c-+& 

A Progress Report to 

The National Aeronautics and Space Administration 

for NASA Contract NAG5-580 

Determine Precipitaion Rates from Visible and 

Infrared Satellite Images of Clouds by 

Pattern Recognition Techniques 

For the Period of 

1 July 1985 - 31 March 1987 

Submitted by 

Space Science and Engineering Center (SSEC) 

at the University of Wisconsin-Madison 

1225 West Dayton Street 

Madison, Wisconsin 53706 

(608)262-0544 

a 3/' L /LAJ4- 

Professor James A. Weinman 

Department of Meteorology Y 

(BBSB-CE-1802t 1) CJXEEMIBE €&ECIFITATICN N87-15850 

ErATES EBCM VISXELE AHC INFLABEL SATELLITE 

IMAGES OF CLOGDS EY PAIlEBE KECCGbITION 

IECHNIQUE PrcgIoss ReFOrt, 1 Jul, 1985 - 31 Unclas 

Bar. 1987 Ph.D. Ttesis (WiscfDEin Uaiv-) G3/47 43498 

7 

--. 

April 8, 1987 
* 

UFDATZD i?ESCTYE AXD ESYARCH PROGXESS 9'7 A GRANT TO "'3ETERXISE 

'SECIPITATION RATZS FRO!i VISIBLE AND INFRA-XED SGTELLITY IX4GES" 

.iCC3>!PLISHED RESULTS : 

3ur previous work on rainfall retrieval from satellite imagery sought to 

r-?ttieve rainfall rates in three categories from GOES visible and infra-red 

+..-~qes of convective precipitation cells. 

5v 'iu et al. (1985) (Enclosure 1). That work used statistical texture and 

rrid'cance features of the imagery to classify the rain in the categories of no, 

1iZL;t and medium intensity rain. 

This work was described in the paper 

4 nore advanced cloud pattern analysis algorithm was subsequently developed 

':3 take the shape and brightness of the various clouds into account in a manner 

tidr is more consistent with the human analyst's perception of GOES cloud 

iTaTey7. The results of that classification scheme were compared with 

creci3itation probabilities observed from ships of opportunity off the U.S. 

e3s:- coast to derive empirical regressions between cloud types and precipitation 

.~roSability. The cloud morphology was then quantitatively and objectively used 

:J lap precipitation probabilities during two winter months during which severe 

(xld air outbreaks were observed over the northwest Atlantic. This work was 

par: of the Ph.D. research of Louis Garand. Table 1 summarizes precipitation 

probabilities associated with various cloud types. Fig. 1 compares maps of 

2recipitation probability derived from the cloud morphology analysis program for 

two months and the precipitation probabilty derived from thirty years of ship 

observation. 

Table 1: Frequency of occurrence of precipitation for various cloud classes 

Class Number of Cases Prob. (X) 

Clear 

Stratus 

Scattered cumulus 

Scatt. stratocumulus 

Broken cumulus 

Cloud streets 

Rolls 

Polygonal open cells 

Convective open cells 

Bright closed cells 

Nimbostratus 

Altocunulus 

Altocumulus & other 

Thin cirrus 

Yultilayer ctrrus 

Cumulonimbus h cirrus 

Dense cirrostratus 

Overcast cumulonimbus 

37 

29 

101 

49 

15 

37 

43 

24 

13 

62 

84 

40 

29 

52 

106 

163 

33 

113 

0.0 

6.9 

7.9 

8.2 

20.0 

13.5 

41.9 

29.2 

55.6 

17.7 

52.4 

20.0 

24.1 

0.0 

14.2 

58.3 

27.3 

72.6 
d 

L.,W I 'L'., 

._. I 

-. rig. 1. ?lean probability of precipitation retrieved from 11761 cloud 

images 128 x 128 Km for January and February 1954 and the 

thirty year clinatology of January from Isemer and Hasse's 

(1985) climatological atlas. 

4 somewhat unexpected result that emerged from Dr. Garand's analysis of the 

GOES tnagery was the development of a technique to derive surface air and dew 

poi2t temperatures in cloud capped marine boundary layers. 

appears that surface air temperatures over the ocean can be derived within an 

r.3.s. 2 K at a spatial resolution of 128 Km. Fig. 2 shows a time series that 

compares air temperatures measured by a buoy for two months with those derived 

from this technique. The cold air outbreaks that correspond to temperature 

changes of - 20 K are clearly determined by this technique. 

temperature maps obtained by this technique with those obtained from thirty 

years of ship data. 

satellite derived data. 

As a consequence it 

Fig. 3 compares air 

The influence of the Gulf Stream boundary is evident in the 

Fig. 2. Time series of observed and retrieved surface air temperatures 

from Julian days 1 - 60, 1984 at buoy 41001 (34.9 N., 72.9 W.). 

Numbers on plot indicate Julian day. 
-3- OR:GINAL PAGE rs 

OF POOR QUALITY 

3e cloud clsssification schene is iescribed in Garand’s thesis, and a 

nanuscript that describes t\e technique in a somewhat abr%dged form has Seen 

3:il:mitted for publication in J.<.A.!4. (Enclosure 2). 

!,ragram have been provided to Dr. Man Li Vu at the Goddard Space Flight Center. 

?s. Cindy Dixon, one of our graduate students is planning to join Dr. Wu this 

suiaer to work on applications of computer pattern analysis to satellite 

im. ge ry . 

