CLOUD COVER DETERMINATION IN POLAR REGIONS

FROM SATELLITE IMAGERY

R.G. Barry, J.R. Key, and J.A. Maslanik

Cooperative Institute for Research in Environmental Sciences

and

Department of Geography

University of Colorado, Boulder

Semi-Annual Report

to

NASA Climate Program

Grant: NAG-5-898

15 June 1988

(EASA-CR-1829G6) CLOUD CO.VEE DETEBUINATION N88-24123

IJS POLAR fiEGICMS FfiGH SfilTEIIllE IflAGEBY

Semiannual fiep-ort (Colorado Ociv.) 53 p

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G3/U7 0145913
1.0 Introduction

The principal objectives of this project are

1) to develop sutable validation data sets to evaluate the

effectiveness of the ISCCP operational algorithm for

cloud retrieval in polar regions and to validate model

simulations of polar cloud cover;

2) to identify limitations of current procedures for varying

atmospheric surface conditions, and to explore potential

means to remedy them using textural classifiers;

and 3) to compare synoptic cloud data from a control run

experiment of the GISS climate model II with typical

observed synoptic cloud patterns.

The following sections list the current investigations underway

and summarize the progress made to date since the last

semi-annual report.

2.0 Comparison of Modeled and Observed Cloud Cover in the Arctic

The Goddard Institute for Space Studies (GISS) general

circulation model was used to predict cloud cover in the Arctic.

Monthly average cloud fractions calculated by the GISS model were

evaluated in terms of temporal and spatial distributions, and

were compared to observed cloud amounts. The GISS model

cloudiness in the Arctic was found to demonstrate the seasonal

variability expected based on observational data, but at a much

reduced range in the central Arctic. As with the observational

data, the zone of maximum cloud cover moves north in the summer,

but the calculated amounts are less than expected by about

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10-20%. The GISS results indicate that, while most of the cloud

cover would appear to be due to large-scale processes, there is

some evidence that reduced ice concentration in summer may give

rise to some low-level cloud on a limited regional basis. The

attached paper provides additional details.

3.0 Test Data Sets

The data being used to investigate the spectral and textural

properties of polar clouds consist of a set of geolocated and

first-order calibrated AVHRR data merged with digital passive

microwave imagery acquired by the Nimbus-7 Scanning Multichannel

Microwave Radiometer. The attached draft paper describes the

purpose, creation, and use of these data. Collaborative analysis

and publication of results with Professor E. Raschke, University

of Koln, is planned.

4.0 Catalog of Spectral and Textural Signatures for

Polar Clouds and Surfaces

The design and testing of algorithms for cloud mapping

requires an understanding of the spectral and textural properties

of the features to be classified. The objective of this task is

to improve our understanding of these characteristics for polar

clouds, snow, and sea ice by recording the means and standard

deviations of these features as derived from multispectral AVHRR

data. To achieve this objective, training areas representing a

variety of cloud and surface combinations were selected from the

merged AVHRR and SMMR passive microwave data sets. The

_ 2 -
statistical characteristics of these training areas in terms of

spectral reflectance and temperature as well as textural

properties were summarized and recorded.

Comparison of this "catalog" of spectral and textural

information with other published data show good agreement,

although differences increase as the homogeneity of the training

area decreases. For example, clouds of a given class that

exhibit varying optical depths yield different spectral

signatures than is the case for optically thick cloud. The

resulting large standard deviation around the class mean is a

likely source of error, and increases the difficulty of

objectively evaluating the performance of different

classification schemes. To help alleviate these problems, a

fuzzy-sets classification routine was developed and tested and a

means of developing artificial data sets was devised. This work

will be described in the next semi-annual progress report.

5.0 Artificial Data Sets

In order to test the sensitivity of cloud classification

algorithms, a control data set with known characteristics is

needed. We are developing a series of such data sets which range

in complexity from very simple geometric representations of

clouds and surfaces with no internal spectral variation to very

complex cloud/surface models which have free-form shapes with

both spectral and textural characteristics. Surface types are

snow-covered and snow-free land, open water, and sea ice. Cloud

layers are classified as low, middle, and high, where levels are

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defined by AVHRR channel 4 brightness temperatures. Data for

each region are then generated for each AVHRR and SMMR channel as

well as sea ice concentration using emp i ri cally-derived

statistics and random-number generators based on uniform,

Gaussian, and lognormal distributions.

Testing will be done on the effects of varying the

proportion and location of the surface/cloud types, the type of

distribution assumed for the classes, whether or not these

distributions overlap in spectral and textural space, and the

class characteristic values. These distribution-related changes

are particularly important in the testing of maximum likelihood

classifiers.

6.0 Coherence and Threshold Methods

In anticipation of and justification for the spatial and

temporal tests of the ISCCP algorithm, the spatial and temporal

coherence tests have been implemented in an attempt to determine

clear sky radiances for each pixel over the period.

Additionally, we are currently investigating the possibility of

setting thresholds dynamically, based on information contained

within the image itself, for use in the ISCCP algorithm.

6.1 Spatiotemporal Coherence

In the last report, we described the application of the

spatial coherence method of Coakley and Bretherton (1982) to

polar data. To aid further in the determination of clear sky

radiances, locations may be examined for their variation in time.

