for Sea Ice Research

Report of the

Science Working Group

December 1984
Front cover photograph by F. Carsey: Surface-based microwave emissivity measurements in the eastern Beaufort Sea, July 1982.

Back cover photograph by A. Hielscher: The research vessel Polar Bjorn in the marginal ice zone of the Greenland Sea, July 1983.

This report was produced by the University of Washington Applied Physics Laboratory and Department of Printing. Copies

are available at no cost from

Oceanic Processes Branch. Code EE

NASA Headquarters

Washington, DC 20546

or from

Applied Physics Laboratory

University of Washington

Seattle, WA 98105
Passive Microwave Remote Sensing

for Sea Ice Research

Report of the NASA Science Working Group

for the Special Sensor Microwave Imager (SSM/I)

December 1984
Members of the NASA Science Working Group

f or the Spe_cial_Sensor_Microwav_e.Imager_ _ _ . .___. .

Norbert Untersteiner, Chairman

Polar Science Center, Applied Physics Laboratory, University of

Washington

Roger Barry

CIRES, University of Colorado

Frank Carsey

Jet Propulsion Laboratory, California Institute of Technology

Donald Cavalieri

NASA Goddard Space Flight Center

James Cornelius

Fleet Numerical Oceanography Center

Fred Deily

Exxon Production Research Company

Douglas Eckert

Oceanographic Services, Inc.

Thomas Grenfell

Department of Atmospheric Sciences, University of Washington

Ray Godin

Naval Polar Oceanographic Center

James HoUinger

Naval Research Laboratory

David Honhart

Office of the Oceanographer of the Navy

Paul McClain

NOAA-NESS

Rene Ramseier

Atmospheric Environment Service Canada

Alan Thorndike

Physics Department, University of Puget Sound

Patrick Welsh

NORDA

Thomas Wilheit

NASA Goddard Space Flight Center

Jay Zwally

NASA Goddard Space Flight Center

Additional help in preparing this report was provided by Claire Parkinson and

Josefino Comiso, both at NASA Goddard Space Flight Center, and Peter Wadhams of Scott Polar Research Institute.

Preceding Page Blank

-in-
Foreword

During the next decade, satellites carrying a range of sensors in nearpolar orbit will provide a unique opportunity for polar research. Our experience with Seasat and Nimbus satellites indicates that many important ice

parameters can be measured using microwave radiometers to acquire global,

synoptic data at coarse spatial resolution, synthetic aperture radars (SAR's)

to spotlight local areas where high spatial resolution is needed, and radar

altimeters to measure ice-sheet topography. To ensure that these data

sources are fully exploited for polar research, NASA has formed three Science Working Groups (SWG's): a "DMSP-SSM/I Sea Ice SWG" to cover passivemicrowave data, an SWG to evaluate science applications of ERS-1 SAR data in

the Alaska region, and an "Ice Sheet SWG" to assess the research potential of

satellite data over the ice sheets of Greenland and Antarctica.

The report of the ERS-1 group was published in December 1983 by the Jet

Propulsion Laboratory under the title "Science Program for an Imaging

Radar Receiving Station in Alaska." The report of the Ice Sheet SWG will be

published later in 1985. The document presented here is the report of the

DMSP-SSM/I Sea Ice SWG. Together, the three reports will be used to guide

NASA's research program in the polar regions, and we hope they will lead the

way to cooperative programs both with other U.S. agencies and with other

countries.

The DMSP-SSM/I Sea Ice SWG was established in December 1982 under

the chairmanship of Norbert Untersteiner. It was charged with answering

these questions:

• What problems of sea ice research could most appropriately be

addressed utilizing DMSP-SSM/I data?

• What are the associated DMSP-SSM/I data requirements to help solve

these problems?

In addition to fulfilling this charge, the report provides the reader with background information necessary to understand the rationale for sea ice

research and to appreciate how dependent such research is on remote sensing. Moreover, the SWG activities have focused attention on the need for

cooperative research aimed at improving and validating algorithms for converting satellite passive microwave data to sea ice parameters. As a consequence, this research is progressing well: analytical tools have been

developed for application, to SSM/I data once they become available, and

-iv-
efforts are under way to develop new techniques for validating the remotely

sensed data-. Finally," the SWG'has provided clear "guidance to NASA toward

development of an SSM/I data processing system to meet sea ice research

requirements.

Robert H. Thomas

Manager, Polar Oceans Program

. ~ National Aeronautics and Space Administration

-v-
Abstract

This report summarizes how data gathered by. remote sensors on satellites can be utilized for sea ice research, and describes how the brightness

temperatures measured by a passive microwave imager can be converted to

maps of total sea ice concentration, and to the areal fractions covered by

first year and multiyear ice. Several ancillary observations, especially by

means of automatic data buoys and submarines equipped with upwardlooking sonars, are needed to improve the validation and interpretation of

satellite data. The design and performance characteristics of the Navy's Special Sensor Microwave Imager, expected to be in orbit in late 1985, are

described. It is recommended that data from that instrument be processed

to a form suitable for research applications and archived in a readily accessible form. The report concludes with a description of the sea ice data products required for research purposes and recommendations for their

archival and distribution to the scientific community.

-vi-
Contents

EXECUTIVE SUMMARY xi

1. INTRODUCTION 1

1.1 Sea Ice '. 2

1.2 Sea Ice Research 4

1.3 Observations 7

2. SATELLITE REMOTE SENSING OF SEA ICE 9

2.1 Visible and Infrared Sensors 9

2.2 Microwave Measurements 10

3. METHODS OF PASSIVE MICROWAVE REMOTE SENSING 18

3.1 Background ....: , 18

3.2 Derivation of Sea Ice Characteristics 20

3.2.1 Single frequency methods 21

3.2.2 Multifrequency methods 23

3.3 Observations in the Marginal Ice Zone (MIZ) 27

3.4 Multispectral Cluster Analysis 28

3.5 Validation 32

4. FUTURE RESEARCH 34

4.1 Algorithm Improvement and Validation 34

4.2 Ancillary Measurements 35

4.2.1 Data buoys 35

4.2.2 Submarine data 37

4.2.3 Other satellite measurements 39

5. THE SPECIAL SENSOR MICROWAVE IMAGER (SSM/I) 40

5.1 Description of the Instrument 40

5.2 Dataproducts 44

5.3 Radiometer Performance Checks 45

5.4 Earth Location 46

6. DATA PRODUCTS FOR RESEARCH 47

6.1 Data Archives and Access 47

6.2 Algorithm Selection 49

7. CONCLUSION 50

8. REFERENCES 52

-vii-
List of Figures

Figure 1. Ice and water exchange between the Arctic and Atlantic

oceans viewed as an estuary exchange between two rotating tanks filled with water of different salinity 5

Figure 2a. Ice drift velocity in the Arctic Basin, averaged and interpolated over all available data since the drift of Nansen's

Fram (1893-1896), including data from the Arctic Data

Buoy Program initiated in 1979 '. 6

Figure 2b. Given the mean velocity field shown in Figure 2a, it is possible to calculate the "life expectancy" of a piece of ice in

the Arctic Basin 6

Figure 3. Nimbus-7 SMMR brightness temperature images of the

north polar region as observed during February 3-7, 1979,

at 18 GHz (1.7 cm) and 37 GHz (0.81 cm) for both horizontal and vertical polarizations 11

Figure 4. Polar images of arctic sea ice concentration (a) and multiyear ice fraction (b) derived from Nimbus-7 SMMR data

for the period February 3-7, 1979 12

Figure 5. Southern Ocean ice concentration in percent derived

from the single-frequency radiometer on Nimbus-5 13

Figure 6. Formation of the Weddell polynya; satellite observations

and results of a computer model driven by mean climate

data 14

Figure 7. A 10-year time series of antarctic sea ice extent 15

Figure 8. Nimbus-7 SMMR horizontally and vertically polarized

brightness temperatures at the 0.81 and 1.7 cm

wavelengths over the annual cycle 1978/79 19

Figure 9. Arctic ice concentration at summer's end averaged over

1973-1976 22

Figure 10. The inferred cloudy sky surface albedo for the period of

maximum meltpond coverage (mid-July) in 1974 23

Figure 11. Microwave brightness temperatures of sea ice and icefree (open) ocean as measured by each of the 10 Nimbus7 SMMR channels in three regions of the Arctic during

February 3-7, 1979 24

Figure 12. Polar images of (a) the 1.7cm wavelength polarization

(PR) and (b) the spectral gradient ratio (CR), both computed from Nimbus-7 SMMR data for February 3-7, 1979 26

-via-
Figure 13a. Plot of the difference versus the sum of the horizontally

.___. _ . . and vertically polarized -Nimbus-7 SMMRJbrightness-tem-—

peratures at the 0.81 cm wavelength for the entire Bering

Sea region 28

Figure 13b. Bering Sea ice concentration contours derived from the

0.81 cm wavelength channels of the Nimbus-7 SMMR data

for February 19, 1983 29

Figure 14. Regional variation of emissivity. Nimbus SMMR and

infrared radiometer data are used to generate a scatter

plot of emissivities at 37 GHz versus those at 18 GHz,

vertical polarization, from a rectangular area in the Bering Sea-Chukchi Sea-Arctic Ocean region 30

Figure 15. Scatter plot of emissivities at 37 GHz versus those at

18 GHz, vertical polarization, from a rectangular area in

the East Siberian and Laptev seas during three consecutive winter months 31

Figure 16. Maps of mean annual atmospheric surface pressure for

1979-1982, indicating a large interannual variability 36

Figure 17. The draft of sea ice along a 1400 km track in the Beaufort

Sea, obtained by upward-looking sonar from the nuclear

submarine Gurnard in April 1976 ..' 38

Figure 18. Coverage of the northern hemisphere during one-half day

by the DMSP SSM/I 41

Figure 19. The seven-channel SSM/I radiometer ready for placement

aboard a satellite 42

Figure 20. Scan geometry for SSM/I 43

Figure 21. Geographical areas in the northern and southern hemispheres to be covered by the PODS archive of SSM/I data,

which will include microwave brightness, ice concentration, and ice type 48

-ix-
List of Tables

Table 1. Seasonal variations of snow and sea ice in the northern and

southern hemispheres at the times of maximum extent, in

millions of square kilometers 1

Table 2. Ice types according to generally used terminology, their

thickness, and their classification in passive microwave

images 4

Table 3. Summary of spaceborne systems available for observing

sea ice parameters 17

Table 4. SSM/I performance characteristics. 43

Table 5. Processing level definitions 44

Table 6. Summary of data sets recommended for archival 45

-x-
Executive Summary

Sea ice is a fractured aggregate of so-called floes, ranging in size from

small fragments to plates measuring several kilometers in diameter. Winds

and ocean currents keep the ice in almost continuous motion, pushing it

apart and exposing the sea surface, or compressing it with great force,

cracking the ice and forming massive pressure ridges tens of meters thick.

This ice cover dominates the interaction between the atmosphere and the

sea in high latitudes, profoundly influencing the climate of these regions. In

summer, the melting ice flushes the sea surface with low salinity water,

creating conditions benign to many marine creatures. The potential destructiveness of the ice to the constructions of man severely limits maritime

activities and the development of needed natural resources. The exact

behavior of this complex and extensive oceanic cover is scientifically important but inadequately known. Direct observations are difficult because of the

darkness, remoteness, and natural hazards of that environment. However,

many sea ice characteristics are discernible by satellite-borne instruments,

and many others are potentially discernible with appropriate research;

another valuable contribution of satellites is relaying data from buoys

designed to drift with the ice and automatically report oceanic and meteorological conditions. A promising avenue of direct satellite research, surveillance of sea ice by measurement of emitted microwaves, has been established over the past decade. This document examines the benefits of such

.. • / • . - •-.

data and recommends a course for optimum scientific utilization of passive

microwave data obtained over the next decade from a Defense Meteorological

Satellite.