Tapes containing the 

I 75% ?OW 6?H 

I I I 

_- 

I 

I 

- 

2 0”NI‘ T2O7- 

i 

Fig. 3. Surfaceair temperature distributions retrieved from 11761 

sample areas in GOES images during January and February 1984 

and the thirty year climatology from Isemer and Hasse (1985). 

A pattern analysis algorithm has also been developed to identify features 

This work comprised the M.Sc. thesis of 

Burfeind was able to locate the fronts on images of 

The results of that work are 

of images of extra-tropical cyclones. 

Mr. Craig Burfeind. 

cyclones in various stages of development. 

presented in Enclosure 3. 

PXOPOSED RESEARCH: 

‘Je will conduct a more detailed analysis of the data that yielded the 

results shown in the enclosed figures. 

also be analyzed to provide additional validation of the image analysis 

algorithms. The results of those additional studies will be included in a 

description of our work that will be submitted to J.C.A.M. 

Data from the CASP/GALE experiment will 

. The mechanisms that produce precipitation differ in cumulonimbus and in 

nimbostratus clouds. We therefore propose to utilize information on the 

location of precipitating regions, such as cold or warm frontal zones in 

extra-tropical cyclones, to adjust the rainfall retrievals. This aspect of our 

2roposed effort will be based on Burfeind’s research. 
-$- 

The availability of ground truth with which the algoritha can be adjusted 

?as 5een a Hinitation to our past efforts. Ve used ship records of the existence 

,>€ r3i3 and NOAA operational radars in our previous studies. We are in the 

process of acquiring data from radars that have Computer Analyzed Plan Position 

Ixdicator, CAPPI, output. These radars are also regularly calibrated with 

dedicated rain gauges. Data from the FACE tropical rainfall experiment is also 

in hand. We propose to use that data to develop improved retrievals of rainfall 

from images obtained from the AVrIRR and possi3ly from the Operational Line 

Scanner on the military DMSP. The latter imagins system measures reflected 

moonlight as well as sunlight so that the observation period is extended. It is 

ny understanding that members of the Goddard staff are looking into the 

feasibility of operating sensors on l?W?f and GOES-Next that measure nocturnal 

reflected visible light. 
Reprinted from JOGRNAL OF ATMOSPHERIC AND OCEAN~C TECHNOLOGY, vel. 2, No. 3, September 1985 

Amencm MNorolopcJ Sociny 

Determination of Rainfall Rates from GOES Satellite Xrnages 

by a Pattern Recognition Technique 

RONGZHANG WU, JAMES A. WEINMAN AND ROLAND T. CHIN 
314 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 2 

Determination of Rainfall Rates from GOES Satellite Images 

by a Pattern Recognition Technique 

RONGZHANG WU,*.’ JAMES A. WEINMAN* AND ROLAND T. CHIN** 

University of U’isconsrn. Madison. U7 53706 

(Manuscript received II June 1984. in final form 3 January 1985) 

ABSTRACT 

Radiances from clouds observed in visible and infrared images obtained from the SMS-2. GOES-?. and 

GOES4 satellites have been used to estimate rainfall by means of a pattern recognition algorithm that was 

applied to single images. The algorithm classified rain into three classes: 0-no rain (0 5 R < 0.5 mm h-I): 1- 

light rain (0.5 5 R 5.0 mm h-’): and ?-ha\.> rain (5.0 mm h-’5 Rj. The rainfall rates used in the training 

set and those used to test the algorithm were derived from a set of twent?-nine Plan Position Indicator (PPI) 

displays obtained from NOAA operational radars. Data were derived from summer storms. tropical storms and 

cyclones. 

Rainfall from precipitating clouds was classified by a pattern recognition technique that used textural and 

radiance features in a hierarchic decision tree. The analysis was applied to regions 20 Y Zc! km in area that \sere 

measured in the visible spectral region with I X I km and 2 x 2 km resolution and in the infrared with 4 X 8 

km resolution. The radiance features used in this analysis were thc radiance maxima. minima. 2nd the means. 

The textural features that were used included the edge strengths per unit area and the maxima and means of 

the mean. contrast. angular second moment. and entrop) in four directions. 

Of the areas sampled in this stud>, approximatel? one-third were in class 0. one-half were in class I and onesixth were in class ?. Case studies that employed data from both the visibie and infrared senson correctly 

identified rainfall classes 0 and (I + 2) in about 90% of the cases and identification into classes I and 2 was 

correct in about 70% of the cases studied. The corresponding skill scores were -80 and WT respectively. Data 

derived on11 from infrared images yielded comct identification of 0 and (1 + 2) classes in 85% of the caxs and 

identification of classcs 0. I and 2 was corn in 658 of the cases. The corresponding skill scores were -658 

and 40% respectively. 

1. Introduction 

The latent heat released b) precipitation affects the 

heat budget of the atmosphere. Rainfall measurements 

are thus useful input to models of the eanh’s climate. 

Latent heat is also the main source of energy of hurricanes (see Anthes. 1982). Adler and Rodgers (1977) 

used satellite-borne microwave radiometn to measure 

the latent heat released in humcanes at large distances 

from shore based radars. That energy was directlj related to the Hind speed measurement in the humcanes. 

Such considerations have motivated studies on the 

measurement of precipitation from space. A recent 

NASA workshop addressed itself to various methods 

that employed microwave. infrared and visible satellite 

imagery (see Atlas and Thiele. 1981). 