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Scenes which do not vary significantly from the day before and

the day after are assumed to be clear, and a clear sky radiance

for the time period can be set equal to the mean or extremum of

the series. The feet of the arch are identified in the same

manner as in the spatial coherence method. Figure 1 shows

temporal coherence plots for two three-day periods. Points with

low standard deviations generally represent clear pixels.

Problems with temporal coherence occur primarily with stationary

cloud systems. Such systems may exhibit high temporal coherence

over a short time period (i.e. a few days). Lengthening the time

series will, of course, alleviate this problem.

Spatial and temporal coherence tests may be combined for the

purpose of identifying which feet of the spatial coherence arch

represent clear sky conditions. Those locations which have a low

spatial standard deviation on any given day AND a low temporal

standard deviation for any three-day period which includes that

day are labeled "clear". In this manner, a clear sky radiance

map can be constructed for the five day period where each value

represents a clear sky value on one or more (in this case an

average) of five days, or possibly none at all if clouds persist.

This spatiotemporal coherence method was applied to each

pixel in a (250 km)^ region east of Novaya Zemlya. The method

labels those pixels which have low standard deviations in both

time and space as clear. A visual comparison of these maps

verified that those pixels labeled "clear" in a manual

interpretation were generally labeled "clear" by the

spatiotemporal coherence method. However, stationary cloud

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systems which were also uniform in texture were mislabeled as

clear. Figure 2 gives an example of this problem.

6.2 Dynamic threshold

To allow for the influence of cloud size on classification,

Coakley (1987) developed a dynamic threshold method for the

Pacific Ocean where an infrared threshold is adjusted to achieve

the highest correlation between the threshold-derived cloud cover

and the mean emitted radiance for mesoscale-sized subregions

within the scene. Unlike the spatial coherence method, the

dynamic threshold method requires no pixel to be completely

cloud-covered or cloud-free. Like the spatial coherence method,

the dynamic threshold method assumes a linear relationship

between emitted radiances and fractional cloud cover, and is

therefore inappropriate for systems like convective ensembles or

cirrus clouds, where the relationship is generally multivalued

and nonlinear.

In order to test whether the dynamic threshold method could

be used to provide more realistic thresholds than are currently

applied in the ISCCP algorithm, spatial coherence diagrams have

been constructed from the AVHRR imagery for consecutive days.

Thresholds were then determined using the dynamic threshold

method. The actual threshold chosen is the one which produced

the maximum correlation between mean radiance and cloud fraction

for subregions of size (50 km)^. Results of this approach

demonstrate that improvements can be achieved using the dynamic

thresholding process as opposed to more restrictive

- 6 -
parameterizations of thresholds.

While the dynamic threshold method can theoretically be

extended to any number of clouds layers and surface types, such

an extension would produce a series of simultaneous equations

equal to the number of unknowns. A unique solution for each of n

unknowns would require n independent spectral channels.

Unfortunately only channels 1 or 2, 3, and 4 (or 5) may be

considered sufficiently independent so that the number of

unknowns is limited to three. An algorithmic solution to this

problem is at least theoretically possible, and is being

considered as a possible route to improve classification of

mixtures of cloud layers and surface types. If a region is

divided into small enough subregions such that each contains

pixels which belong to only two feet of the spatial coherence

arch, then the dynamic threshold method may be applied separately

to e'ach group of subregions which contain only one surface and

one cloud layer. This method was tested here on regions 48

pixels square or (240km)^ with subregions (30 km)^ for a total of

36 pixels per subregion and 64 subregions per region, an example

of which is given in Figure 3. Evaluation of the results of this

test is currently underway.

7.0 Modifications to the ISCCP Algorithm

A thoroughly-tested version of the ISCCP algorithm for lowand mid-latitudes has been applied to polar data by Rossow

(1987). The algorithm misses approximately 20% of the cloud

(Rossow, 1987, personal communication). Problems cited include:

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"real" cloud amount is high which forces the algorithm to use

more extreme values for clear sky radiances; no distinction is

made between snow-covered and snow-free land, or sea ice and open

water in setting thresholds; thresholds were not "tuned" to the

low radiances encountered in polar regions; and no adjustments

were made for solar zenith angle.

Modifications to the basic ISCCP algorithm are being made to

address these problems directly. Threshold values in the

spatial and temporal variation tests are being adjusted to take

into account the similarity in brightness temperatures between

low cloud and sea ice. Adjustments are also necessary to deal

with varying surface types within regions and subregions.

Surface types are determined with a land mask, SMMR 37 GHz

(horizontal) data, and SMMR-derived sea ice concentration.

The basic ISCCP algorithm assumes a constant surface type

over the five-day period. In most cases, this assumption is

valid. However, snow melt in the spring and snowfall in late

summer are particular problems for this algorithm. Movement of

sea ice into or out of a region will cuase similar problems. In

both cases, emissivities of the surface change, thereby affecting

the response in the thermal channels. Additionally, albedos

change dramatically so that those portions of the algorithm which

incorporate visible data will be affected the most. The problem

lies in the determination of the clear sky composite maps for the

visible and thermal channels. If, for example, sea ice moved

into a region on the last of day of the period, the average

albedo making up the clear sky value would be significantly

- 8 -
affected. If the algorithm detected this event and instead chose

an extreme (minimum albedo) for the composite value from the

first four days, this last day pixel would be labeled "cloud" in

the final bispectral threshold test.