Sea ice processes occur on time scales ranging from those of the ocean

(months to centuries) to those of the atmosphere (hours to years). Thus,

observations of sea ice are logistically demanding, as some 10%-15% of the

World Ocean must be monitored every few days for decades to record its

behavior adequately. Although accurate and economical environmental

observations are a considerable challenge, some equally considerable

successes in this area have already been posted.

The first satellite to obtain global measurements of thermally emitted

microwaves was Nimbus-5. launched in 1972 and carrying a single frequency

radiometer (the Electrically Scanning Microwave Radiometer, or ESMR). This

satellite produced the first accurate, global, regular data on sea ice extent.

This data set also yielded information on ice compactness (concentration),

ice age, and surface condition. The Nimbus-5 radiometer was followed by

five-frequency, dual-polarization radiometers (the Scanning Multifrequency

-xi-
Microwave Radiometer, or SMMR) on Seasat and Nimbus-7. These sensors,

which essentially fill the interval to the present time, produced a more

powerful data set yielding more detailed information on ice concentration

and type. Research extending the interpretation of these data sets is ongoing and productive.

Future observations are important for two reasons: to increase the

period of continuous observations so it is long enough for comparison with

significant oceanographic and climatic events, and to'improve the information that can be derived from these observations for operational and

scientific applications. The next system with which such future observations

can be made is the Special Sensor Microwave Imager (SSM/I), which is

scheduled to fly for several years starting in late 1985 or early 1986 on satellites of the Defense Meteorological Satellite Program (DMSP). Although this

sensor was not designed specifically for scientific research on sea ice, its

complement of measured frequencies (19.3, 22.2, 37.0, and 85.5 GHz) will

include those most useful for sea ice research. NASA therefore established a

Science Working Group to make a detailed assessment of the benefits of the

SSM/I data set to science and the requirements imposed by scientific exploitation on the handling and storage of the data.

We of the Science Working Group vigorously recommend acquisition by

NASA of data gathered with the SSM/I, processing of the data to obtain sea

ice parameters, and archiving of the observations in an optimally accessible

form. Specifically we call for

• Archival of daily average brightness temperatures over the polar

regions, binned at a grid size of 25 km for all channels except 85.5 GHz,

which should be binned at a grid size of 12.5 km. These data, which

represent a large volume, are needed for developing new algorithms to

derive geophysical parameters from the satellite measurements. Distribution by mail on magnetic tape or optical disk is adequate for this

purpose.

'• Computation and archiving of sea ice extent, concentration, and ice

type derived using algorithms currently under development. These ice

parameters should be retained in orbit-swath format and as gridded or

mapped averages. Estimates of ice extent should be daily averages at

a spatial resolution of 12.5 km; other parameters should be averaged

over three days with a grid size of 50 km.

The Science Working Group further supports current activities of the Pilot

Ocean Data System (PODS) at the Jet Propulsion Laboratory aimed at

.-xii-
developing the software necessary to implement these recommendations.

Once this-software" system has been satisfactorily tested with SSM/I data

after the launch of the satellite and the validation period of the algorithms,

we recommend transfer of the data processing and archival function to the

National Snow and Ice Data Center (NSIDC) for routine operations.

In support of these activities, we recommend continued research aimed

at improvement and validation of sea ice algorithms. This will require collaboration among various civilian and military groups in the detailed analysis

of existing SMMR data, execution of focused in situ measurements on the ice,

and examination of information obtained from data buoys, airborne surveillance systems, and upward-looking sonars aboard submarines.

In addition, noting the major potential offered by satellite data from both

the SSM/I and synthetic aperture radars (SAR's), we strongly support

research using ice data from the Shuttle Imaging Radar (SIR) series, and

from the European Space Agency's Earth Remote Sensing Satellite (ERS-1).

Finally, noting the importance to all meteorological, oceanographic, and

remote sensing studies of the Arctic Buoy Program, which obtains data on

air, ice, and oceanic conditions across the entire Arctic, we strongly recommend that this program be maintained through the next decade and

strengthened where appropriate. This program will increase the value of

SSM/I data to the operational communities, to the sea ice geophysics and

climatology communities, and to designers of future earth-observing systems

in support of long-term interdisciplinary studies of the land, ocean, atmosphere, and cryosphere.

-xiii-
1. Introduction

Snow and sea ice are the most ephemeral features of the earth's surface.

Both undergo large fluctuations annually, and their extent changes over time

scales of decades and centuries. Even the large ice sheets of Antarctica and

Greenland which vary on time scales of many millennia change rapidly in

comparison with the geologic evolution of the earth's crust.

Polar sea ice covers millions of square kilometers of the ocean but has a

thickness of only a few meters (Table l). This implies that sea ice has a small

heat capacity and that small perturbations in the heat balance at the surface

produce large variations in ice extent. In the energetic (visible) part of the

solar spectrum the open ocean is nearly black and absorbs most of the

incoming radiation. In contrast, the ice cover is nearly white and reflects

most of the incoming radiation. Hence the presence or absence of sea ice

profoundly alters the heat balance of the atmosphere-ocean system. It has

also been demonstrated that snow and ice have a positive feedback effect on

the climate; an unusually large ice cover, for instance, will reduce the

amount of absorbed solar radiation, creating conditions that will induce a

further enlargement of the area covered by ice.

Table 1. Seasonal variations of snow and sea ice in the northern and southern

hemispheres at the times of maximum extent (from various sources,

Untersteiner, 1984), in millions of square kilometers.

Surface

Area

Snow on land

and

sea ice on

the ocean

Snow and

sea ice

areas

combined

Entire

Globe

510

Minimum

Maximum

Minimum

Maximum

Minimum

Maximum

Northern

Hemisphere

Land

100

2

49

Ocean

155

8

15

10

64

4%

25%

Southern

Hemisphere

Land :

49

14

14

Ocean

206

3

20

16.5

34

6.5%

13.5%

-1-
Sea ice is an integral component of the global climate system and

interacts with both the ocean and the atmosphere. Because of this, longrange climate forecasts will require the inclusion of sea ice within both the

climate models and the global, observational system. Sea ice also has an

immediate impact as an impediment to offshore resource development and

shipping - implying the need for routine, synoptic observations and shortterm forecasts of sea ice behavior. Ice advisories and short-term forecasts

are issued by the Navy and the National Oceanic and Atmospheric Administration (NOAA), and passive-microwave data are essential to the success of

this work.

Research into the dynamic relationships between sea ice and other elements of the climate system is particularly important as we become more

aware of the potential for climate change due to short-term effects such as

El Nino or more lasting effects such as the increase in atmospheric COz. In

this report we review the status of sea ice research and recommend specific

activities related to passive microwave remote sensing from space.

The most difficult aspect of devising a strategy for studying any component of the physical environment is choosing which processes to include or

ignore, and the amount of detail to resolve or neglect. Asking the right

overall question is generally easy, but asking it in the right way requires that

we already have a measure of insight into the nature of the problem. In geophysical and particularly climatological research, it is rarely possible to isolate a single physical process for study. More often, a geophysical measurement is the composite result of many processes acting simultaneously.

Therefore, some a priori understanding of the system, derived from past

observations and study of various individual phenomena, is required.

Because of past research on sea ice processes, including analysis of satellite

observations, we have made significant progress toward such understanding.

Data from spacecraft have played an essential part in this process, and they

will be even more important to future progress. Consequently, we strongly

support the acquisition of data from upcoming earth-viewing satellites that

will overfly the polar regions. Tn particular, we expect passive-microwave

data to continue to play a central role in sea ice research.

1.1 Sea Ice

The formation of sea ice begins with the appearance of individual frazil

ice particles on the ocean surface. The frazil particles then aggregate and

form a slushy layer called shuga. Further cooling of this initial skim of ice

transforms the slushy surface into a more consolidated layer called nilas.

-2-
During the initial stages of freezing and later when a solid layer of ice is

advancing downward into the water, some brine is included in the ice.

Because the crystallographic structure of ice does not allow the inclusion of

non-ice molecules, the brine in sea ice is contained in macroscopic pockets

(of millimeter size). In the freezing process, much of the salt dissolved in sea

water is rejected, making the bulk salinity of sea ice generally smaller than

that of sea water. Rate of freezing and ice salinity are related such that the

fastest growing ice has the highest salinity (and vice^versa).

A special phenomenon occurs in the top few. centimeters of the ice.

Here, progressive cooling causes an upward expulsion of brine which results

in a skin of extremely high salinity typically found on newly frozen ice.

The processes by which salts are incorporated in the forming ice,

migrate within the ice layer, and, in time, are to a'large part returned to the

ocean (Untersteiner, 1968; Cox and Weeks, 1974) are complex and extremely

important in determining the microwave radiative properties of the ice.

First year ice that survives the summer becomes second year ice. The

physical properties of that ice make it distinguishable from older ice only

under exceptional circumstances. Therefore, all ice remaining at the end of

a melt season is termed multiyear ice. When snow on first year ice begins to

melt during the early part of summer, the patchy distribution of melt water

with, its lowered albedo causes locally preferential melting. This process continues until autumn freeze-up, and results in an undular relief with some of

the surface covered by refrozen meltponds. Each succeeding melt season

enhances the relief on the ice surface (hummocks become higher and meltponds fewer) until it reaches equilibrium after a number of years.

As the surface changes from relatively flat for first year ice to hummocks

about 1 m high for well developed multiyear ice, the properties of the nearsurface layer also change. The approximate volume of brine in the top few

centimeters of the ice is about 12% for young ice and diminishes as the ice

grows in age and thickness. Although the seasonal snow cover that falls on

the ice is originally salt free, it may pick up significant amounts of salt as it

is pushed around by the wind.

This age progression is summarized in Table 2. Each change influences

microwave radiation emanating from the ice surface. Consequently, in principle, different types of ice can be distinguished by measuring their

microwave emissions. In practice, microwave classification is restricted to

the four ice types shown in Table 2, and generally only first year and multiyear ice are distinguished.

-3-
Table 2. Ice types according to generally used terminology, their thickness, and

their classification in passive microwave images. The freezing season

lasts from early autumn to late spring. Depending on the geographical

region, 2 m of ice (in some regions even more) can form during that

time. Therefore, the thickness of first year ice covers a wide range,

depending on the date -when a particular surface element began to freeze.

TERMINOLOGY

New Ice

Young Ice

First Year Ice

Multiyear Ice

Frazil ice (loosely

floating needles

and platelets)-*- nilas

(first stage of thin

but consolidated ice)

Depending on date

when freezing started

Multiyear

THICKNESS

2 - 10 cm

10 - 30 cm

20 - 200 cm

generally > 2 m

PASSIVE

MICROWAVE

CLASSIFICATION

New ice

I

I j

First year ice

Multiyear ice

1.2 Sea Ice Research

The most fundamental description of the sea ice cover is its mass balance. The mean annual cycle is a result of ice formation (sources), horizontal flux (drift), and melting (sinks). This cycle implicitly contains all the

external and internal dynamic and thermodynamic processes affecting sea

ice. These processes also affect the underlying sea and the overlying air, and

they represent the interactions of sea ice with the climate system. The basic

role of sea ice is to restrict heat and mass exchange between the ocean and

the atmosphere; ice also modifies forcing of the sea by the wind, and ice

forming or melting alters the local brine content of the water column, changing stratification and mixing of the ocean.

In some regions of the World Ocean, sea ice formation is connected to

deep convection and the formation of characteristic water masses. It has

recently been shown that 75% of the water masses of the World Ocean acquire

their specific properties in an area measuring only 5% of the surface of the

-4-
World Ocean, and that this small region is located at polar latitudes (Sarmiento and Tottweiler, 1984). The implications of this fact are at present not

understood in detail, but the conclusion is inevitable that, on time scales of

decades and longer, sea ice, deep circulation of the oceans, and global climate must be intimately related.