Microwave radiances respond direct]) to hydrometeors: howeker. microwave data are available at 

twelve-hour intervals at best. While rainfall distribuSpace kience and Engineenng Center ’ Also affiliated with Central Meteorological Bureau, Beijing. Peo- 

** Department of Electncal and Computer Enpneenng 

ple’s Republic of China 

tions are not uniquely related to the appearance of 

clouds seen in visible and infrared imagery, rainfall 

estimates can be derived more frequently from operational geostationaq and sun synchronousl) orbiting 

meteorological satellites. Combining space borne measurements of precipitatior. that employ both methods 

could thus improve the temporal resolution of rainfall 

rate distributions as well as the accurac! of such determinations. 

Cloud observations in visible and infrared images 

obtained from satellites have been used to estimate 

rainfall rates over remote areas. The most widely employed techniques described by Barren and Martin 

( I98 I ) and b! Atlas and Thiele ( 198 1 ) obtain rainfall 

rates from regressions against area or rates of areal 

growth of cold clouds. Techniques such as the ones 

developed by Griffith er a/. 1978) require the analysis 

of sequential images which can be obtained from geostationary satellites. A somewhat different approach 

was developed by Follanske and Oliver ( 1975): that 

technique relied upon subjective cloud type identification in sample areas. Le.. the recognition of various 

cloud patterns by an experienced meteorologist. All of 

these rainfall estimation procedures are time consumC 1985 American Meteorological Society 
SEPTEMBER 1985 RONGZHANG WL'. JAMES A. UEINMAN AND ROLAND T. CHIS 315 

ing and labor intensive because the meteorologist is 

obliged to exercise careful judgement. 

Lovejoy and Austin (I 979) developed a pattern recognition technique that estimated rainfall from visible 

and infrared GOES satellite images. Their technique 

anal!zed the statistics of radiances in both images. 

hlore recently. Negri €1 al. ( 19841 developed a simplified and objective adaptation of the Griffith-M'oodle! 

technique. which avoids the need to trace the temporal 

de\ elopment of precipitating clouds. 

The present study developed a pattern recognition 

algorithm to derive rainfall rates from single visible 

and infrared images or infrared images onl!. The technique that we used was an outgrowth of the preliminary 

study of Lee and Chin (1983). This technique determines rainfall in 20 X 20 km grid elements on the basis 

of radiance and texture features. Radiance features are 

globai measurements of the 20 X 20 km grid elements 

containing information abut the overall characteristic 

of the radiances within the grid element. Texture features are measurements that concern the spatial distribution of the radiances. M'hen the grid contains radiances that var) little. the dominant properties ofthat 

grid are ?he radiance feature. M'hen the grid has a wide 

variety of radiances. the dominant propcny of that area 

is texture. The algorithm classifies rainfall into three 

classes that correspond to operational radar precipitation levels: O-no rzin (0 S R < 0.5 mm h-I): Ilight rain (0.5 I R < 5.0 mm h-I): and ?-heavy 

rain (5.0 mm h-' S R). The rainfall rates used in the 

training sets for the development of the algorithm and 

those used to test the algorithm were derived from a 

set of twent! nine plan position indicator (PPI) displays 

obtained from 50.4.A operational radars. A training 

set contains tw'o subsets: 1 i the set of images that characterizes tl'pical cloud?ain patterns and 2) a corresponding sei of ground truth to identif! patterns obsen.ed in the first subset. The training set is used to 

estimate the conditional probabilit! distributions of 

the features extracted from I ). from which the decision 

rule (the rule for the identification ofthe various c!asses) 

is constructed. Data were obtained from summer 

storms. tropical storms and cyclones in the Gulf of 

Mexico coastal repon and near south Florida. 

2. Data sets 

a. Siivellitc daia 

Data were obtained from the geostationan. satellites 

GOES-:! during the summer of 1978. ShlS--7 during 

the summer of 1379 and GOES-4 during the summer 

of 1983. The sensors on these satellites produced images 

in the visible spectrum. 0.55 < X < 0.70 pm. and in 

the thermal infrared. 10.5 < h < 12.6 pm. The spatial 

resolution or the visible Sensors was either I k 1 km 

or 2 X 2 km while that of the infrared sensors Ha5 I 

X P km. The satellite data sources are summarized in 

Table 1. The infrared pixels were quantized into 256 

gre! levels that were calibrated in terms of brightness 

temperature. The images in the visible spectrum were 

quantized into 64 gre? levels; these levels were multiplied by four to produce images that have a dynamic 

range that matches that in the infrared. 

Before commencing the pattern recognition analysis. 

the visible and infrared images were navigated with 

respect to terrestrial landmarks so that the radar images 

could be collocated. Yavigation problems were especially troublesome when correlations were sought between satellite and radar imageq of rapid]! developing 

cells that were either at the edges of larger systems or 

were completely isolated. The existence of rain was 

indicated but no attempt was made to categorize the 

intensity of the rainfall in such cases. 

Visible radiance le\-& were corrected to account for 

the effect of solar zenith angle on the illumination of 

the clouds by means of the empirical relationship de- 

\eloped b\ Raschke and Bandeen (1968). The satel!ite 

data cited in Table I were acquired from different satellites o\.er several years. The calibration of the \.isible 

sensors was known to have drifted (see Muench. 198 1 ): 

accordingl). images of U'hite Sands. New Mexico and 

bright clouds were used to calibrate the relative response of the visible channel. Conversion from digital 

counts to directional reflectivit? was achieved h\ the 

calibration method described by Smith et al. (1 98 1). 

The intrinsic reflectivities of White Sands and bright 

clouds are independent of time so that the variation 

in the apparent reflectivities could be determined. The 

relative radiance values in all of the images obtained 

from a given satellite were thus restored to an approximately consistent set of values. 