In the algorithm specifications (Rossow, 1987) the

compositing step is to use five-day means if there are enough

clear pixels or, if not, 30-day means and extremum. In the polar

regions, surface characteristics may be too variable for the use

of 30-day values to be valid, particularly in summer when sea ice

motion increases and concentration changes rapidly. In late

summer, snow may occur at any time. Therefore, in the modified

version of the algorithm, it is assumed that pixels within a

reasonably small spatial area on the same day and of the same

surface type will be no less similar than the same pixel up to

thirty days later.

Additionally, tests are being performed on the utility of

incorporating a third channel in the analysis - AVHRR channel 3.

In spite of the fact that this channel is sometimes noisy, and

that there is a reflected component in the daytime images, this

spectral band is particularly useful in distinguishing low cloud

from sea ice.

8.0 Summary

In the first six months of this first project year a

methodology was developed for combining AVHRR and SMMR data.

These data were calibrated and registered to a polar sterographic

projection for subsequent digital analysis. Cloud cover and

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surface types were manually interpreted, viewing angle effects

were examined, and the development of a catalog of spectral and

textural properties of polar clouds and surfaces was begun.

Additionally, cloud analysis and clustering methods such as

Coakley and Bretherton's spatial coherence and the fuzzy sets

approach were implemented.

In the second half of the year, work has continued on the

catalog of spectral and textural features, where new combinations

of the calibrated AVHRR data (i.e. ratios and differences of

AVHRR channels) were included in the analysis and data from two

seven-day sequences of imagery for two areas of the Arctic were

studied. The spatial coherence method was extended to include

time ("temporal coherence") and space and time ("spatiotemporal

coherence"). These methods are being examined to gain further

insight into the spatial and temporal features of polar clouds

and surfaces as they effect the functionality of the ISCCP

algorithm, which was implemented here and is currently undergoing

testing and modification.

The relationship between theoretical and empirical

approaches was examined with the GISS general circulation model,

where the spatial and temporal distributions of monthly average

cloud fraction were compared to observed cloud amounts. The

sensitivity of the ISCCP algorithm and clustering procedures is

being tested through the use of synthetic data sets where

statistical and geographical characteristics of image data are

carefully controlled.

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9.0 Future Work

During the next year of this two-year project, we will

integrate the procedures developed during the first year and

apply them to additional data, in particular winter (January)

data for the Arctic and both summer and winter data for the

Antarctic. Statistical distributions of the spectral and

textural features of polar clouds and surfaces will be analyzed,

as these have important implications for the use of statistical

classifiers such as maximum likelihood method commonly applied in

remote sensing.

The results from the statistical analyses of empirical data

will help guide the development of synthetic data sets, which in

turn will aid in the development of cloud analysis algorithms.

Synthetic data sets have many potential uses and can include

fuzzy boundaries between classes (possibly incorporating the

fractal nature of cloud edges) and texture. These possibilties

will be examined.

Work will continue on the ISCCP algorithm testing and

modification for use in the polar regions. As described in this

report, a number of problems with the application of this

algorithm remain unsolved.

- 11 -
References

Coakley, J.A., Jr., 1987. "A dynamic threshold method for

obtaining cloud cover from satellite imagery data," J.

Geophys. Res., 92, no. D4, 3985-3990.

Coakley, J.A., and P.P. Bretherton, 1982. "Cloud cover from

high-resolution scanner data: detecting and allowing for

partially filled fields of view," J. Geophys. Res., 87, no.

C7, 4917-4932.

Raschke, E., 1987. "Report of the ISCCP workshop on cloud

algorithms in the polar regons," National Institute for

Polar Research, WCP-131, WMO/TD-No. 170, Tokyo, Japan, 19-21

August 1986.

Rossow, W.B., 1987. "Application of ISCCP cloud algorithm to

satellite observations of the polar regions," in Raschke,

E., 1987. "Report of the ISCCP workshop on cloud algorithms

in the polar regons," National Institute for Polar

Research, WCP-131, WMO/TD-No. 170, Tokyo, Japan, 19-21

August 1986.

Rossow, W.B., 1987. "Preliminary documentation for ISCCP Cl data

set", Pilot Climate Data System, Goddard Institue for Space

Studies, NASA.

Rossow, W.B., F. Mosher, E. Kinsella, A. Arking, M. Desbois, E.

Harrison, P. Minnis, E. Ruprecht, G. Seze, C. Simmer, and E.

Smith, 1985. "ISCCP cloud algorithm intercomparison," J.

Clim. Appl. Meteor., 24, 877-903.

- 12 -
List of Figures

Figure 1: Examples of the temporal coherence method for two

three-day series of the same region. The horizontal

axis is the mean of 2x2 pixel cells, the vertical

axis is the standard deviation.

Figure 2: Cloud map of a (240 km)^ region showing the area

labeled "clear" by the spatiotemporal coherence

method (solid line), and the portion of that area

determined to be clear from a manual interpretation.

Figure 3: Spatial coherence and the dynamic threshold for a

(250 km)2 region.

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APPENDIX I

Arctic Cloud Cover Simulated by the

Goddard Institute for Space Studies

General Circulation Model

Robert G. Crane

Report

Submitted to Dr. Roger G. Barry

Cooperative Institute for Research in Environmental Sciences

December 1987
1. Introduction

Most General Circulation Model (GCM) sensitivity experiments show a

marked high latitude response to external forcing such as changes in the

solar constant, changes in atmospheric CC^, or experiments with glacial

boundary conditions. The implication of all of these experiments is that

the polar regions may play a significant role in global climate and may also

be one of the first places to demonstrate present or future climate change.