For the sake of illustration, an extremely simplified sketch of air-sea-ice

interaction in the Arctic (Hunkins, 1983) is shown in Figure 1. Ice transport

in the Arctic Basin, as shown in Figures 2a and 2b, occurs along the main

drift features of the Beaufort Sea Gyre and the Transpolar Drift Stream

flowing from the East Siberian Sea toward Fram Strait. The main ice sink

(region of ice loss) is the entire width of Fram Strait between Greenland and

Spitzbergen. The cold East Greenland Current to the west carries ice far

southward to the southern tip of Greenland and beyond, while the warm "West

Spitzbergen Current to the east melts the ice rapidly, and it engulfs the Svalbard Archipelago only during winter.

Earths

Rotation

WSC = West Spitsbergen Current

EGC = East Greenland Current

Figure 1. Ice and water exchange between the Arctic and Atlantic oceans viewed

.as an estuary exchange between two rotating tanks filled with water

of different salinity (higher in the Greenland Sea and lower in the Arctic Ocean). Ice is driven by southward winds across the entire width

of Fram Strait, the narrow passage connecting the two basins. On the

east side, the ice is slowed by an opposing current and melted by its

high temperature (generally a few degrees above freezing). On the

•west side, the ice drifts with the cold East Greenland Current, and

melting begins only at latitudes far south of Fram Strait. It should be

noted that this picture is highly schematic, 'pertains only to the upper

few hundred meters of the ocean, and ignores the water exchange

through the Barents Sea, the Canadian Archipelago, and Bering Strait

(all of which are much smaller than the exchange through Fram

Strait), [from Hunkins. 1983] ..

-5-
/

 7 ' -- x

Figure 2a. Ice drift velocity in the Arctic Basin, averaged and interpolated over

all available data since the drift of Nansen's Fram (1893-1896),

including data from the Arctic Data Buoy Program initiated in 1979.

The total number of point-displacement years used in constructing

this figure is approximately 100. [from Colony and ThorndUce, 1984]

Figure 2b. Given the mean velocity field shown in Figure 2a. it is possible to calculate the "life expectancy" of a piece of ice in the Arctic Basin. The

contours shown here depict the average number of years that it will

take for a piece of ice to either melt along the margins of the Arctic

Basin or exit through Fram Strait. Such probabilistic data will be

extremely useful in conjunction with analyses of passive microwave

monitoring of ice types and ice extent.

-6-
Research during the International Geophysical Year (1957-58) and the

Arctic Ice Dynamics Joint-Experiment (1972-76) (Pritchard, Ed., 1980)

together with data from satellites and the Arctic Data Buoy Program have

advanced our ability to model the behavior of large fields of sea ice. Further

advance -will require linking these models to those of the atmosphere and

ocean, and frequent, synoptic observations of sea ice conditions in both polar

regions. This is, in essence, the view expressed in the Scientific Plan of the

WMO-ICSU World Climate Research Programme (WMO-ICSU, 1981) and its

predecessor, the Global Atmospheric Research Programme (GARP, 1975,

1979).

1.3 Observations

To understand sea ice behavior and its interaction with the ocean and

atmosphere the following measurements are needed:

a. Global measurements at daily intervals. Because sea ice responds

rapidly to atmospheric forcing, measurements at a coarse spatial resolution (several kilometers) are adequate. Measured characteristics

should include

• Sea ice extent - defined by the boundary between sea ice and ice

free ocean.

• Sea ice concentration — the percentage of a specified ocean area

that is covered by sea ice.

• Sea ice thickness

• Sea ice type - the percentage of different types of sea ice within a

specified area.

» Surface conditions — snow free or snow covered; thickness of snow

cover; liquid water content of the snow cover; extent of surface

ponding of melt water; surface albedo; surface roughness.

• Surface temperature — and the temperature of overlying atmosphere.

• Surface winds - speed and direction.

• Sea ice motion - to describe overall motion of the ice pack.

• Ocean conditions beneath the ice — currents, salinity, and temperature.

• Cloud cover.

-7-
Many of these measurements can be obtained from satellite passive

microwave data.

b. Localized measurements at high spatial resolution in areas of particular interest, such as the marginal ice zone:

• Sea ice motion - detailed measurements of the velocity field of an

array of individual ice floes.

• Ice floe sizes - and the location of leads of open water and new, thin

ice.

• Intensity of ridging — where the ice has been locally compressed to

form ridges above the surface and keels that can extend tens of

meters below the surface.

• Icebergs and thick floes of multi-year ice - location, size, and

motion.

These measurements also can be made from space using a synthetic aperture radar, which provides all-weather, day/night imagery of sea ice with a

spatial resolution of tens of meters (Fu and Holt, 1982; NASA-JPL. 1983).

Thus, most required sea-ice measurements can be made from space. However, there is a continuing need for in situ measurements: first, to validate

the space measurements and improve our ability to interpret them and,

second, to obtain measurements, particularly those of ocean properties

below the ice, that cannot be obtained from space. We shall discuss these

ancillary measurements further in Section 4.2.

In this report, we are concerned mainly with specifying the requirements

for data obtained during SSM/I missions aboard DMSP spacecraft. First, we

shall provide a general review of sea ice remote sensing, with a more detailed

description of passive microwave measurements.

-8-
2. Satellite Remote Sensing of Sea Ice

Until the advent of satellite observations, little quantitative information

existed on seasonal and interannual changes in sea ice cover. Remote sensing of sea ice depends on the contrasting appearance of ice and open water

over most of the electromagnetic spectrum. The unique value of satellite

observations is their ability to provide global coverage of the ice pack at

intervals as short as a single day. Satellite data from visible, infrared, and

microwave sensors provide the most reliable and regionally uniform information available on the global distribution of sea ice and its seasonal variations.

Satellite data on global ice extent now cover more than 10 years, with longer

records for individual regions. These data provide realistic ice boundary conditions for atmospheric and oceanic models, and they are used operationally

for sea ice forecasting.

2.1 Visible and Infrared Sensors

Among the satellite data sets that have proved valuable for sea ice studies are the visible and infrared observations from the Landsat series, NOAA

series, and Tiros-N. These images provide fine spatial resolution, but they

are obscured by clouds and, in the visible band, unattainable during hours of

darkness. Visible imagery with a spatial resolution of 80 m from the Landsat

missions has been available since the launch in July 1972 of Landsat 1. Subsequent Landsat missions had improved resolution (approximately 30 m).

Individual large ice floes can be identified readily on Landsat images, allowing

calculation of their size and shape and estimation of ice concentrations. By

identifying individual floes on sequential images, ice floe velocities can be calculated also, but the Landsat repeat interval is much longer than optimal.

Also, thin ice is indistinguishable from open water, contributing a significant

error, and the Landsat orbit covers only latitudes equatorward of 81°, excluding much of the Central Arctic.

Since the launch of NOAA-2 in October 1972, visible and infrared images

have been available from NOAA weather satellites flying in near-polar orbits.

NOAA-2 and the three subsequent NOAA-series satellites carried two-channel

Very High Resolution Radiometers (VHRR's), which were succeeded by fouror five-channel Advanced Very High Resolution Radiometers (AVHRR's)

aboard NOAA-6 to NOAA-8 and Tiros-N. AVHRR images have a resolution of

approximately 1.1 km, but their usefulness is limited by the obscuring effect

of clouds.
2.12 Microwave Measurements

In contrast to visible and infrared data, microwave imagery allows nighttime as well as daytime coverage and is not obscured by clouds; however, it

generally has a much coarser resolution. Launch in December 1972 of the

Nimbus-5 satellite with the Electrically Scanning Microwave Radiometer

(ESMR) introduced global microwave coverage with a spatial resolution of

approximately 30 km. The relatively coarse resolution of the sensor allows

the creation of maps of the entire polar regions from the microwave data

(Figures 3-5) in a way that would be impractical using visible or infrared

instruments with their finer resolution.

The value of microwave observations for sea ice studies is due to the

sharp contrast between the microwave emissivities of sea ice and open water.

At the 1.55 cm wavelength (19.35 GHz) of the single channel Nimbus-5 ESMR

instrument, sea ice has emissivities ranging from 0.84 to 0.95, roughly twice

the emissivity of approximately 0.44 for open water. Consequently, if there is

a mixture of sea ice and open water within a given 30 km resolution cell, the

composite emissivity for that resolution cell lies between these limits. This

allows the brightness temperatures recorded by the microwave instruments

to be converted to approximate sea ice concentrations. Estimated accuracy

is approximately ±15% in regions with predominantly first year ice (Zwally et

al., 1983a; Figure 5). Accuracy is less in regions with a mixture of ice types,

since brine drainage through the ice during the summer melt results in a

generally lower microwave emissivity for multiyear ice than for first year ice.

Multichannel microwave instruments have the potential not only of

increasing the accuracy of ice concentration calculations but also of allowing

determination of such additional variables as ice temperature, snow cover,

multiyear ice fraction, and surface melting. The Scanning Multichannel

Microwave Radiometer (SMMR) launched on the Nimbus-7 satellite in October

1978 has ten channels of microwave information. Data from four of the channels are presented in Figure 3. Algorithms to extract various sea ice parameters from the SMMR data have been developed and tested by groups in Norway, Canada, and the United States (Svendsen et al., 1983; Swift et al., 1984;

Cavalieri et al., 1984). Arctic Sea ice concentration and multiyear ice fraction derived from the SMMR microwave data are illustrated in Figure 4. A

major restriction of the Nimbus-7 SMMR data for studies of the Central Arctic

is that the satellite's orbit prevents data collection poleward of 84°, in contrast to the global data collection of the Nimbus-5 ESMR.

Even with the limited length of the data sets available, satellite remote

sensing has already made substantial contributions to our knowledge of sea

-10-
1.7 cm HORIZONTAL 1.7 cm VERTICAL

:<

0.81 cm HORIZONTAL 0.81 cm VERTICAL

:,

Figure 3. Nimbus-7 SMMR brightness temperature images of the north polar

region as observed during February 3-7. 1979, at 18 GHz (1. 7 cm) and

37 GHz (0.81 cm) for both horizontal and vertical polarizations. The

18 GHz data illustrate the sharp contrast, about 50 K, between sea ice

and water, whereas the 37 GHz data show a variation of about equal

magnitude across the pack ice itself. Comparison of the polarizations

shows a substantial decrease in the contrast between the vertical and

horizontal components for both frequencies over ice-covered regions

compared with the ice free oceans. These differences are used to

determine the areal concentration (compactness) of the ice cover and

the ice type, [from Cavalieri et al., 1984]

-11-
(a) /'

100%

96%

92%

88%

84%

80%

76%

72%

68%

64%

60%

56%

52%

48%

44%

40%

36%

32%

28%

24%

20%

16%

12%

8%

4%

0%

'

'

Figure 4. Polar images of Arctic Sea ice concentration (a.) and multi-year ice

fraction (b) derived from, Nimbus-7 SMMR data, for the period February 3-7, 7979. [after Cavalieri et al., 1984]

-12-
0° LONGITUDE

50° S. LATITUDE

FEBRUARY

w

y

100%

80%

64%

48%

32%

12%

1

OCTOBER

Figure 5. Southern Ocean ice concentration in percent derived from the singlefrequency radiometer on Nimbus-5 (Zwally et al., 1983a). The months

of minimum (February) and maximum (October) ice extent are shown

In each case data are averaged over the indicated month and then over

the years 1973-1976. The October results show a large area of low mean

concentration near the Creenurich Meridian. This feature is called the

Weddellpolynya; it was observed in 1974-1976 but not in 1973.