Another source of errors was the lack of uniform 

response of all of the sensors that comprised the visible 

imaging system. This effect could introduce streakiness 

in the image that could produce errors in the texture 

features. This effect will be described in the next section. 

%lost of the data in Table I were screened to minimize 

this effect over scenes of surfaces that were known to 

be uniform. 

Detailed analyses of satellite imager! revealed that 

the best results were obtained if the training and test 

data sets were obtained from the same satellite system. 

The reason for the incompatibility of data acquired 

from different satellites was not readily apparent: however. there are ample archkes of data from an\- given 

geostationan satellite so that we do not regard this as 

a significant problem. 

The 20 X 20 km grid elements that were used in this 

analysis were selected to match the resolution of the 

37 GHz channel of the Scanning Multifrequenc! Microwave Radiometer of Nimbus 7. The grid elements 

contained a suficient number of independent measurements in the infrared images to be statisticall! useful ( - 13): the situation w'as better in the visible images 

where each grid element contained at least one hundred 

pixels. 
316 JOURNAL OF ATMOSPHERIC AND OCEANIC TECH3OLOGI VOLCME 2 

T4BLE I. Surnman of data sources 

Julian 

Date da) 

28 August IV78 240 

25 Jul) 1979 206 

26 Jul! 19'9 207 

-7 Sepiember !979 246 

I?Jul! I979 I94 

6 Jul! 1979 I87 

I7 4ugust 1983 229 

Time 

(GMT) 

I.(o(I 

I930 

1730' 

1930 

2100 

I500 

1700*' 

1830.' 

I500 

1645 

I 800 

2030 

1030 

1800 

20Xl 

I900 

I6W* 

I '(". 

I sa>* '

1900' 

1930 

1530 

1800 

1830 

I900 

1800 

1900 

?Ooo 

2130 

Satellite 

GOES-: 

GOES-2 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SI\IS-' 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SMS-2 

SXIS-2 

SMS-2 

SMS-2 

SMS-2 

SMS2 

SMS-2 

SMS2 

GOES4 

GOES-4 

GOES-4 

GOES4 

Resolution Conirast 

VIS IR \'IS 1R 

High High 

High High 

LOW Medlou 

Lou MedIlou 

Lou Med/lou 

Lou Medllou 

Lou Medilou 

Lou bled,'lou 

Lou Lou 

Lou Loa 

Lou Loa 

Lou Lou 

Lou Lou 

Lou Lob 

Lou Lou 

Hi@ High 

High High 

High High 

H;gh H:gh med 

Lou Lou 

Lou Lou 

High High 

High High 

High High 

High High 

High High 

High High 

High High 

High High 

N eatner 

s>stem 

Tropical storm 

Tropical storm 

Tropical storm 

Tropical storm 

Tropica! storm 

Tropical storm 

Tropical storm 

Tropical siorm 

Humcane 

Humcane 

Humcane 

Humcane 

H umcane 

Tropi.al stam 

Tropica! siinn 

Tropical storm 

Th u nderstoims 

Thunderstorm 

Th:: nderstoThundersiom 

Thunderstorm 

Thunderstorm 

Thunderstorm 

Thunderstorm 

Th u nderstorm 

Humcane 

Humcane 

Humcane 

Hlrmcdnc 

Radar . station 

GLS 

GLS 

GLS 

GLS 

GLS 

GLS 

GLS 

GLS 

D.49 

DAB 

D4B 

DAB 

DAB 

SI1 

SI1 

SI L 

GLS 

GLS 

GLS 

GLS 

GLS 

GLS 

G LS 

GLS 

GLS 

GLS 

GLS 

GLS 

G LS 

Denotes training XIS used to represent catrgonec charactenzed b! spatial resolution and \isible trxlurai contrast A = [Xj More than 

one training se! uas used uhere *: or *' are designated 

Denotes uhere the infiared mean teitural fea:lrre. A = 18; restncis the numhen of infrared features 

I> Radar dara assigned the value tha? occupied the greatest fractional 

area. Ambiguities could arise around the edges of larger 

s!.stems w.here small precip,tation cells were localized and e\olving rapid]!. Isolated cells also had to 

be carefull! so that their 1ocati,7, in as properl? &.sighted among neighbonng grid e!ements The 

radar data could be displz\ed once it us djgitized. 

The anal!.sis of twent! 

satellite images nas on the L:ni,.ersit\ of 

U.isconsin hlan-computer Interacti\.e Data ,4cquisition 

S\-stem (MclD,4S,: (see Suom, (,, dl,, 19s31, - 

Rainfall rates here inferred from radar data that were 

obiained sirnultaneousl! w.ith the satrllite imagsn- (see 

Table 1 ). The radar data were obtained from National 

U'eather Sen.ice M'SR-57 units which haw IO cm 

ua~elengths and a 230 km scan radius. Rainfall rates. 

R rmm h- ' i. were computed from the mecisured radar 

refiecti\-ities. .Z (mm6 m-3). b! the relationship Z 

= 55Rl.": this relationship is in operational use for con- 

\.ecri\e precipitation. Complete PPI scans were obtained in men minutes. and these scans were obtained 

and 

\s.iihin fi\ e minutes of the corresponding satellite image$. Microfilm images of PPI display were projected 

on a transparent grid arral- with 20 >. 2@ km spacing. 