Climatic processes in the Arctic are dominated by the presence of sea

ice-primarily through the effect on the surface albedo and oceanatmosphere heat exchange—and by the polar cloud cover. Unfortunately,

neither sea ice nor cloud cover are particularly well treated in many

GCM's, and in the case of the cloud cover the observational data are also

very limited.

The role of cloud cover in climate has been discussed by Crane and

Barry (1984) with a particular focus on the polar regions. The Arctic cloud

cover is known to have a large seasonal variability increasing from about

40-60% in winter to 80-90% in summer (Huschke, 1969). This large increase

in summer is attributed, in part, to a shift in the cyclone tracks with an

increase in the number of low pressure systems that enter the Arctic Basin

at this time. It is also assumed that the rapid increase in spring/summer

cloud cover is partly due to decreasing ice concentration and increased

moisture availability. At present, however, we do not have sufficient

observational data to fully support these assumptions. The problems of the

satellite retrieval of cloud parameters is being examined in related studies

as part of this project; it is the purpose of the present report to
concentrate on the Arctic cloud cover simulations in the Goddard Institute

for Space Studies' (GISS) GCM.

2. The GISS GCM

The present study examines the Arctic cloud cover simulated by a

control run of the GISS Model II. This model, described in detail in

Hansen et al. (1983), has a horizontal resolution of 8 degrees latitude by 10

degrees longitude and 9 atmospheric layers (2 in the boundary layer, 5 in

the remainder of the troposphere and 2 in the stratosphere). Snow depth,

cloud cover and cloud height are computed in addition to the usual

atmospheric variables, and the model includes diurnal and seasonal cycles.

The ocean temperatures and the sea ice cover, however, are specified

climatologically.

A comparison of the observed and modelled synoptic climatology of the

Arctic Basin has been made by Crane and Barry (1988) using this model.

The observed pressure fields over the Arctic are compared with those

obtained from the model using a classification typing procedure developed

by Kirchhofer (1973) and a rotated principal components analysis for both

data sets. The analysis shows that the GISS model appears to give a quite

realistic simulation of both the spatial and temporal characteristics of the

sea level pressure field. Using the same synoptic climatology Barry et al.

(1987) suggest that synoptic controls may be an important factor

determining the observed cloud amount during the spring and early summer

of 1979 and 1980. Cloud cover is derived from the model data in such a

way that it is unlikely that a similar synoptic scale study would be valid in

this case (see below). Some idea of the relative importance of synoptic
scale advection versus local effects can be obtained, however, by an

examination of the monthly data at various levels in the model.

3. The GISS CGM Cloud Scheme

The GISS model calculates both large scale and convective cloud cover.

The actual cloud amounts are not saved on the 5-hourly output available

from the control run, but they can be inferred from the optical depth

which is saved for levels 1 to 7.

Convective Cloud

The convective cloud is obtained as a proportion of the mass of

saturated moist air rising from one layer to the next. The scheme

computes a cloud fraction which, at each time step, is compared to a

random number between 0 and 1. If the random number is less than the

cloud fraction, a cloud amount of 1 is assigned for that time step; if the

random number is greater than the cloud fraction a cloud amount of zero is

assigned. The GISS model, therefore, uses a cloud amount of zero or one

at each time step and simulates the effects of fractional cover within a

grid box by using full cloud cover for a fractional amount of time. This

gives similar results to computing separate radiation calculations using

fractional cloud for both the clear and cloudy portions of the grid box, but

saves computer time (Hansen et al., 1983).

Large-Scale Clouds

The large-scale cloud is given by the saturated fraction of the grid

box, assuming uniform absolute humidity and subgrid scale temperature

variation. As with the cumulus cloud, the fractional amount of cloud cover

is also compared to a random number and either zero or complete cloud
cover assigned for the time step. No distinction is made between

convective and large-scale cloud in the present analysis but, in general,

large-scale clouds account for about 80% of the global cloud cover

generated by the model (Hansen et al., 1983).

Cloud Optical Depth

In place of the fractional cloud cover, the 5-hourly data give the

optical depth at each layer of a grid box in the presence of clouds. That

is, a cell has an optical depth of zero if no clouds are present and a value

greater than zero in the presence of clouds. The actual optical depth is a

function of cloud type, temperature and depth in the atmosphere. For

large-scale cloud, if the layer temperature is less than 258 K the optical

depth is given as .33 to match the observational data of Plan (1975) which

indicates that cirrus clouds typically have an optical depth of 0.25 - 0.5.

Otherwise the optical depth is proportional to the pressure thickness of the

layer containing the cloud. For convective cloud the optical depth is .08

times the pressure thickness giving an optical depth of about 8 per 100 mb

of thickness. Where both large-scale and convection clouds are present in

a cell, the largest optical depth is used.

Determination of Cloud Cover Fraction

While the optical depth of cloud is obviously the important factor in

terms of radiative calculations, this is difficult to compare with

observations that are made in the form of fractional cloud cover. For the

present study, the fractional cloud cover is obtained from the optical depth

data by averaging over time.

The cloud optical depths are used to produce a binary map of cloud

cover at each level; that is, for optical depth > 0. cloud cover = 1, for
optical depth = 0. cloud cover = 0. The binary grids are used to produce

low, middle and total cloud cover; low cloud is cloud in levels 1 or 2 (from

the surface to about 850 mb), middle cloud is cloud in levels 3 and 4 (about

850 mb to 550 mb), and total cloud includes cloud at any level. The binary

grids of low, middle, and total cloud are averaged over 30 days (150 grids).