-13-
DECEMBER FEBRUARY APRIL

JUNE AUGUST OCTOBER

JANUARY 1,

INITIAL CONDITIONS APRIL 15 JUNE 15 JULY 1

RESULTS OF A COMPUTER MODEL DRIVEN BY MEAN CLIMATE DATA

Figure 6. Formation of the Weddell polynya; satellite observations and results of a

computer model driven by mean climate data. Monthly averaged

microwave brightness data from Zwally et al. (I983a) taken by

Nimbus~5 during the growth of the sea ice cover in the Weddell Sea

clearly show the formation of a large polynya, a body of open water surrounded by ice. Numerical simulations from Parkinson (1983) of the

same region also demonstrate a tendency toward polynya formation

The huge polynya was first observed by Nimbus-5 in 1974, and other

satellites in subsequent years. Its size and exact location vary from

year to year.

-14-
ice and its interannual variations. For instance, analysis of satellite data by

Zwally et al. (I983b) to determine the seasonal cycle of sea ice on the Southern Ocean for the ten-year period 1973-1982 has revealed large regional variability, a significant decrease in mean ice extent during the mid-1970's (earlier reported by Kukla and Gavin, 1981), and a rebounding of the ice cover in

subsequent years (Figure 7). Additional data and studies of this type are

needed for determining long-term trends and for understanding the causes

of observed interannual variability.

Figure 7.

1973 1975 1977 1979 1981 1983

A 10-year time series of antarctic sea ice extent (Zwally et al., 19B3b).

The total area containing sea ice vjas estimated from satellite

passive^nicrowave measurements. Superimposed on the annual

cycle, there was a significant decrease in sea ice extent during the

late 1970's. There has been some recovery in recent years, but not to

the high values of the early 70's. Extended observations are needed to

reveal the causes of these long-term changes in sea ice extent and

their relationship to climate.

Satellite data have also revealed such puzzling phenomena as the wintertime existence of the Weddell polynya, a large region of open water in the

midst of the Southern Ocean pack ice, visible in Figure 5 (Zwally and Gloersen. 1977; Gordon, 1978; Carsey, 1980). This has, in turn, prompted numerical modeling studies which have furthered our understanding of the polynya

and its oceanographic and atmospheric interactions (Martinson et al., 1981;

Hibler and Ackley, 1983; Parkinson, 1983). Figure 6 shows a comparison of

satellite observations and results from one of the modeling studies (Parkinson, 1983).

-15-
Active microwave imagery is obtained by spaceborne synthetic aperture

radar systems (SAR). This data source is also of value for sea ice studies, as

has been verified abundantly with the short but useful data set collected by

NASA's Seasat in June-October 1978. The Seasat SAR operated at a

wavelength of 23.5 cm (1.275 GHz) and produced images which, like the passive microwave images of the ESMR and SMMR, are not obscured by clouds

and have a resolution of 25 m, considerably finer than that of other spaceborne microwave sensors. SAR imagery can also reveal details of ice morphology, but the greatest utility of SAR data is in measuring small-scale

motion of the ice (Hall and Rothrock, 1981). First year and multiyear ice are

not distinguished easily at the long Seasat wavelength; in situ backscatter

data indicate that a radar carrier frequency higher than 10 GHz is required

for unambiguous determination of ice type.

Satellite data have also been used extensively in descriptive observational studies (e.g., Streten, 1973; McNutt, 1981; Cavalieri et al. 1983; Zwally,

et al., 1983a) and in studies of the relationship between the monthly and the

interannual variations of the sea ice cover and the atmospheric wind and

temperature fields and/or oceanic fields (e.g., Budd, 1975; Ackley and Keliher, 1976; Walsh and Johnson. 1979; Cavalieri and Parkinson, 1981; Gordon,

1981; Parkinson and Cavalieri, 1982). They are well recognized as the most

uniform and consistent data sets available for sea ice. However, the use of

these data sets for climate studies is limited severely by their brevity, and

their usefulness would be increased by information from the SSM/I program.

The methods available for remotely sensing sea ice from space, and their

suitability for specific purposes, are summarized in Table 3.

-16-
Table 3. Summary of spaceborne systems available for observing sea ice parameters.

Parameter Comments

Ice Extent Visual: High resolution, accurate; limited by clouds

and darkness. Partial remedy: minimum brightness

technique to eliminate clouds (but not darkness).

In the case of Landsat, poor geographic coverage.

Infrared: Good resolution and accuracy; limited

by clouds and large atmospheric effects.

Partial remedy: minimum brightness technique

as above, but problems with atmospheric effect

and low SST contrast during the warm season

remain.

Passive Microwave: Lower but useful spatial

resolution, no effect of darkness, only small

effects of atmospheric conditions.

Synthetic Aperture Radar: Very high resolution,

all weather and seasons; occasional problems

with open water/new ice discrimination; high

cost of data processing. Only limited data set

available for research from Seasat.

Ice Concentration Visual: Same as ice extent.

Infrared: Same as ice extent.

Passive Microwave: Similar to ice extent; some

unresolved questions about accuracy, especially

in summer.

Synthetic Aperture Radar: Same as ice extent,

except that small leads are not resolved and

their contribution to the total open-water is

not known.

Ice Type

(here limited to mean

the distinction

between first year

and multiyear ice)

Visual: No use.

Infrared: Possible use in relating ice thickness

to surface temperature, but rendered operationally

uncertain because of overwhelming effect of unknown

snow cover.

Passive Microwave: Very useful irt principle, but

existing algorithms give results at odds with other

data. Severe problem with obtaining independent

verification (ground truth).

Synthetic Aperture Radar: Research results to date

indicate that a SAR system with a higher carrier

frequency, C-band or X-band, would be effective.

Ice Thickness and

Thickness Distribution

No spaceborne system available.

-17-
3. Methods of Passive Microwave Remote Sensing

In this section we summarize methods currently in use and under

development for observing sea ice by means of its electromagnetic emissions

in the microwave spectral range.

3.1 Background

All matter radiates electromagnetic energy as a consequence of molecular interaction. The energy is emitted over a range of wavelengths, the precise mix being determined by the viewing angle, molecular composition, temperature, and material structure. Ice and water have distinct emission signatures, primarily because of differences in the way molecules are arranged

in each. Moreover, different ice samples can have distinct signatures, partly

because of temperature differences and partly because of differences in texture and impurity content. Snow density, grain size, surface roughness, brine

content, and the degree of wetness all influence the radiated energy, and

they influence it differently at different wavelengths. Thus, by sampling an

appropriate suite of wavelengths, the ice cover can be distinguished from

open water and classified according to its surface and near-surface characteristics.

Over sea ice, passive microwave data can be used to distinguish water

from ice and characterize the major ice types (Table 2): new ice, a few centimeters thick; first year ice, generally snow covered and up to 2 m thick;

and multiyear ice, which has survived at least one summer, has undergone

deformation and cracking, and is of variable thickness with a more or less

undulating surface. Multiyear ice is also distinctive in having a lower salinity

than younger ice, and this gives it a distinctive microwave signature. In principle, the relative concentrations of water and each of the major ice types

can be distinguished by passive microwave data at appropriate wavelengths.

Major problems occur, however, during the summer when the existence of

water on the ice surface significantly affects the microwave emissions (Figure 8). This problem is currently under investigation by several NASA- and

Navy-funded researchers (Comiso et al., 1984; Carsey, 1984), and in the Canadian Atmospheric Environment Service.

Over land, passive microwave data can potentially distinguish the extent,

water equivalent, and onset of snow melt for seasonal snow cover (Kunzi et

al., 1982), and the regions where summer melting occurs on the terrestrial

ice sheets of Greenland and Antarctica (Zwally and Gloersen, 1977). Some of

these applications are still research topics, but they promise to become

-18-
,0.8V

r+- • tf- .TO

(

£.^^-^^

-*-^T^T ..TV

1.7V

•0.8H

^

- 250

225

200

1 75

180

CHUKCHI SEA

\

CANADIAN BASIN

-~— — ^ *="•.!«

IN SITU TEMPERATURES

m

275K

250K

225K

1.7H 1.7V 0.8H 0.8V

CHUKCHI SEA MAXIMUM TB

CANADIAN BASIN MINIMUM TB

|OCT| NOV | DEC | JAN | FEB | MAR | APR | MAY | JUN | JUL | AUG | SEPT

1978 1979

Figure 8. Nimbus-? SMMR horizontally and vertically polarized brightness temperatures at the 0.81 and 1.7cm wavelengths over the annual cycle

1978/79. The maximum brightness temperatures observed in the

Chukchi Sea correspond to microwave emissions from first year ice,

whereas the minimum brightness temperatures observed in the Canadian Basin correspond to emissions from largely multiyear ice. The

distinction between first year ice and multiyear ice is maintained

throughout most of the year, but becomes impossible during the summer months because of surface melt conditions. In situ temperatures

obtained from arctic data buoys illustrate the seasonal change in temperature for each region, [from Cavalieri et al., 1984]

operational tools within the near future. In addition, it may be possible to

deduce snow accumulation rates over the polar ice sheets (Zwally, 1977; Rotman et al., 1982).

The spatial resolution of a microwave sensor increases as the wavelength

decreases or as the antenna size increases. Thus, high resolution can readily

be obtained in the visible and infrared bands, but this short wavelength radiation is strongly affected by atmospheric conditions, particularly clouds.

Microwave radiation lies in the frequency range of 1 - 300 GHz (wavelengths of

about 30 to O.lcm). It penetrates clouds and can provide all-weather,

-19-
day/night synoptic measurements of several parameters of the earth's surface and atmosphere (Njoku, 1982). The first satellite sensor to provide global information on ice extent was the Electrically Scanning Microwave

Radiometer, launched by NASA aboard Nimbus-5 in December 1972. It measured horizontally polarized radiation at 19.35 GHz with a spatial resolution of

about 30 km and had a useful swath width of 14-00 km. Because measurements were made at only one frequency, there is ambiguity in data interpretation. However, estimates of ice concentration can be made to an accuracy

of about ±15% in areas where the ice cover is more or less homogeneous.

The quality of ESMR data deteriorated after 1976, but they were still used

for operational ice forecasting by the Navy/NOAA Joint Ice Center until 1983.

In October 1978, the Scanning Multichannel Microwave Radiometer was

launched aboard Nimbus-7. It has provided excellent data since then and is

expected to continue operating into the late 80's. The SMMR acquires data in

both vertical and horizontal polarizations at five frequencies: 6.6, 10.7, 18,

21, and 37 GHz. Spatial resolution ranges from 30 to 150 km, depending on

frequency, and swath width is 780 km. Data from the 18 and 37 GHz channels

are used to obtain both the total concentration of sea ice and the fraction of

multiyear ice (Cavalieri et ai, 1984).

Starting in 1985 or early 1986, the U.S. Defense Meteorological Satellite

Program will launch a series of spacecraft that will carry the Special Sensor

Microwave/Imager, a scanning microwave radiometer that will collect data

from a 1300 km wide swath in both vertical and horizontal polarizations at

19.35, 37, and 85.5 GHz and at 22.235 GHz in vertical polarization only. Spatial resolution will be between 12 and 50 km, depending on frequency.

3.2 Derivation of Sea Ice Characteristics

Conversion of passive microwave data to sea ice characteristics relies on

the basic equation of microwave radiometry (Grody, 1976), which evaluates

brightness temperature (TB) for frequency i and polarization j

TBv=tiiTt+ti[2Tt-tii(TA + T.)]+(\-Cii')T,lli . (l)

where £y is the emissivity, ft is the atmospheric opacity, Ta is the mean

atmospheric temperature, T0 is the surface temperature, and T^ is the

brightness of outer space. If the atmosphere and the outer space terms are

neglected, a simplified expression is obtained:

Tmi = ^T0 . (2)

where the different values of e^, about 0.5 for open water and 0.8 to 0.95 for

the different ice types, produce the ice-water and ice type contrasts. In gen-

-20-
eral, the emissivity is that of an aggregate of surface materials; specifically,

for n ice types

...

where

\" f — 1 / A \ £<*-! (4)

and T0(k)=T0 unless a different thermometric temperature is assigned to

each ice type. The microwaves emitted at shorter wavelengths originate

nearer the surface and are consequently more affected by the thermometric

surface temperature.