Outlines of rainfall rate contours were manuall! traced 

on the gnd and the effects of ground clutter were also 

removed. The rainfall rate classes and coordinates of 

the grid were then digitized. If a grid element w'as partiall! filled b!. t\so rain classes. the grid element was 

3. Analjsis 

u Tlic doL'isioti rirlc 

The maximum likelihood decision rule w'as used in 

this stud! to classif> the three rain classes denoted as 

u0. u,. and w2. A feature bector S = (.yI. s2: - -sk). 

characterizing the rainfall information in a grid element. was classified as class a, if 

ORIGINAL 

POOR QUALm 
p(.G1)P(w,) > p(Sju,)P(U: , ) . for all j # i. (1) 

u-here p(<!uI) is the conditional density function of X. 

and P(u:.,) is the u priori prohahilit) of Occurrence of 

rainfall class uI. In this stud! the probability of occurrence of all three rain classes were assumed to be the 

same. The conditional densit\ functions p(<[u,) i = 0. 

1. and 7 wre assumed to be normall\: distributed and 

their means and covariances are estimated from a set 

of training samples. The components of the pattern 

recognition process. as depicted in Fig. 1. are training 

and classification. The following problems were then 

addressed: 1) what features should be extracted from 

each _end element to give a good representation of the 

rainfall rates: and 2) how does one choose the proper 

training sets3 

L i x Ixt '45' - 

i 

[INFRARED 

FEATURE EXTRACTION 

VISIBLE 

:xlXt(Ij, I, & 

; x Ixt (1: 

I 

b Ft~i.: 21 r" c.\-rrucrion 

M'r emplo>ed 24 features from each of the visible 

and the infrared images to determine the \,anous rainfall classes: these radiance and textural features are 

summarized in Table 7. These features ha\-e ken more 

comprehensi\el> described by \Veszka et al. ( 19761 and 

Psribh (lY77). 

The radiance features are statistical parameters derived from all of the pixels in each 20 %. 20 km grid 

element. The! include the maximum. minimum. and 

mean of the radiance Ere! levels. 

One of the texture features that are used to determine 

radiance variations is the edge strength per unit area 

defined b) the area a\.eraged Roberts gradient 

ESTIMATION OF 

DECISl3N RULE 

DENSITY FONCTIOWS 

P:XIOJc? 

p(XllA 

p[xllr ?;: 

1 bl-d .V-d 

{ !I(Ix. n) - 1017 + d. n + d); 

+ il(m + d, nj - I(m. n i d)lj (2) 

where I(m. n) are the radiances at specified Cartesian 

coordinates 117 and n. and d is the number of pixels 

- separating the AI X 'V points. The Roberts gradients. 

RG are designated as k = [7] and [ 161 in Table 2. 

Grey level difference statistics were additional texture 

features that were emploqed in this study. These quantities depend on the histograms of the distributions of 

radiance difference. At&). If pairs of pixels are considered within a 20 X 20 km grid element the gre! level 

radiance difference is: 

(3) 

where both and 2 can assume values of either 0 or 

kd d is the distance separating the pixel pairs and H = arc tan(di2) = 0". 45". 90". I?" is the direction in 

which the pixel pairs are measured. The gre) diference 

g ma! assume \slues in the range 0. 1: - .35. 

The histograms h6&) of gre! lebel radiance differences in a grid element provide a measure of the scale 

size of the cloud features. For example. if a gnd element 

contains clouds a,hose horizontal dimensions are large 

and whose texture is smooth compared to the selected 

pisel separation d. then grey level differences g - 0 

will be high& probable. On the other hand. if clouds 

g = iI(I?L n) - I(/,? + 2. II + 8,;. 

DECISION RULE. [INFRARED FEATURE EXTRACTION I ! x!xt UNKNOWN CLASS 

VISIBLE 

I r -I 1 I 

TESTING SETS 

FIG 1 Schematic diagrrrn; of :he paitem recopniiron algxithm 
OWbGiNAL PAGE IS 

OF POOR QUALITY 

318 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOG)' VOLL'ME 2 

TABLE 2. Features' used to charactenze satellite images of clouds 

Feature 

number 

h Features 

Radtancc 

Mean gre) level 

Standard deLiation of gre? level 

Maximum prq lebel 

Minimum gre? level 

Maximumiminimum gre! lebel ratio 

Gre! lebel range 

7ex:urc 

Pixel separation d 

VIS IR 

-I 

7 

? 

Edge strength per unit area RG[ 71 - 4 

Maximum within 4 directions ME4N[F]. COS[9]. .4SU(lO]. ENT(1 I] - 4 

1'1 

IF;]-[ 1 I] 

!I?]-[ i 51 Mean of 4 directions - 5lE4N[ 121. CO?;[l?]. 4SM[l4]. ENT[15] - 4 

[I61 Edge strength per unir area RG[ 161 4 8 

I I ']-i20] Maiinurn within 4 dirmions MEAN[ 171. COY[ 181. 4Sali 191. EKT(2O) 4 8 

[' !]-[Z4] Mean of 4 directions hlE.4Yi21]. COY'j22j. 4SV[23]. E'CTi241 4 I; 

- 

4 total of 24 features were considered for infrared images onl) and 48 features were considered for infrared and wsible images. 

are small or if the! are inhomogeneous in appearance 

compared to the pixel separation. then a larger range 

of p is more probable in a given grid element. The 

histograms h,d(g) will thus be spread over a larger range 

of g as graininess or streakiness increase. Note that the 

texture ma! be streaky in a particular direction so that 

the histogram must be specified as a function of ande 

8 as well as d. 