The time average gives a fractional amount which is similar to the original

monthly cloud clover fraction calculated by the model (Figures 1 and 2).

4. Results

The monthly average cloud cover fraction for the Arctic north of 72°N

is shown in Figure 3a. This shows a seasonal cycle having a summer

maximum similar to that of Huschke (1969) (Figure 4), but with much lower

values. At the same time, the winter values are much higher in the model

showing that the seasonal range in the model cloud cover for the central

Arctic is much less than the observations would suggest. Figure 3b shows

the seasonal cycle for the area north of about 55°N and in this case the

cycle is reversed with less cloud in the summer months. This reversal in

the seasonal cycle is also demonstrated on a regional basis although both

the western Arctic (135°W-135°E) and the Canadian Arctic (45°W-135°W)

show very little seasonal variability (Figure 5), and most of the variation

occurs in the European sector (45°W-45°E) and the Kara/Laptev Sea (45°E135°E). In these regions the seasonal cycle is again reversed with less

cloud in the summer months. These regions extend from approximately

86°N to 55°N and should not, therefore, be compared with the regional data

of Huschke. The change in the seasonal cycle reflects the change in

cyclone tracks as they move further north in summer; the decrease in the
regional cloud fractions being matched by an increase in the central Arctic

fraction (Figures 3a and 5d). This seasonal variability is further illustrated

in the cloud maps for January and July (Figures 6 and 7). In January the

largest cloud fractions are found in the higher mid-latitudes primarily in

the North Pacific region and in the NE Atlantic, while in July the greater

cloud cover occurs north of about 70°N. The July distribution of total

cloud cover for the Arctic agrees with Gorshkov (1980) in that there is a

relative maximum at high latitudes with the largest fractions being in the

north Atlantic region. However, there is a major difference in the Central

Arctic where Gorshkov shows 90% cloud cover while the model has a

relative minimum with less than 60% cloud cover. One should note,

however, that the model values for June and August are much higher

(Figure 5e) and that we are only examining one year of model output.

Comparing Figures 6a-c it is apparent that the regions of largest low,

middle and total cloud cover coincide suggesting that cloud cover is

controlled by the large scale processes. We should also note, however, that

the low cloud fraction is much higher than either Voskresenskiy and

Chukanin (1959) or Vowinckel and Orvig (1970) find for the Eastern Arctic.

Figures 7a-c for July, on the other hand, show the higher levels of low,

total and middle cloud coinciding in the western Arctic and North Atlantic,

but with an area of increased low cloud north and west of the Taymyr

Peninsula where ice concentrations have already begun to decrease in the

model.

The minimum ice extent in the model occurs in September with low ice

concentrations (less than 30%) in the southern Barents/Kara Sea and along

the Beaufort Coast. Ice concentrations of less than 50% are found along
the Siberian coast and ice concentrations between 75% and 85% are found in

the Beaufort up to about 78°N and the Barents Sea up to about 86°N. The

September cloud cover shows a general decrease over the July figures with

the largest cloud cover fractions being found further south once more. For

the most part there is a general agreement between the low, middle and

total cloud, but again there are areas which have a relative maximum in

low cloud and a minimum in middle cloud corresponding to the regions of

low ice concentration in the Barents/Kara Sea and, to some extent, along

the Beaufort Coast. There are extensive areas of low cloud in the

Canadian Arctic and the East Siberian Sea, but these also coincide with

regions of extensive middle cloud as well.

Although it cannot be demonstrated conclusively, these results do

suggest that the decreased ice concentration in the Seasonal Sea Ice Zones

of the Eastern Arctic and along the Beaufort Coast do contribute to

increased low level cloudiness in the model. For the most part, however,

the Arctic cloud cover does appear to be controlled by the larger scale

processes.

5. Summary and Conclusions

The GISS model cloudiness in the polar region of the Northern

Hemisphere demonstrates the seasonal variability that would be expected

from the observational data, but at a much reduced range for the Central

Arctic. As with the observational data, the zone of maximum cloud cover

moves north in the summer, but actual amounts are less than expected by

about 10-20%.

Barry et al. (1987) suggest that there is a strong synoptic control on

8
cloud cover amounts in the Arctic which is supported by the more detailed

microphysical studies of Jayaweera (1982) who also suggests that the lower

stratus layers may be formed by the lifting of fog over sea ice, while the

upper layers are a response to large-scale southerly flow. In the case of

the GISS model, while most of the cloud cover would appear to be due to

large-scale processes, there is some evidence that reduced ice concentration

in summer does give rise to some increased low level cloud on a limited

regional basis. One final consideration is that during the early summer,

when the model is already showing too little cloud in the Arctic, there is

also a tendency for mid-latitude aerosols to be transported to the Arctic

resulting in the presence of optically thick Arctic Haze (Rahn, 1985). This

again may have some further effect on the surface and lower tropospheric

energy balance in the Arctic.
Acknowledgment

This work was supported under NASA Climate Research Grant NAG 5-

898 to Roger G. Barry. We are indebted to Dr. J. Hansen for providing the

GISS Model data.

10
References

' Bairy, R. G., Crane, R. G., Schweiger, A. and Newell, 1, 1987. Arctic

Cloudiness in Spring From Satellite Imagery. J. Climatology, 7: 423-451.

