1999 2001 
                                       12,500 -                          
                                       25,000?                           

  DMSP        OLS          VIS/IR      550 - 2700    USA       past      operational

  NPOESS      follow-on to                           USA       2008      operational
              DMSP and                                                   
              NOAA                                                       
              satellites                                                 

  GOES-N      VAS          VIS/IR      4000          USA       past?     operational^1^

  RADARSAT    SAR          MW          10 - 150      CAN       past,     operational
                                                               2000      

  EOS         MODIS        VIS/IR      250 - 1000    USA       2000      operational^2^

  ENVISAT     ASAR         MW          30 - 1000     ESA       1999      back-up

  EOS         AMSR         MW          11,600        USA       2000      back-up

  QuikSCAT    SCAT         MW          25,000        USA       1998      experimental

  ENVISAT     AATSR        VIS/IR      1000          ESA       1999      experimental

  ENVISAT     MERIS        VIS/IR      300 - 1200    ESA       1999      experimental
  ----------- ------------ ----------- ------------- --------- --------- ----------------

^1^ Great Lakes only; ^2^ subject to good quality ice algorithm

Anticipated sources of satellite data, 1997-2002. Operational status is
defined as follows. *Operational* means that the data will be used as a
primary source of data for operations with processing systems and
communications established for accessing and analyzing the data.
*Back-up* means that the data are candidates as a source of data should
a primary data stream become unavailable. In addition, if these data
sources are demonstrated to be available on an operational basis, it is
possible that they could augment the existing operational sensors.
*Experimental* means that the data are unlikely to be used operationally
during the next 5 years, but that there is interest in ascertaining
their potential for ice monitoring.