Four features that characterize the shape of h,.&) 

are calculated: 

Mean: 

Contrast: 

4ngular second moment: 

E ntrop!,: 

where .V is the total number of pixel pairs in the grid 

element separated b! distance d and direction 0. 

The MEAN is the average of the gre? level radiance 

differences. Contrast. CON. is a measure of the local 

variation of radiances in a grid element. Large values 

of MEAN and CON in a given direction correspond 

to markedl! structured clouds such as cumulus with 

shadows. The maximum directional ME.rZN and COY 

are designated as k = [SI, [ 171 and k = [9]. [ 181. respectively. in Table 2. The mean of these quantities 

averaged over four directions are designated as k = [ 121. [2 I] and k = [ 1-71. [22] in Table 2. 

The angular second moment. .4SM. is a measure of 

the homogeneit! of gre! level differences over distances. 

d. and angles 8. A large ASM implies that although 

neighboring pixels ma! have different ere!' levels. the 

differences in these levels at neighboring pixels are 

similar over man! pairs of pixels in the grid element. 

It is high for cirrus or cumulonimbus an\.&. for decks 

of stratus. and for hmds of cloud5 oriented in the direction ofP. The 4Shl can depend on angle ifthe clouds 

are banded in a dominant direction. The maximum 

.4SM in a gi\m direction are designated as features k 

= [ IO] and [ 191 and the mean ASM over all directions 

are designated as i, = [ 141 and [27] in Table 2. 

Entrop!. EST. is a measure of the absence of a distinct scak of organization. h is a feature thar is a maximum w.hen all radiance differences g ha\e an equal 

probahilit! of occumng The EVT is low when the 

lexture is smooth and when it is highl) structured. This 

dual \,slued dependence on he,&) arises because €UT 

is small when h6.@! = 0 and a 1. The latter effect is 

contributed b! the logarithmic contribution to ENT. 

Again EST depends on direction for features X = [I I ] 

and [ZO] and it can be averaged o\er all directions in 

features k = [ 151 and [24]. 

The corrected visible and infrared images or the infrared images alone were processed on McID.4S. .A 

pre-processing step rejected clear grid elements in the 

\isible images that had maximum grq level intensities 

< 80. The 20 'x 30 km grid element5 containing clouds 

roughl! in the same stage were identified on the basis 

of values of the textural contrast feature k = [E]. 
Thi. radiance and texture features can be regarded 

,,' c(rr7,pcments ofa \'eCtor < = (.VI. .v:. s3. - * *.Y&) that 

.I ,!.\Ln+i. - CaCh 20 X 2@ hm grid element. The featfire , L.c,c,7$ nflp,e grid elements ?hat comprise a gi\en rain- 

(JIj C~SS~ in each scene can be described by a mean 

,.t.L;i7r = (-pi. p:. p;. . . . . p,.) and a variance vector 

6: = (c,I. (T2-. g3-. - * ui2j. assuming that i is normall\ 

t:!s!ri5~td and that all features are independent of each 

Bcc3usc these vectors can in principle ha\:e as many 

2q 4s components. not all of H hich contribute to the 

v-*;7?rarion of rainfall classes. the number of feaiures 

k,.,? 10 bt reduced. This was achieved b> a feature sei:;tlc~n procedure u hich ranhed the features in the orLIL- 0; the;; abilit! to separate the rainfall rate classes. 

7-c:~ v.2~ iniplemented b! a di\ergence that normalized 

:!::~ sc*;TaraiiOR of the means of the feature b! the cum 

,6 the \ ananccs. The di\ergence. for the feature .xA betwccn rainfall rate categories i and i' is: 

p: h c r. 

i # i'. (8) 

Di\ erpences were computed during the training 

phst for all pairs of rainfall rate classes. Those features 

fLlr which the di\ergences ranked highest were chosen 

tCl dir;rimina!e between the \.anous rainfall rate classes 

IF, the classification phase (see Swain and Davis. 19s:). 

Table 3 presents a summap ofthe number ofscenes 

::-. uhi<k \snous features were ranhed in the order of 

(i:*;rssing d:\ ergencr: for $:parating classes 0 and ! I 

- 21. 1.e.. ranA 1 w2s the most e!Teit;\e discriminating 

Ii.LiUx while ranh 9 was the leas1 Thus \be find that 

foi thc separation of Tar, and no rai~?. \.isibie feaiurs 

i 11 ranked foremost in eight scc'ilej. \,isible feature [-3] 

rcin'hec! foremost in nine scenes. \+&le feature 1131 

ranhcd foremost in eight scenes. etc. Similarl?. infrared 

-L..>~rc i?] ranhed first in i i scenes. etc. 

The \isible radiance features A = [I]. [3] and [I] 

um frrquentl! signifizani discriminating features: 

hmser. texlurt. fea:ures k = [ 1 I]. [ 141 and [ 151 wre 

d!s;' frequent!! signifixnt disxminatin: features 

U'hiie these ie\ture features affected the appearance of 

precipitating clouds. they ha\.e onl! been recognlzed 

sa@wti\el> in pre\.ious studie5 as mentioned in thc 

inlroduction. Texture features defined o\er large pixel 

separations. a' = 4. appeared to be wrlakl! related 10 

rainfall. This is consistent with the findings of J'au and 

Rogers ! 984). H hich suggest that most precipiming 

cr!ls are sma!ler than - IC) im. 

The infrared radiance features X = [I]. [I] and 141 

were frequentl! significant in identif! ing precipitating 

regions. [i.e.. those designated (1 - 2)]. Houwer. texture features X = [ 101. [ 1 '] and [ IS] were also signiii- 

.. 

cant. Here too. the texture features that were defined 

in terms of the smallest pixel separation were the most 

important. 