Crane, R. G. and Barry, R. G., 1984. The Influence of Clouds on Climate

with a Focus on High Latitude Interactions. J. Climatology, 4: 71-93.

Crane, R. G. and Barry, R. G.; 1988. Comparison of the MSL Synoptic

Pressure Patterns of the Arctic as Observed and Simulated by the GISS

General Circulation Model. Submitted to Met. and Atmos. Phys.

Gorshkov, S. G. (ed) 1980. World Ocean Atlas. Vol 3, Arctic Ocean. Dept.

of Navigation and Oceanography, Ministry of Defence, USSR (Pergamon

Press, Oxford, 1983).

Hansen, J., Russell, G., Rind, D., Stone, P., Lacis, A., Lebedeff, S., Ruedy,

R. and Travis, L., 1983. Efficient Three-Dimensional Global Models for

Climate Studies: Models I and II. Mon. Wea. Rev., Ill: 609-662.

Kirchhofer, W., 1973. Classification of 500 mb Patterns. Arbeit der

Schweizer Meteorolo. Zentral., 45: 16 pp.

Platt, C. M. R., 1975. Infrared Emissivity of Cirrus-Simultaneous Satellite,

Lidar and Radiometric Observations. Quart. J. Roy. Met. Soc., 101: 119-126.

Rahn, K. A. (ed) 1985. Arctic Air Chemistry. Atmos. Environ., 19.

Vowinckel, E. and Orvig, S., 1970. The Climate of the North Polar Basin.

In Orvig, S. (ed) World Survey of Climatology, 14, 129.

Voskresenskiy, A. I. and Chukanin, K. I., 1959. Meteorologicheskie Usloviya

Obled Eneiya v Oblakov Tipa st is sc. Trudy Arktichescogo i

Antarkticheskogo Nauchno-IssledovateFskogo Instituta, 228: 124.

1-1-
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Figure 3. Seasonal Cycle of Cloud Cover

for the Arctic North of 72N (a) and

North of 65N (b)
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50

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Figure 5. Monthly Cloud Cover (GISS Model) For a) the Western Arctic,

b) Canadian Arctic, c) European Arctic, d) Laptev Sea, and

e) the North Pole
Figure 6. Mean January Cloud Cover From the GISS GCM.

(a) Total Cloud (shaded areas = greater than 70% cloud)

(b) Low Cloud (shaded areas = greater than 60% cloud)

(c) Middle Cloud (shaded areas = greater than 40% cloud)
Figure 6. Continued

ORIGINAL PAGE IS

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Figure 7. Mean July Cloud Cover From the GISS GCM.

(a) Total Cloud (shaded areas = greater than 70% cloud)

(b) Low Cloud (shaded areas = greater than 60% cloud)

(c) Middle Cloud (shaded areas = greater than 40% cloud)
Figure 7.. Continued
Figure 8. Mean September Cloud Cover From the GISS GCM.

(a) Total Cloud (shaded areas = greater than 70% cloud)

(b) Low Cloud (shaded areas = greater than 60% cloud)

(c) Middle Cloud (shaded areas = greater than 40% cloud)
Figure 8. Continued
APPENDIX II

Submitted to Int. Journal of Remote Sensing Letters

MERGING AVHRR AND SMMR DATA FOR

REMOTE SENSING OF ICE AND CLOUD IN POLAR REGIONS

J.A. Maslanik, J.R. Key, and R.G. Barry

Cooperative Institute for Research in Environmental Sciences

and Department of Geography

University of Colorado, Boulder

Campus Box 449

Boulder, Colorado 80309-0449

ABSTRACT

Multispectral data from AVHRR are digitally processed and

merged with multi- frequency, dual-polarized passive microwave

imagery acquired by the Nimbus-7 SMMR. Five channels of AVHRR

data, four channels of SMMR brightness temperatures, and

SMMR-derived ice concentration and ice type are navigated and

co-registered to a polar stereographic grid. The merged data

sets are currently being used in combination with meteorological

information for integrated studies of clouds and sea ice.
1

1. Introduction

The important role that polar processes play in the dynamics

of global climate is widely recognized (Earth Systems Science

Committee 1986). Remote - sens ing of the distribution and

characteristics of clouds and sea ice is required to detect and

monitor variability in the Arctic and Antarctic, as well as to

provide input to climate models.

Optical-wavelength sensors provide high spatial resolution,

but are limited by the long periods of darkness, poor lighting

conditions, and persistent cloud cover common in polar regions.

Passive microwave sensors operate independently of solar

illumination, can view the surface through cloud cover, and

provide surface information not contained in visible or thermal

imagery. However, existing passive microwave sensors yield

relatively poor spatial resolution. A combination of digital

multispectral data appears to be a logical way of capitalizing on

the unique capabilities of each sensor, while overcoming some of

the problems inherent in remote sensing at high latitudes. This

work outlines the creation of a set of merged digital data

consisting of optical, infrared, and thermal imagery from NOAA's

Advanced Very High Resolution Radiometer (AVHRR) and passive

microwave imagery from the Nimbus-7 Scanning Multichannel

Microwave Radiometer (SMMR) into a form suitable for the analysis

of cloud and sea ice cover.
2

2. AVHRR Data

The AVHRR sensors carried onboard the TIROS-N and NOAA

series of satellites sample the earth in 4 or 5 spectral

channels, with a nadir resolution of 1.1 km (IFOV of 1.4

milliradians) at a satellite altitude of 833 km (NOAA 1984). The

channels are 1: 0.58-0.68 m (relected visible), 2: 0.725-1.00 m

(reflecte d visibl e and nea r IR), 3: 3.55-3.9 3 m

(reflected/emitted thermal IR), 4: 10.3-11.30 m (emitted thermal

IR) , 5: 11.5-12.50 m (emitted thermal IR). The data used in this

study are derived from the standard Polar Orbiter Level Ib

archive (NOAA 1984), and consist of partial orbits of Global Area

Coverage (GAC) imagery. This is a reduced-resolution product

created through on-board satellite proc

essing to yield a nadir resolution of slightly better than 4 km.