3.2.1 Single frequency methods

Algorithms designed to extract ice concentration and ice type from

satellite passive microwave data have been developed for the Nimbus-5 ESMR

and the Nimbus-7 SMMR instruments. For ESMR data, the algorithm derives

ice concentration from observed radiance by linear interpolation between

the radiance of open water and that of fully consolidated sea ice (Gloersen et

al., 1974; Comiso and Zwally, 1982; Carsey, 1982). From Eq. 3, the ice concentration C1

/ is

„ _I

~ ~

where Tg is the observed brightness temperature, 7^ is the brightness temperature of the water, Tt

jj is the effective thermometric (or physical)

temperature of the ice, and E(/) is the emissivity of the ice. The dependence

of surface temperature on location and season is taken from climatological

data. Equation 5 implicitly assumes a unique value of ice emissivity. This is

approximately true if one ice type predominates, as in Antarctic waters,

where ESMR data have been used to derive a four-year time series of ice

cover (Zwally et al., 1983a). Where there are two or more ice types, errors in

estimated ice concentration can become large.

The single wavelength of the ESMR limits its ability to distinguish

between different ice types, but in certain seasons and locations the relative

amounts of first year and multiyear ice can be estimated if the total ice concentration is known (Gloersen et al., 1978; Carsey, 1982). As an example, the

amount of ice to survive the summer can be estimated by analyzing the

increase in TB when the mixture of multiyear ice and open water is approximately replaced by a mixture of multiyear ice and new ice (Carsey, 1982).

-21-
This technique was vised to compile Figure 9 showing the four-year mean of

multiyear ice concentration in the Arctic at the beginning of the freezing

season.

USSR

Figure 9. Arctic ice concentration at summer's end averaged over 1973-1976.

This result is obtained from Nimbus-5 data by interpreting the change

in microwave brightness when the water between multiyear ice floes

in August freezes to become the matrix of first year ice cementing the

pack together in winter. This plot shows the fraction of multiyear ice

at the onset of the cold season.

One channel of data can also provide an estimate of the fractional area

covered by freshwater meltponds during the early summer when the ice surface supports a relatively large percentage of such puddles. The area

covered by meltponds is needed to improve estimates of surface albedo

which, in summer, is largely controlled by the area of the darker puddles;

small changes in this albedo are significant both in ice balance models (Maykut and Untersteiner, 1971) and in climate models. July, the period of maximum meltpond coverage, is also the period of minimum TB, since the bright

exposed ice shares the surface with low-radiance meltponds and leads (Carsey, 1984). The area of exposed ice, An, is computed by

Tow Tsh

(6)

-22-
where Tow is the brightness of the lead, TBh the brightness of the exposed ice.

and Ta the contribution of the atmosphere to TB. The area occupied by leads

is derived by calculating the amount of ice to survive the melt season by the

method described above (Carsey, 1982) and correcting this value for an

assumed rate of lateral ablation during the melt season. Finally, the surface

albedo is calculated by attributing specific albedos to the ponds, leads, and

exposed ice. The many assumptions generate unknown errors, but a first

estimate of albedo can be derived. Figure 10 shows Arctic-wide surface

albedo for the day of locally maximum ponding (near July 14).

ALASKA

USSR

CANADA

Figure 10.

The inferred cloudy sky surface

albedo for the period of maximum meltpond coverage (midJuly) in 1974. This result is

obtained by observing directly

via. the microwave record the

amount of exposed ice.

Remotely sensed data on ice concentration and ice age (type)

along with surface udnds and ice

displaceTn,ent derived from data

buoys will provide the hey to

determining the overall ice balance mentioned in Section 1.

Efforts are under way to

increase the sophistication of the

buoys and provide additional

data for the calibration of

remote sensing instruments.

3.2.2 Afultifrequency methods

With the launch of the Seasat and Nimbus-7 SMMR's in 1978, ten channels

of dual-polarized, radiometric brightness temperatures at five wavelengths

became available for improving ice concentration determinations. The

microwave spectra of first year ice, multiyear ice, and ice free ocean shown

in Figure 11 were obtained with the Nimbus-7 SMMR, and demonstrate the

microwave characteristics of each sample over the range of SMMR

wavelengths. In the figure, brightness temperatures for each of the ten

SMMR channels are plotted for three regions in the Arctic. "A" corresponds

to a region of consolidated first year ice in Baffin Bay, "B" to a region of

largely multiyear ice in the Canadian Arctic, and "C" to a region of ice-free

water in the Norwegian Sea.

-23-
250

_ 200

^

_

::

:

LU

.

/

3

a

-

150

100

FIRST-YEAR ICE

MULTIYEAR ICE

OPEN OCEAN

1.0 2.0 3.0

WAVELENGTH (cm)

:

fV VERTICAL

HORIZONTAL

VERTICAL

HORIZONTAL

:

Figure 11. Microwave brightness temperatures ofseaice andice-free (open) ocean

as measured by each of the 10 Nimbus-7 SMMR channels in three

regions of the Arctic during February 3-7, 1979. "A" corresponds to a

region of consolidated first year ice in Baffin Bay, "B" to a region of

largely multiyear ice in the Canadian Arctic, and "C" to a region of icefree ocean in the Norwegian Sea Hatched bands indicate plus and

minus one standard deviation about the mean [from Cavalieri et al.,

1984]

The six spectra in Figure 11 illustrate two important characteristics.

The first is that the difference between the vertical and horizontal brightness

temperatures is consistently greater for open water than for ice types at all

wavelengths. The second is that the difference in brightness temperature

between the first year and multiyear ice increases with decreasing

wavelength, because this difference arises from scattering of the upwelling

microwaves by the air bubbles included in multiyear ice. It is these two

microwave characteristics that form the basis of a multichannel algorithm

developed by Cavalieri et ai (1984) for determining both the total ice concentration and the area covered by multiyear ice. The algorithm utilizes the

difference between the horizontally and vertically polarized brightness temperatures to calculate the ice concentration through the use of the polarization parameter, PR, defined for each wavelength X by

TBV(X)+TBH(X) '

-24-
where TBH and Tgy are the horizontally and vertically polarized brightness

temperatures. The polarization, PR, for the 1.7 cm (18 GHz) wavelength is

shown in Figure 12a for the entire Arctic. The sharp contrast in PR between

ice-free and ice-covered portions of the ocean is clear and demonstrates the

utility of PR for calculating sea ice concentration.

The spectral variation with ice type observed in Figure 1 1 forms the basis

for distinguishing between first year and multiyear sea ice in the algorithm.

The parameter incorporating these spectral variations at 0.81 cm (37 GHz)

and 1.7 cm (18 GHz) wavelengths is the spectral gradient ratio, GR, which is

defined in terms of the vertically polarized brightness temperatures TBV by

Over the open ocean GR is positive, over first year ice approximately zero,

and over multiyear ice negative. As is illustrated in Figure 12b, this parameter varies across the Arctic Basin, indicating the transition from first year ice

in the peripheral seas to multiyear ice in the Central Arctic.

The key advantage of using the brightness temperature ratios PR and GR

in determining ice concentration and type is that errors due to the unknown

thermometric temperature of the ice are greatly reduced. Because

microwave brightness temperatures are proportional to thermometric temperature, variations in ice thermometric temperature are a significant

source of error in the ice parameter calculations. By using the ratio technique, errors in the calculated ice concentration resulting from unknown

horizontal variations in ice temperature across the Arctic are reduced by an

order of magnitude compared with algorithms that model the thermometric

temperature from climatological data (Cavalieri et al., 1984). The ratio technique, then, reduces the need to acquire independently observed thermometric ice temperatures and to model the physical temperature of the

radiating layer of the ice. This radiance ratio technique was utilized to provide the areal percentage of ice cover or ice concentration and the fraction

of multiyear ice illustrated in Figure 4 for a five-day period in February 1979.

An alternate approach for utilizing the multichannel SMMR data has been

developed by Swift et al. (1984) for operational use in specific regions by the

Canadian Atmospheric Environment Service (AES). Essentially, this algorithm is based on a set of linear equations of the form of Eq. 1 in which

region-specific microwave emissivities and ice temperatures interpolated

from observations are used. The use of both active and passive microwave

data for ice type discrimination has also been examined in Canada. The

-25-
(a)

POLARIZATION

(b)

SPECTRAL

GRADIENT

RATIO

1

> 06

M

03

000

- 03

<-.06

Figure 12. Polar images of (a) the 1. 7 cm wavelength polarization (PR) and (b) the

spectral gradient ratio (GR), both computed from Nimbus-7 SMMR data for

February 3-7, 1979. The contrast in the polarization between ice-free ocean and

consolidated pack ice and the almost uniform polarization across the Arctic Basin

shown in (a) demonstrate the utility of using this parameter for calculating ice

concentrations. The variation in the spectral gradient ratio across the Arctic

Basin shown in (b) illustrates the utility of multispectral information for distinguishing between first year andmultiyearice.

-26-
results of Gray et al. (1982) show great promise in resolving ice type using

13.3 GHz radar and 19.4 GHz radiometry.

One algorithm designed for operational use with the SMM/I radiance data

employs the difference between the horizontal and vertical radiances at

37 GHz for computing ice concentration (Hollinger and Lo, 1983). This

approach is similar to the SMMR processing algorithm (Cavalieri et al., 1984)

in that it uses the polarization information to obtain ice concentration. Ice

type, first year or multiyear, is determined by first calculating a parameter P

derived from the vertically polarized 0.81 cm (37 GHz) wavelength SSM/I

channel and then comparing P to a predetermined value P0 as follows:

First year ice ifP>P0

Multiyear ice ifP<P0 .

This method does not allow for mixtures of ice types, but is essentially a first

year/multiyear discriminator. The coefficients used in this algorithm are

different for each season. At present, except for determining the position of

the ice edge, there are no plans for using the 85.5 GHz radiances to retrieve

sea ice parameters. Current studies and recent field experiments, however,

suggest that use of higher frequency radiances may improve discrimination

of ice concentration and ice type (Troy et al, 1981; Grenfell, 1984).

3.3 Observations in the Marginal Ice Zone (MIZ)

In marginal ice zones, conditions are sufficiently different to require special techniques for obtaining estimates of ice characteristics. An analysis of

the SMMR microwave polarization characteristics observed over the Arctic

Basin showed little difference in the horizontally and vertically polarized

brightness temperatures (Cavalieri et aL, 1984) - in contrast to microwave

signatures of sea ice obtained in the Fram Strait region during the Norwegian

Remote Sensing Experiment (NORSEX) which showed a significantly greater

variance in the horizontal radiances than in the vertical ones (Svendsen et

al., 1983). This anomalous behavior of the horizontal polarization is

explained by the presence of layers of solid ice in the dry snow covering the

sea ice floes in the MIZ. This is probably the result of alternate thawing and

freezing cycles which are encountered more frequently in MIZ's than in the

Central Arctic. Because of this behavior, the NORSEX group developed a sea

ice algorithm that utilized only the vertically polarized SMMR radiances

(Svendsen et al, 1983).

Recent analysis of SMMR observations made during the Bering Sea Marginal Ice Zone Experiment (MIZEX-West) shows that the microwave character of

-27-
the ice cover is also influenced by the presence of new ice, young ice, and

thin first year ice (MIZEX West Study Group, 1983). SMMR brightness temperatures at the O.B1 wavelength (37 GHz) observed over the entire Bering

Sea region are plotted in Figure 13a. Each point is graphed according to the

difference and sum of the vertical and horizontal polarizations. This

approach results in two distinct clusters: a fairly tight cluster in the upper

left corner corresponding to open ocean, and an elongated cluster in the

lower right corresponding to sea ice. Using aerial ice reconnaissance data,

Cavalieri (1984) found that the 0.81 cm (37 GHz) SMMR dual-polarized radiances could be used to distinguish among new, young, and first year ice and

derive ice concentrations, as indicated in Figure 13b. These results hold

promise for using polarization information at this wavelength for subdividing

different types of first year ice.