Separating rainfall classes 1 and 2 was most frequentl! achieved with features [I]. [3] and [3] in the 

\isible and in the infrared images. No texture features 

were consistent11 able to contribute to this preliminan. 

separation. Accordingly. the classification was conducted in tuo steps: the no-rain and rain separation 0. 

and (I + 2). used features that differed from those used 

to subsequent11 separate between classes 1 and 2. 

d. Fmwc propcrric.c aud rraining sets 

The visible and infrared contrast features [22] are 

shoun in Fig. 2 a5 functions of time. The points represent each class mean of these features for rainfall 

classes 0. I and 2. These two figures show that the 

fcature values changed with time and varied uith storm 

s!sterns. So simple threshold could be found to separate each c!ass for all cases. These figures show that 

clouds in different stages of convective activit! had different feature \ alues. For example. on da\. 1 Sb of 1979 

at 1600 GMT. clouds were in a grou-th stage and at 

i900 GMT Fig. 2a shows that the clouds were in a 

deca! stage. This implied that the separation of rainfall 

classes should be performed by taking different convective activit!- stages into account. 

The contras? is a measure of convective activity so 

that this feature should be correlated in the visible and 

infrared channels. Moreoker. large values of visible 

contrast indicate that small scale con\ecti\e activit? is 

strong as in the earl! stages of cumulus growzh. .4s the 

cumulus clouds mature. anvils and cirrus shields exhibit loner contrast. Contrast. rather than infrared 

bnghtness temperature. w'as selected as an initial 

screening feature because some tropical stoms cml? 

_en'\\ to modest altitudes so tha: radiance features which 

depend on brightness temperature could miss such 

c1;ruds. For example. the active thunderstorm shown 

in Fig. 2a has greater contrast than the stable tropical 

storm show in Fig 2d 4s a resu!t. feature [22] w'as 

used to di\.ide training sets into three cksses. i.e.. high. 

med!um and low contrast [:,'I. 

A \isible contrast that was apprec~ab!! greater than 

the infrared contrast was attributed to sireakiness in 

the \-isible image. Tne subsequent analysis of such an 

image uould on]? consider the \isibie features I, = [I]. 

['I. [4]. [SI. and [6] and all of the rele\an? infrared 

features. An example of such a case is shown in Fig. 

2c lier re the high visible contrast. CON[22] > 150. is 

cleari! inconsistent \vith the intermediate infrared 

contrast. This figure can be compared to Fig. 2b in 

w hich \.isible and infrared contrasts are similar in magnitude. 

It \#.as generall! preferable to utilize the visible contrast after its \erisimili?ude was determined because 

thc spatial resolution of the visible channels was greater 
320 

TABLE 3 Xumber of images in uhich pattern recognition feaiurc [!I] uerr ranled 

betueen I and 9 for rainfall classification (01 no rain and ( 1 + 2) rain 

Rank 

4 - features A 1 - 3 4 5 6 , 8 9 

Radiance 

Mean Gre? Level 

Max. Gre? Level 

Min. Gre! Level 

[I1 

[31 

141 

3 

I 

1 

I 

I 

4 

Texture Pixel 

features separation 

- -7 RG - hlax. MEAN 1 

Max. 4531 - 

Max. EN1 - 

\!e3n. MEAS - 

hlecln. COX - 

\lean. 4SM * 

Mean. EN1 - 

1 

7 

7 

, 

4 

-7 

9 

1 4 

I 

9 4 , 1 , 

I 

% 

z 

1 1 , ! - 

I 

1 

7 

- 

RG 4 

Max. ASM 4 

Max. EST 4 

Mcan. ME 4N 4 

Mean. ASS1 4 

Mean. ENT 4 

I 

I - -7 3 I -7 7 - - 

4 

I 

4 4 

3 

Radiance 

features 

Mean Gre) Lebel 1 

Std De\iation 

h1ax Grz! Lmel 

Min Gre! Letel 

Max Slifi Ratio 

Gre? Lexel Range 

1 

1 

Textural 

features - 

RG 

Max ME\\ 

Vax CO2 

hlzx \S\1 

Max E\T 

Mean \I€\\ 

Heir COX 

Wan 4S\f 

\lean EIT 

RG 

hlax ME42 

Max co\ 

Max 4SM 

\lax E\T 

Mean ME42. 

Mean CON 

- 

hlCdn 

Pixel 

separation 

4 

4 

4 

4 

4 

4 

A 

4 

4 

8 

8 

P 

P 

F 

8 

8 

a 

I 

3 

I 

3 

I 

i 

7 

1 1 

hlean ENT 8 i24i I 1 

than the resolution ofthe infrared images and the classification divergence was greater (see Section 3c). 

The different resolution of the visible image alkcts 

some feature values. For example. on da\ 186. all images have a resolution of 1 X 1 km. w-hereas on day 

246 of 1979. tho resolution \vas 1 >. 1 km at 1930 
32 1 

"UT I c CLASS 1 

+ CLASS 2 

L - 

c c 

:80: 153c k 

1 

160C 1 

I 

2000 

22Ct 

I 

1832 

200'. 

2200 

T? ' 1 ! 

,/ 

The decision tree shown schematic all^ in Fig. 3b 

was used to separate the rainfall classes. 11 is described 

as follo\vs: 

The anal!.sis of an image commenced b! checking 

the visible contrast and the resolution of the test set to 

decide H hich contrast catego? applies to the test set. 