GAC data files contain calibration parameters, pixel location in

latitude and longitude, and solar zenith angle information. Data

values are recorded in 10 bits.

AVHRR 5-channel GAC imagery for January 6-13 and July 1-7,

1984 covering areas with snow, ice, ocean, and land, and

including a variety of cloud types over different surfaces are

used in this study. First-order calibration of the AVHRR GAC

data was performed using the calibration coefficients contained

in the image files and the methods described in the NOAA Polar

Orbiter Users Guide (NOAA 1984) and in Lauritsen, et al. (1979).

Coefficients for the thermal channels (channels 3, 4, and 5) are

generated in-flight using a stable blackbody as a warm source,
2 fr

and space as the cold source. The data values recorded in

channels 1-5 were converted to approximate spectral albedo in

percen t for channel s 1 and 2, and radianc e in

m i 11 i wa 11 s/m^ - s ter adi ans - cm - *• for channels 3-5. These

conversions were performed as a linear function, with slope and

intercepts provided by the calibration coefficients contained in

the data. The thermal radiances were also converted to physical

temperature in Kelvins, or brightness temperature, using an

approximation of the inverse of the Planck function. The percent

reflectance values produced for channels 1 and 2 by this method

are approximations of albedo. Care is required when comparing

these values to broad-band albedos sensed over a wider spectral

range (Hughes and Henderson-Se1lers 1982).

No adjustment for atmospheric effects were made with the

exception of the solar zenith angle corrections described below.

The typically low water vapor content in polar atmospheres, as

well as the low physical temperature of the atmosphere, reduces

most atmospheric effects to a point where they may be neglected

for the type of analysis performed here.

To account at least in part for the significant effects of

solar zenith angle differences along the wide swath of AVHRR, the

digital number for each channel 1 and channel 2 pixel was

normalized by dividing the pixel value by the cosine of the solar

zenith angle for that pixel as provided in the GAC data records.

Since only one zenith angle is provided per 8 data pixels, the

intermediate zenith angles were interpolated linearly to yield an
3

approximate angle for each pixel.

A final step in the AVHRR data correction involved

extracting the reflected-energy component from the emitted

component in channel 3. At 3.7 micrometers, this channel records

both reflected and emitted energy. Brightness temperatures

estimated from channel 4 were converted to channel 3 expected

radiances using an inverse calculation of the Planck

approximation with channel 3 parameters. Subtraction of these

radiances from the actual radiances measured in channel 3 yields

the component of energy in channel 3 attributable to reflection.

3. Passive Microwave Data

Passive microwave data used in this study were acquired on

January 6, 8, and 10 and July 2, 4 and 6, 1984 by the Scanning

Multichannel Microwave Radiometer (SMMR) on Nimbus 7 (Gloersen

and Barath, 1977). The SMMR collects data in five microwave

channels (6.6 GHz, 10.7 GHz, 18.0 GHz, 21.0 GHz, and 37.0 GHz),

with two polarizations (horizontal and vertical) per channel.

Instantaneous field of view of the sensor varies with channel,

ranging from 148 x 95 km for the 6.6 GHz channel to 55 x 41 km

and 27 x 18 km for the 18 and 37 GHz channels, respectively.

The SMMR data used in this study consist of data that had

been converted from orbital format to a polar stereographic

projection. The dual-polarized 18 and 37 GHz data are mapped to

grid cells with a dimension of 25 x 25 km true at 70° latitude.

The 18 GHz are interpolated to accommodate the 25 km resolution
4

grid. During the gridding process, no distinction is made

between day, night, and twilight orbits, and data from

overlapping orbits are averaged to yield a daily value. The data

stored in these grids are recorded as brightness temperature in

Kelvins.

4. Conbining AVHRR and SMMR Data

Merging of the AVHRR GAC imagery and the SMMR passive

microwave data required selection of a common grid size that

offered a compromise between resolution and data volume. A polar

stereographic projection yielding equal-area pixels true at 70°

latitude was selected as the desired map base, with a 5 km grid

cell (pixel) size. This pixel size represents a slight

degradation of the AVHRR GAC resolution, but has the advantage,

in terms of data processing, of being an even multiple of the

SMMR 25 x 25 km cells. The SMMR data were converted to 5 km cell

sizes by simple duplication of pixels, thereby avoiding any

artificial "improvement" in resolution. Sea ice concentration

and old ice fraction were calculated from the SMMR data using the

NASA Team Algorithm (Cavalieri, et al. 1984). This algorithm is

based on the normalized difference between vertically- and

horizontally-polarized emissivity at 18 GHz.