Figure 13a.

Plot of the difference versus the

sum of the horizontally and vertically polarized Nimbus-7 SMMR

brightness temperatures at the

0.81 cm wavelength for the

entire Bering Sea region Each

point corresponds approximately

to one 30 km SMMR field of view.

Of the two clusters, which

correspond to ice-free ocean and

sea ice, the ice cluster is

elongated because of the presence of new (N), young (Y). and

first year (F) sea ice types.

[Cavalieri, 1984]

70

60

I 50

i

~Z> 40

o 30

5

20

10

OCEAN

250 300 350 400

0.81 CM TB(V + H)

450 500

3.4 MuLtispectrcd Clxcster Analysis

There are two radiometrically distinct ice types: saline first year ice and

desalinated multiyear ice. Because of the difference in salinity (and therefore in the absorption coefficient), first year ice is substantially more opaque

than multiyear ice. Thus for first year ice the observed radiation emanates

from a thin layer of ice at the snow/ice interface, whereas multiyear ice radiates from farther below the surface. If effects of the snow cover or the surface, which will be discussed later, are neglected, the emissivity of first year

ice is expected to be high and frequency-independent. Multiyear ice, on the

other hand, contains inhomogeneities that suppress outgoing radiation

-28-
Figure 13b. Bering Sea ice concentration contours derived from the 0.81 cm

•wavelength channels of the Nimbus-7 SMMR data for February 19,

1983. Aerial ice reconnaissance observations made from the NASA

CV-990 aircraft coordinated vjith Nimbus-7 SMMR observations during

MIZEX-West demonstrate the discrimination of ice type urith the

0.81 cm wavelength polarization, [after MIZEX-West Study Group,

1983]

because of scattering. The shorter the wavelength, the more scattering will

occur, and the more the radiation from deeper layers will be suppressed.

To illustrate how this frequency dependence could be utilized in distinguishing various types (or surfaces) of ice, Figure 14 (left) shows a scatter

plot of observed emissivities from the Central Arctic, Chukchi Sea, and Bering Sea region at 37 and 18 GH2, in vertical polarization. Also shown (right)

are the geographic origin of the points in the scatter plot. Four clusters are

identifiable and have been labeled as multiyear ice, first year ice, new ice,

and ocean, because the data points originate from the Central Arctic (a

region of old floes), the Chukchi Sea (a region of first year ice), the Bering

-29-
1.0

0.9

> 0.8

N

I

0

CO

j 0.7

0.6

0.5

Emissivities (18 GHz vs 37 GHz, Vertical)

0.65

Ocean

j_ _L

0.75 0.85

E (37 GHz. V)

0.95

Figure 14. Regional variation of emissivity. Nimbus SMMR and infrared radiometer data are used to generate a scatter plot of emissivities at 37 GHz

•versus those at 18 CHz, vertical polarization, from a rectangular area

in the Bering Sea-Chukchi Sea-Arctic Ocean region. The clusters

(left) represent highly concentrated multiyear ice, first year ice, new

ice, and ice free ocean. The map (right) shows the geographic origin

of the cluster data points, [from Comiso, 1983]

Sea (where substantial new ice can be found), and the ice free ocean. The

distinct clusters for multiyear and first year ice illustrate the effect of

volume scattering discussed in the previous paragraph. The larger spread in

the emissivities at 37 GHz than at 18 GHz indicates the greater effect of

volume scattering at the shorter wavelength. The identification of the "new

ice" cluster also illustrates how the different penetration depths at different

frequencies can be utilized. Below a certain threshold thickness, the radiation observed at 37 GHz comes mainly from the ice, whereas that observed at

18 GHz comes partly from the ice and partly from the water underneath the

ice. Thus, the emissivity of "new ice" is the same as that of first year ice for

the 37 GHz channel but lower for the 18 GHz channel.

Although different ice types tend to cluster differently, some validation

is required to interpret the different clusters properly. For example, the

data points in the multiyear ice cluster could actually represent a mixture of

partly first year and multiyear ice and partly consolidated second year ice.

One procedure that could be used to remove the ambiguity is to analyze

smaller regions, one at a time. The scatter plots in Figure 15 for February,

-30-
FEBRUARY 1979 MARCH 1979 APRIL 1979

95

85

75

65

y .85

cc

LU

N"

5 .75

00

"w

65

85

.75

EAST SIBERIAN AND

LAPTEV SEAS

"

FY

MY SY

SY

FY

MY

SY

MY

EAST SIBERIAN SEA FY -- FY

FY

MY MY MY

LAPTEV SEA

FY

;:».*•

SY

SY

FY

65

65 .75 85 .65 .75 .85

t(37 GHz, VERTICAL)

.65 .75 85

Figure 15. Scatter plot of emissivities at 37 GHz versus those at 18 CHz, vertical

polarization, from a rectangular area in the East Siberian and Laptev

seas during three consecutive winter months. When the region is subdivided into two sections, the three clusters evident in the top plots

split into just two clusters, indicating a large representation of multi-year ice (MY) in the middle plots and possibly of second year ice (SY)

in the bottom plots.

March, and April 1979 show how effectively this can be done. In the top row,

the combined data points from the East Siberia and Laptev seas consistently

form three distinct clusters labeled MY (multiyear), SY (second year) and FY

(first year). When the East Siberian Sea and Laptev Sea data are analyzed

separately, only two clusters are evident in each case, as shown in the middle

and bottom plots. Whereas the SY cluster could not be unambiguously

identified in the combined plot, the separate Laptev Sea plot clearly indicates a cluster consistent with the signature measured for second year ice.

95

-31-
Using these cluster diagrams, we can classify each data point according

to ice type by assuming mixing ratios proportional to the distance of that

data point to the centroid of each cluster. For a two-cluster case, as in the

Laptev Sea and the East Siberian Sea (Figure 15), the proportional amount of

first year ice (or multiyear ice) can be derived in a straightforward manner.

A similar procedure can be implemented for three nonlinear clusters, such

as those shown in Figure 14. The presence of thick or very granular snow

cover complicates the problem because it causes an unusually large variability in the emissivity of each ice type, especially at frequencies of 37 GHz or

higher. However, a procedure developed by Comiso et al. (1984), which uses

two or more frequencies and allows the emissivities to vary, can be used to

overcome this problem. The same procedure is also useful in the marginal

ice zone, where corrections for atmospheric and other effects are needed to

retrieve ice parameters at higher frequencies.

3.5 Validation

A satellite is capable of observing vast regions in extremely short times;

the radiometer on Nimbus-5 provided a complete map of the area poleward

of 55°N or S in 12 hours. The data acquired by the satellite, however, must be

interpreted in terms of physical characteristics of the ice cover. Significant

progress has been made in achieving this capability. Several algorithms have

been compiled to estimate ice concentration and the relative amounts of

multiyear and first year ice. The results show broad agreement between

different algorithms but some significant differences from in situ data which

have not yet been fully explained. A major problem is the lack of "ground

truth" information. In situ measurements by observers on the ground are

sparse and can only be obtained by great physical effort (Koerner, 1973).

Even then, the area that can be covered remains small, and the identification

of the type of ice seen by the observer is prone to error.

Passive microwave techniques integrate information from hundreds of

square kilometers to provide a composite estimate of average ice conditions

within an individual picture element, or "pixel." Accurate validation requires

either a detailed knowledge of ice conditions within the pixel area or a

knowledge of the statistics of the ice conditions. Broad spatial coverage can

be obtained from aircraft, but here the interpretation problem is also

present: visual observations are subjective. Nevertheless, airborne measurements offer the greatest promise for testing and improving algorithms

used for interpreting satellite data. Aircraft sensors have a high spatial resolution, and individual ice floes can be identified. The ongoing series of MIZEX

-32-
field experiments permits the comparison of in situ measurements by investigators on the ice with simultaneous aircraft observations. This will provide

a reliable calibration of data from the airborne sensors. At the same time

data from the Nimbus-7 SMMR can be compared with the aircraft measurements.

Another powerful source of validation data is profiles of the ice made by

upward-looking sonar from submarines. These give accurate estimates of sea

ice draft and its variability. Such profiles cover only a narrow swath, but they

do give an estimate of ice type statistics and the amount of open water (Wadhams and Home, 1980).

At the time of writing this report, an intensive effort is under way to

compare results from different algorithms, and to compare all results with

available "ground truth" data, including submarine measurements. The purpose of this effort is to compile a "state of the art" algorithm for converting

SSM/I data to estimates of sea ice concentration and type (e.g., Hollinger et

aL., 1984). This algorithm is expected to be more accurate, particularly in

determining ice type, than the operational algorithm to be used by the Navy.

It is important to establish data processing procedures before SSM/I

becomes operational. However, we expect that experience with SSM/I data

and acquisition of additional ground truth data will yield improved algorithms. In the next section, we shall review our recommendation for a

research program designed to validate and improve SSM/I algorithms and to

provide ancillary information that cannot be obtained from SSM/I data.

-33-
4. Future Research

We shall consider here two areas of research: work needed to develop

and validate algorithms for conversion of passive microwave data to estimates of sea ice characteristics, and acquisition of needed data that cannot

be obtained by the SSM/I.

4.1 Algorithm Improvement and Validation

Sea ice is constantly changing, both in location and in physical characteristics. Consequently, observing sea ice and validating the observations are

difficult tasks. In this situation, it is important to establish a strategy that

minimizes the need for the most costly and time-consuming components of

the research — in situ measurements. The cluster analysis technique

described in Section 3.4 provides the key to our proposed strategy. This

technique makes use of the satellite measurements themselves to identify

microwave characteristics of the interrogated surface. Limiting values of ice

emissivity associated with the clusters can be derived from the data, and

geographical locations where these limiting values were measured can be

identified. Moreover, changes in brightness temperature clusters over time,

together with observed ice drift, can be used to distinguish between changes

in the emissivity itself and changes in the composition of the ice cover (ice

types).

Clearly, this approach cannot stand alone; there is still need for in situ

validation. However, it does serve to identify the locations where, and the

seasons when, field efforts will provide a maximum return. We recommend

that results from the MIZEX series and more localized work at Mould Bay on

Prince Patrick Island in the Beaufort Sea be used to supplement ongoing

cluster analysis research. Future field campaigns should be designed primarily to address problems identified by the cluster analysis technique.

Such campaigns are extremely costly. They should be planned carefully, and

full advantage should be taken of ships of opportunity. We anticipate that

this symbiotic approach will result in algorithms for analyzing SSM/I data in

terms of all major ice types and of the characteristics of melt pools and snow

cover on the ice. These advances will also improve our estimates of ice concentration.

In addition to such field programs, we see a continued need for largescale validation studies. Many of the necessary data will be acquired by

existing operations such as the NOAA and DMSP weather satellites, Canadian

ice reconnaissance flights, and submarine upward-looking sonar measurements. The Navy/NOAA Joint Ice Center routinely uses many of these data to

-34-
compile assessments of the sea ice cover. This provides an opportunity for

systematic comparison of these various data sets with those from SSM/I. We

strongly recommend close interaction between the sea ice research community and the Joint Ice Center in order to take full advantage of this opportunity. In addition, we support increased research application of upwardlooking submarine data.

Airborne measurements will continue to be an effective means of obtaining detailed validation data over large areas. In particular, we recommend a

series of aircraft validation under-flights after launch of the SSM/I. Planning

and timing of these flights should be directed by the prevailing status of sea

ice algorithms and by analysis of initial sets of SSM/I data.

4.2 Ancillary Measurements

Information that cannot be acquired from SSM/I data includes the following: ice thickness, winds near the ice surface, cloud cover, the state of

the ocean beneath the ice, and detailed characteristics of the sea ice such as

floe sizes, positions of leads, ridge intensity, and ice velocity. Most of these

parameters can be obtained from a combination of other satellite observations, submarine sonar measurements, and data buoys.

4.2.1 Data, buoys

An array of drifting buoys has been maintained in the Arctic since 1979

(Untersteiner and Thorndike, 1982). At any time, there are approximately 10

buoys giving twice daily measurements of air pressure, buoy temperature,

and location. These data provide fields of atmospheric surface pressure (Figure 16) for estimating surface wind fields, temperature, and ice motion.

a. Surface urinds. The wind is the primary mechanical force acting on

the ice. Measurements taken during the Arctic Ice Dynamics Joint

Experiment demonstrated that the surface wind can be estimated

accurately from observations of atmospheric pressure made at

unmanned data buoys. Surface winds estimated from pressure gradients by extrapolation from coastal stations alone are too inaccurate

for calculating ice dynamics.

b. Surface air temperature. At present, temperature is measured inside

the drifting buoys. This measurement integrates the effects of air

temperature, snow temperature, and radiation in a way that is difficult

to interpret. Improvements to the buoys are planned to give a better

-35-
0° ISO"

Figure 16. Maps of mean annual atmospheric surface pressure for 1979-1982,

indicating a large interannual variability. These maps uiere calculated

from data gathered during the the Arctic Data Buoy Program (Untersteiner and Thorndike, 1982). Started in 1979, this program produces

real-time, twice daily maps of surface pressure and ice drift velocity in

the Arctic Basin. About 10 drifting data buoys are in operation at any

given time. These data have become a crucial tool in driving and validating models of the dynamics of sea ice. Remotely sensed data on ice concentration and ice age (type) along with surface winds and ice displacement derived from data buoys will provide the hey to determining the

overall ice balance mentioned in Section 1. Efforts are under way to

increase the sophistication of the buoys and provide additional data for

the calibration of remote sensing instruments.

-36-
measurement of air temperature. This will be of direct value for

estimating the heat fluxes between the atmosphere and the ice and for

converting measurements of microwave brightness to estimates of surface emissivity.

c. Ice -motion. If enough buoys are present, the general field of motion

can be resolved (Figure 2). From this, the advection and convergence

terms in the equations for ice concentration can be derived. Measurements of ice motion are needed to check the output of models of ice

dynamics. They can serve also as the driving input to kinematic and

dynamic models describing how the ice properties respond to a given

velocity field.

We strongly support continuation of the Arctic Buoy Program. We also

recommend development of improved buoys capable of measuring temperature profiles within the sea ice and ocean properties beneath the sea ice.

These should be used in conjunction with existing types of buoys to provide

information on ice growth and melting, stratification of the ocean beneath

the ice, sensible heat flux in the oceanic boundary layer (WMO-ICSU, 1984),

and other parameters as buoy technology develops. Emplacement of such

buoys on antarctic sea ice should have a high priority, since we have negligible information on antarctic sea ice motion and thickness.

4.2.2 Submarine data

Upward-looking sonar measurements from submarines have proved to be

an invaluable source of data for sea ice thickness distributions (Wadhams,

1981). Although generally limited to a small number of transects across

selected regions of the Arctic, the submarine data provide an independent

source of information for comparison with passive microwave satellite data.

A number of submarine sonar profiles obtained during operation of the

Nimbus-5 ESMR (spring, 1976) and of the Nimbus-7 SMMR (spring, 1979;

spring and autumn, 1982) provide statistics on amounts of open water and

thin ice and on the relative abundance of first year and multiyear ice. Such

comparisons of submarine and satellite data will be an important part of the

validation effort.

Previous studies of submarine under-ice profiles have demonstrated that

there is no large region (30 km or more) that is composed entirely of multiyear ice. Wadhams (1981) found that the percentage of young ice (less than

1 m thick) in the Central Arctic is usually l%-5%. In addition, there is evidence in the profiles that a measurable quantity of first year ice exists

-37-
throughout the Arctic, mixed with multiyear ice. Figure 17, for instance,

shows the probability density function of "level ice" (ice with a local gradient

less than 0.025) calculated from a 1400 km long profile in the Beaufort Sea

observed in April 1976. The area under each of the peaks is proportional to

the relative abundance of undeformed ice in that thickness class. There are

two peaks for young ice representing two preferred drafts (0.3-0.4 m and

0.8-0.9 m), and the main peak is split into two components, at 2.1-2.2 m and

2.7-2.8 m. These are interpreted as the preferred drafts of undeformed first

year and multiyear ice. The relative areas show that the multiyear ice, while

a major component of the ice cover, comprises only about 60% of the whole.

These results suggest that the microwave radiances used to determine

multiyear ice should be interpreted with caution, and that submarine sonar

profiles should be analyzed carefully to validate both the multiyear ice fraction and the overall ice concentration calculated by the passive microwave

algorithms. The sonar profiles should also yield the maximum fraction of

multiyear ice, which should then improve the definition of the multiyear ice

radiances. Three submarine data sets (spring 1979, spring 1982, autumn

1982) coinciding with Nimbus-7 SMMR observations are currently being

analyzed for this purpose.

0.7

0.6

0.5

g 0.4

0.3

CD

O

CC

CL

0.2

O.I

ll f I IH-J- i i i

-5 5 10 15 20

DEPTH (m)

Figure 17.

The draft of sea ice along a

1400 km track in the Beaufort

Sea, obtained by upward-looking

sonar from the nuclear submarine Gurnard in April 1976. The

probability density distribution

shows that the most prevalent ice

by far is multiyear ice (see

Table 2) with a thickness of 3-

4m. A secondary peak in the

spectrum indicates the presence

of young and first year ice with a

thickness of about 1 m, [from

Wadhams and Home, 1980]

-38-
4.2.3 Other satellite measurements

Satellite measurements using sensors other than microwave radiometers

also provide important information over the polar regions. Landsat, NOAA,

and DMSP weather satellites obtain visible and infrared images which supplement passive microwave data. Reliable data on cloud cover are particularly

sparse over the Arctic Ocean (Barry et ai, 1984) as a result of problems with

discriminating clouds from snow and ice in visible and infrared satellite data.

Current sensors and the methods employed by the International Satellite

Cloud Climatology Project will not permit mapping of high-latitude cloud

amounts and levels. Resolution of this problem on a routine basis must probably await the operational 1.6 /^m channel sensor planned for a future NOAA

polar-orbiting satellite.

Infrared measurements can also be used to calculate ocean and ice surface temperatures. Synthetic aperture radar (SAR) provides images in all

weather at a spatial resolution of tens of meters. Comparison of sequential

SAR images of the same area gives motion vectors for individual ice floes.

Radar altimetry data have been used to detect the margin between ice and

ocean, but these data are collected only from directly beneath the satellite.

Altimetry data may also provide information on swell within the pack ice and

the roughness of the ice surface — a proxy indicator of ice type. Winds over

the ice cannot at present be measured from space, but winds seaward of the

ice margin can be derived from scatterometer data.

All of these sensors will fly on meteorological or oceanographic satellites

for at least part of the time during which the SSM/I will operate aboard DMSP

satellites. Important data will be collected, and we strongly recommend that

plans for processing and distributing SSM/I data include provisions for distribution of colocated data from these other sensors.

-39-
5. The Special Sensor Microwave Imager (SSM/I)

The SSM/I is a microwave radiometer that will be flown aboard a series of

satellites operated by the Defense Meteorological Satellite Program starting

in late 1985 or 1986. DMSP satellites fly in circular, sun synchronous, nearpolar orbits at an altitude of 833 km with a period of 101 minutes. The SSM/I

is similar to the SMMR deployed on Seasat and Nimbus-7, but with some

important differences. The SMMR operates only on alternate days because of

spacecraft power limitations, and completes near-global coverage approximately every six days. In one day the SSM/I will give highly redundant coverage of the polar regions (see Figure 18), as well as virtually complete coverage of the equatorial band. The SMMR operates at five frequencies (6.6, 10.69,

18.0, 21.0, and 37.0 GHz), each of which has a channel for both vertical and

horizontal polarization. The SSM/I will operate at four frequencies (19.35,

22.235, 37.0, and 85.5 GHz) with two polarizations at each frequency except

22 GHz, which will have only a vertical polarization channel.

5.1 Description of the Instrument

The SSM/I consists of an offset parabolic reflector, approximately

60x65 cm in size, and a seven-port horn antenna mounted on a rotating drum

that contains the radiometers, digital data subsystem, mechanical scanning

subsystem, and power subsystem. A small mirror, positioned to reflect the

cold sky and a "hot" reference of known temperature, provides cold and hot

calibration references during each scan. A prototype of the SSM/I in the

deployed position is shown in Figure 19.

SSM/I data are organized spatially by pixel, scene station, and scan. A

pixel is the smallest data element and is a radiometric sample of a single

footprint of a single frequency and polarization. A scene station is the center

of coaxially located pixels. A scan is one rotation of the drum during which

scene station, cold calibration, hot calibration, and housekeeping data are

taken.

With a velocity of 6.58 km/s the satellite advances approximately

12.5 km during each 1.9 s antenna rotation period. Over the active scan angle

of 102.4°, which covers a 1394 km wide swath on the surface, there are 128

scene stations. Alternate scans are labeled "A" and "B." Each A scan contains

64 samples of the five lower frequency channels at 1.6° intervals and 128

samples of the two 85.5 GHz channels at 0.8° intervals. Each B scan contains

only the 128 samples of the two 85.5 GHz channels. This method results in a

scene station spacing, in both directions, of about 25 km for the five lower

-40-
180

270

Figure 18. Coverage of the northern hemisphere during one-half day by the DMSP

SSM/L The hatched areas show swaths observed by the microwave

radiometer during a 12-hour period. In the subsequent 12 hours most of

the remainder of the surface, except for some small equatorial areas,

•would be observed. The small area surrounding the pole is not observed

by SSM/L

-41-
SCANNING

REFLECTOR

SUPPORT

SKY REFLECTOR

HOT LOAD

RIM OF HORN

ANTENNA

ELECTRONIC AND

MECHANICAL

SUBSYSTEMS

Figure 19. The seven-channel SSM/I radiometer ready for placement aboard a satellite. In this figure, up is also up on the spacecraft, i e., perpendicular to the

earth's surface. The key elements of SSM/I are the scanning reflector, the

calibration loads, the horn antenna, and the electronic and mechanical subsystems. The scanning reflector, the large disk-like object at top, and its

support frame-work are rotated This rotation causes the area "seen" on the

surface to be swept in a circular path urith the propagation lines forming a

large cone. In operation, microwaves from the surface are focused by the

reflector into a horn antenna whose rim is just visible near the image

center. As the scanning reflector is turned the small disk called the sky

reflector, which is facing to lower left in the figure, is brought before the

horn antenna (between it and the scanning reflector) once per revolution

and causes the antenna to "see" outer space instead of the earth's surface

and thus to obtain a cold or low constant radiance, calibration point.

Further along in the revolution, the antenna is exposed to a "hot load'

source of high known radiance for another calibration point. The hot load

is not visible because it is behind the sky reflector. Thus, an absolute calibration including the horn antenna is monitored during each scan.

-42-
frequency channels and 12.5 km for the two 85.5 GHz channels. Figure 20

shows the scan geometry.

An integration time of 7.95 ms is used for the lower frequencies whereas

3.89 ms is used for 85.5 GHz. The performance characteristics of the SSM/I

are shown in Table 4. Note that in each case the effective beamwidths provide substantial overlap between adjacent pixels.