Re-mapping of the AVHRR data to the polar projection was

performed using the ground locations in latitude and longitude

provided in the GAC records. Latitude and longitude coordinates

for the intermediate pixels were interpolated linearly from each
5

eighth-pixel location and transformed from latitude/longitude to

polar grid coordinates using standard map projection algorithms.

Unfilled grid cells were interpolated by an average of non-zero

neighbors. At the edges of the orbit swath, the reduction in

true ground resolution due to the earth's curvature is reflected

by pixel duplication that yields larger "effective" pixels,

although the grid cell size remains constant across the rectified

polar-stereographic image. Since one of the ultimate objectives

of this project is to investigate viewing angle dependencies on

observed reflectivity and temperature, no adjustments were made

to compensate for these viewing angle effects. Accuracy of the

AVHRR navigation using this method is expected to be within 25

km, or roughly the equivalent of an individual SMMR pixel.

In order to maintain as much radiometric resolution in the

data as possible, the AVHRR and SMMR imagery were typically

stored and processed in 16-bit form.

5. Applications

An example of a calibrated and registered AVHRR channel 1

image centered approximately on Novaya Zemlya, and covers the

Kara and Barents Seas is given in Figure 1. Figure 2 illustrates

the SMMR data (37 GHz horizontal) for the same area. Resolution

differences and differences in information content between the

two sensors are obvious. The color - composite of AVHRR channels 1

and 4, and SMMR-derived ice concentration shown in Figure 3

demonstrates the ability to map ice concentration and ice edge
6

position beneath cloud cover.

The merged AVHRR-SMMR data sets are proving useful for a

variety of applications that benefit from near-simultaneous

observations of clouds and surfaces. Currently, these data are

being used to determine the spectral and textural.properties of

clouds over different concentrations and conditions of sea ice.

Other applications include analysis of directional reflectance

and emittance differences, the investigation of the relationship

between SMMR-derived ice concentration and AVHRR-measured albedo,

comparison of ice concentration and ice-edge position as

estimated by AVHRR and SMMR, and comparison of AVHRR-derived

physical temperatures with physical temperature and emissivity

estimated from SMMR.

These studies will aid in the adaptation of the cloud

analysis algorithm of the International Satellite Cloud

Climatology Project (ISCCP) for use in polar regions (Rossow et

al., 1985). In particular, the SMMR and SMMR-derived data sets

are useful in the determination of surface type, a crucial step

in the algorithm. Although still in the testing and development

stage, this application of the combined data sets has already

shown that the surface often changes within the five- and thirtyday time periods over which the ISCCP algorithm operates,

violating one of its basic assumptions. Future work will

investigate the utility of passive microwave data merged with the

Level B3 AVHRR product to be archived as part of ISCCP (Schiffer

and Rossow, 1985).
6. Conclusion

The ability to retrieve information on albedo, physical

temperature, and brightness temperature from spatially and

temporally co-located imagery is proving to be a useful aid in

comparing the spectral and textural properties of ice and clouds

in a broad range of the energy spectrum. The merged data sets

offer a means of capitalizing on the unique capabilities of AVHRR

and passive microwave data by reducing the inherent limitations

of each sensor (e.g., illumination requirements, cloud cover, and

low resolution).

ACKNOWLEDGMENTS

This work was supported under NASA grant NAG-5-898 and DOD

University Research Instrumentation Program grant N00014-85-C0039. Thanks are due to W. Rossow and E. Raschke for providing

AVHRR GAC data.
8

References

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Advanced Very High Resolution Radiometer infrared

observations. J. Geophys. Res., 90, No. C6, 11667-11677.

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Geophys. Res., 88, 5355-5369.

Earth System Science Committee 1986, Earth system science: a

program for global change. NASA Advisory Council Report,

Earth System Science Committee.

Gloersen, P. and Barath, F.T. 1977, A scanning multichannel

microwave radiometer for Nimbus-G and Seasat-A. IEEE J. of

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Schiffer, R.A. and Rossow, W.B., 1985, ISCCP global radiance data

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Figures

Figure 1: Calibrated and registered AVHRR channel 1 image

centered approximately on Novaya Zemlya, and

covering the Kara and Barents Seas.

Figure 2: SMMR 37 GHz horizontal data for the same area shown

in Figure 1.

Figure 3: A composite of AVHRR channels 1 and 4, and

SMMR-derived ice concentration for the areas shown in

Figure 1.
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Publications Supported in Whole or Part

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Barry, R.G., Crane, R.G., Newell, J., and Schweiger, A., 1988.

"Arctic cloudiness in spring from satellite imagery. A

response," J. Climatol., in press.

Crane, R.G. and Barry, R.G., 1988. "Comparison of the MSL

synoptic pressure patterns of the Arctic as observed and

simulated by the GISS General Circulation Model," Meteor.

Atnos. Physics, in press.

Maslanik, J.A., Barry, R.G., and Crane, R.G., 1987. "Approaches

to the interpretationof cloud cover in the polar regions,"

(abstract). Clouds in Climate II. (A WCRP Workshop on

Modeling and Observations, Columbia, MD). WCRP, NASA, NSF,

NOAA, and DOE, p. 169.

McGuffie, K., Barry, R.G., Newell, J., Schweiger, A., and

Robinson, D., 1988. "Intercomparison of satellite - derived

cloud analyses for the Arctic Ocean in spring and summer,"

Int. J. Remote Sensing, 9 (3), 467-477.