DMSP

SATELLITE

6.58 km/sec

SATELLITE

VELOCITY

102° ACTIVE

SCAN ANGLE

GROUND

TRACK ^

1394 km

SWATH WIDTH

12.5 km

1.90 s

85 GHz

37 GHz

19 AND 22 GHz

Figure 20.

Scan geometry for SSM/I. The

rotating antenna system on

SSM/I sweeps the observed area

on the surface in two alternating

modes, one in -which all four frequencies are recorded and one in

which only the 85 CHz data are

recorded. The angle of emission

at the surface is a constant

across the swath. The use of a

single antenna for all four frequencies results in a different

surface resolution for each frequency, with the highest frequency, 85 GHz, having the

finest resolution, less than

15 km.

Table 4. SSM/I performance characteristics [from Hollinger and Lo, 1383].

Parameter

Center Frequency, GHz

Polarization

Radiometric Accuracy, K

AT (noise), K

Dynamic Temp. Range, K

Effective Resolution, km

(3 dB antenna beamwidth;

includes integ. time)

Integration Time, ms

Radiometer Performance

19.35 ± 0.05

Vert.. 5 Horz .

1.5

0.8

375

70 x 45

7.95

22.235 ± 0.05

Vert.

1.5

0.8

375

60 x 40

7.95

37.0 ± 0.1

Vert. § Horz.

1.5

0.6

375

38 x 30

7.95

85.5 ± 0.3

Vert. § Horz.

1.5

1.1

375

16 x 14

3.89

-43-
5.2 Dataproducts

The DMSP will transmit SSM/I data to the Telemetry and Data Relay

Spacecraft (TDRS) in geosynchronous orbit for relay to a ground station in

White Sands, New Mexico. This station will, in turn, relay the data over a

domestic communications satellite link to the Fleet Numerical Oceanography

Center (FNOC) and to Air Force Global Weather Central (AFGWC).

The raw data stream is packed to be compatible with the sampling strategy of the Optical Linescan System (OLS), the primary sensor aboard DMSP.

The OLS accepts one 3276-bit block from the SSM/I every second. These

blocks are either transmitted immediately or recorded on board for later

playback to a ground station. Once this serial bit stream is recorded on the

ground, it is defined as Level 0 data (see Table 5 for processing level

definitions).

Table 5. Processing level definitions.

Level 0

Level 1

Level 1.5

Level 2

Level 3

Raw satellite data

Earth located engineering units

Temperature Data Records (TDR's) and

Sensor Data Records (SDR's)

Navy's Environmental Data Records (EDR's)

in swath format

Gridded brightness temperatures and ice

concentration maps (total ice, first year,

multiyear)

Processing at FNOC and AFGWC will include production in swath format

of Sensor Data Records, or SDR's (Level 1.5), Environmental Data Records, or

EDR's (Level 2), and, possibly, antenna temperatures (TA's) to be included as

Temperature Data Records, or TDR's (Level 1.5 ). The antenna temperatures

are the data from which the SDR's are calculated by correcting for the

antenna pattern. The SDR's are earth-located brightness temperatures in

degrees Kelvin that have been tagged for surface type, calibrated, corrected

for antenna pattern, formatted, and written to a file for later processing.

-44-
The EDR's are geophysical parameters computed from the SDK's using operational algorithms. NESDIS Satellite Data Services Division (NESDIS/SDSD)

•will archive TDK's, SDK's, and EDR's.

The Working Group considers the operational algorithms inadequate for

research applications. To meet research needs, we recommend computation

and archival of sea ice extent, concentration, and type using algorithms

currently under development.

5.3 Radiometer Performance Checks

Spacecraft sensors tend to undergo long-term changes in sensitivity,

usually in the form of a degradation. Consequently, the calibration of SSM/I

should be routinely validated by examining, at least monthly, the brightness

temperatures of certain monitor areas, regions of the earth a few hundred

kilometers across, known to have only a small variation in microwave emission over long periods (see Table 6). Examples of such regions are the

Table 6, Summary of data sets recommended for archival.

Data Set

SDK's

12.5 km

brightness

temperatures

25 km

brightness

temperatures

50 km

ice maps

Ice extent

Monitor areas

Time Average

(days)

N/A (swath)

1

1

3

1

1

Channels

(GHz)

19.4 V,H

22.2 V

37.0 V,H

85.5 V,H

85.5 V,H

19.4 V,H

22.2 V

37.0 V,H

Combination

85.5 V,Ha

19.4 V,H

22.2 V

37.0 V,H

85.5 V,H

Parameters

Global

brightness temperatures

in swath format

Gridded average

brightness temperatures

(polar regions only)

Gridded average

brightness temperatures

(polar regions only)

Gridded average total

ice concentration and

multiyear ice fraction

(polar regions only)

Ice boundary

Summary of brightness

temperatures in monitor areas

Storage

(MBytes/year)

28,000

-

2,150

1,350

240

2

0.6

These data sets will be calculated by means of research algorithms selected by the NASA

Sea-Ice Algorithm Working Group. The combination of channels to be used in the algorithms

has not yet beeen specified.

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Southeast Pacific, the Amazon rain forest, and the antarctic ice sheet. By

using the measured brightness temperatures of these features, and by

linearly adapting SSM/I SDR radiances to keep these values constant, consistent brightness temperatures should be provided to the algorithms used

to generate geophysical parameters for research purposes.

5.4 Earth Location

It is crucial to know precisely the location of each of the scene stations.

The required accuracy is one-third the size of the finest resolution pixel. In

the case of SSM/I, the 85 GHz data have a spatial resolution of 14.5 km, which

implies the earth location must be accurate to within 5 km. It is expected

that the SDK's earth location accuracy should satisfy this requirement, based

on the reported system specifications. However, routine checks should be

made to ensure this accuracy is maintained.

-46-
6. Data Products for Research

The data products required are determined by the time and space scales

at which the sea ice cover responds to its environment. The inherent scale of

sea ice microwave variation is a few days during periods of ice-edge advance

and retreat and surface melting; it is about one day along the ice margin.

Spatial scales in the margins are tens of kilometers. These scales call for

daily data at a resolution of 10-20 km.

The SSM/I data set will be of sufficiently fine spatial and temporal resolution to permit mapping of the polar regions on a daily basis. The appropriate

daily data base should contain brightness temperatures for all channels,

earth-gridded and binned, with two computed geophysical parameters: ice

concentration and ice type. The recommended ice products are summarized

in Table 6.

The geographical areas that should be covered are illustrated in Figure 21. The northern area includes the North American Great Lakes and the

Sea of Okhosk, and the southern area includes the maximum extent of Southern Ocean sea ice.

Because the brightness temperature data base covers several years and

is expected to be useful beyond the lifetime of the sensor, it is important to

place the scientific archive on media that are highly reliable, long lived, and

easily accessible. These requirements preclude the use of magnetic tape as a

primary storage medium. Experience has shown that tape degrades with

time, requires periodic exercising, and is time consuming to handle. A far

better solution would be digital optical storage disks. This method fulfills all

the archival requirements, with the added advantage of being less expensive

than any of the magnetic media alternatives now available.

6.1 Data Archives and Access

In addition to the basic archival services provided by NESDIS/SDSD, the

Working Group endorses development of capabilities for providing high-level

ice products by the Pilot Ocean Data System (PODS) at the Jet Propulsion

Laboratory (Table 6) and the planned transfer of these capabilities to the

National Snow and Ice Data Center (NSIDC) at the University of Colorado after

the launch of SSM/I.

These archives are intended principally for scientists performing retrospective analyses of sea ice and snow phenomena. Access will be provided by

a "menu-driven," on-line system that allows browsing and catalog review. The

research data sets selected will be written on magnetic tape or optical disk

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160

100

180

80

200

220

240

280

260 100

240 120

140

200 160

180

Figure 21. Geographical areas in the northern and southern hemispheres to be

covered by the PODS archive of SSM/I data, which urill include

microwave brightness, ice concentration, and ice type. These data, originally acquired in spacecraft orbital coordinates, will be converted to terrestrial geographical coordinates.

-48-
and delivered to the user by mail. Thus the time between selection and

receipt of data can, if necessary, be as short as one week.

If real-time data are needed, for instance, in support of field activities,.

such data will be available from the Navy-NOAA Joint Ice Center.

6.2 Algorithm Selection

Geophysical algorithms are still in a state of development. Therefore,

the Science Working Group recommends that algorithm research be

vigorously pursued, including the development of validation methods: NASA

is currently sponsoring research to develop an optimal set of algorithms to

fulfill our recommendations, and we expect that this set will be available by

the time of launch. We recommend that this activity be followed by suitable

studies toward validating the algorithms used.

-49-
7. Conclusion

Interactions of the atmosphere and ocean in high latitudes are an essential component of the global climate system. This interaction is strongly

affected by the presence of ice floating on the sea surface. A key problem in

developing an understanding of air-sea-ice interaction has been lack of suitable observations on the appropriate time and space scales in the remote

and inhospitable polar regions. Over the past decade earth observing satellites carrying microwave sensors have demonstrated a unique all-weather

capability to acquire sea-ice data year round. Microwave data sets provide

routine global mapping of ice extent, concentration, and type. The data to be

acquired from the Special Sensor Microwave Imager (SSM/I) aboard Defense

Meteorological Satellite Program spacecraft will extend the already substantial time series of ice observations from past satellite missions and greatly

enhance the value of existing data.

Exploitation of SSM/I data for sea-ice research involves the following

steps:

a. An archive of both observed brightness temperatures and derived geophysical parameters must be maintained to provide data of optimal

utility for the research community.

b. Continued research is needed to refine the methods for deriving geophysical data from the microwave record. This research includes

simultaneous in situ studies of ice conditions and microwave behavior,

and analysis of the historical data set.

c. Investigations of polar ice, ocean, and atmosphere interactions should

include acquisition and analysis of in situ data from buoys, surface

ships, and submarines, as well as modeling studies.

The Science Working Group strongly recommends that a capability to

process, archive, and access the SSM/I data be developed by the Pilot Ocean

Data System at the Jet Propulsion Laboratory. Furthermore, we recommend

that the SSM/I data base be housed at the National Snow and Ice Data Center

in Boulder. The archive should contain microwave brightness temperatures

and geophysical parameters derived from these brightness temperatures.

These data should be archived in swath format, and averaged to provide gridded or mapped products. We recommend creation and operation of an

archive containing the following data:

a. Global swath brightness temperature data for all seven channels of the

SSM/I.

-50-
b. Daily averaged microwave brightness temperatures at 25 km resolution

for the five lower-frequency channels and at 12.5 km resolution for the

85 GHz channels.

c. Three-day averaged estimates of ice concentration and multiyear ice

fraction on a 50 km grid.

d. Daily ice-edge maps on a 12.5 km grid.

e. Daily brightness temperatures over selected areas with little seasonal

change. These data will be used to monitor long-term sensor performance.

Satellite data can be used to gain a fuller understanding of processes

such as air-sea-ice interactions only in the context of a total research program that includes in situ observations, modeling, and process studies. The

value of these programs depends on suitable methods for integrating their

diverse output products and initial assumptions. This is the area in which

the recommended data archive system, optimized for efficient access, plays

the greatest role. The development and maintenance of an archive for SSM/I

data is the central recommendation of this document. Through the archive,

the satellite observations and the research activities of all scientists will

enhance progress in describing air-sea-ice interactions and their effects on

the ocean-atmosphere system. •

Because geophysical algorithms remain in a state of development, the

Science Working Group recommends that algorithm research be vigorously

pursued. NASA is currently sponsoring research to develop an optimal set of

algorithms to fulfill the recommendations made here, and we expect that this

set will be available by the time of launch. We recommend that this activity

be followed by an algorithm validation effort comprising focused field experiments, supplemented by data from submarines, buoys, and airborne surveys.

These data are needed both to validate the satellite measurements and to

complement them by providing information that cannot be acquired from

space.

-51-
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