The visihle or infrared mean intensit! gre! le\.el k 

= [ 1 J n'as then used to determine which trainins set 

should be chosen inside the given contrast category. 

The visible mean intensit! ere! lebel was preferable to 

the corresponding infrared feature pro\.ided that visible 

data were a\.ailable. The decision rule determined by 

the chosen training se? was then used to discriminate 

between the no rain. 0. and the rain. (1 t 2) classes. 

In the cases tested. the separation \\'as achieved a.ith 

\.isible feaiures [I]. [-3j. i41. [I?]. I!?]. [ !9]. 1231 and 

[24; and \iith infrared features [:I. [-3j. 141. [IO]. [13]. 

and [ 151 (see Table 3). It was noted thai ?he infrared 

mean relture feature X. = [8] was highl! \.anable as 

storms de~eloped. If this texture feature was markedl! 

different from the infrared mean texture feature of all 

of the mining sets. rhsn oni! ihe infrared intensit! 

features I, = [I]. [-?I. and [a] and all of the \.isible features were used to discriminate between rain and no 

TRANINC, SETS 

I 

nlGn 

?T (22) CONTRAST (221 CONTRAST (22) 

TEST SET r RAIN SAMPLES 3 C.ASS !I*?; 

r 

d (22; 3ETERUINE 

3NrRAST CATEGOR 

1 VIS 'I OR IE) I1 f 

' DE 'E RYlNE TFAINIYS 1 SE ANC HE 

COfiRE SWW3NG 

[ DEClSlOh 9JLE 

NS RAIN SAMPLES 

I I 3EClSl3h RLlLZ 
322 JOCRSAL OF 4TMOSPHERlC 41D OCEATIC TECHhOLOGl VOLt'HE 2 

T~BLE 4 Erect of radiance and texlure feature., on classificauon of gnd elmwntr. ['Kc rain (0) rain I I * 2)] 

Class St31!SIlCS 

Feawres used for \'is:[ I]. 13). 141: Yis:[l]. [1].[4]. [14]. [IC]. 1191. (23). 1241 

separation' \'is:[ I]. IR:[ I] IR:[ I). (31. [4] IR:[l]. 131. 14). [IO]. [14]. [IS] 

Resolution (hrn I** Biu>*'* [tit, rain !O ra:n il a 2,] 

1x1 

2x2 

1.04 2 0.07 

0.84 f 0.14 

1.03 z 0.07 

0.89 i 0. I3 

81 2 I 

PO 2 5 

84 2 I 

78 f 5 

l.00 = 0.02 

0.96 -c 0.05 

9-41 I 

92 f 3 

SA/// (Lor(**** I%] 

l>l 502 13 51 z 8 hc I2 

2r2 6@= IO 54 I9 HI =4 

Carego? \IS contrast. C0\[22] Lou 

*' No of image pairs (I X I 1 hm resolution 3 

lo oOrnage pairs (2 > 2) hm resolution 4 

**' Panofshb and Bner ( 1966) 

rain classes. Othewise. all of the features were used. 

The reason for this decision is that the infrared largescale texture features vary greatly as clouds develop: 

hou,ever. that variation appcars to bear little direct relationship to rainfall. The cases where this criterion 

was invoked are designated bc a plus in Table 1. 

.4fter determining that each grid element was either 

in the "nonraining" (0) or the "raining" ( 1 + -3) classes. 

those grid elements in which rain was falling were subsequentl! divided into classes 1 or 2. The features used 

for this second classification were the visible and infrared radiance mean. maximum and minimum grey 

levels. X. = [I]. [3] and [4]. The feature (41 for visible 

or infrared was used to decide which training set should 

be used. 

This pattern recognition algorithm was performed 

on a Hams-800 computer. The computer time required 

to analyze one test set was about two minutes. 

4. Rainfall classification results 

The effect of different features on the abilit! to sep 

arate the various classes was first investigated. Table 3 

shows the separation between the rainfal! classes. "rain" 

(1 + 2). and "no rain" (0). obtained h) applying the 

pattern recognition algorithm to visible and infrared 

images using different festures. The features that were 

used to separate classes 0 and (1 + 2) are shown. This 

table illustrates the relathe importance of radiance and . texture features. Note that the first two columns summarize results obtained when onl? radiance features 

such as the mean [I]. the maximum [3] and minimum 

[4] radiance gre). levels were considered. The third column shows the effect of including numerous additional 

texture features in the anal!sis. The performance of 

the rainfall retrieval algorithm is ebaluated b! tabulations of the bias. the percentage of correctl! classified 

grid elements and the skill score percentage (see Panofsk! and Brier. 1968). 

It is note\sorth! that the addition of eight texture 

fearures in the anal!,sis improbes the skill score b! more 

T4BLE 5 Summan of rainfali dassificauon 

from infrared iriages oni! ' 

Lou " 

High'" 

LOU 

High 

ht. z 6 

Hf f 7 

61 f 3 

64 = 10 

SAili w,)re* [e 1 

LOU c- z 10 41 2 4 

H igh 6t> I b 43- 13 

Panofsk? and Bner (1968;. 

** 1.R. fea:ures A = [I]. 131. Ilj. lyj. [&I. [IO]. [Ill. [12]. 1141. 

*I* 1 R. features A = [I j. [ij. [4] sere then used to di\ide the rain 

11 5; were used to scparaie rain or no rain classes. 

ciass (1 4 2J into classe!. i or 2 

' 1.R resolution: 4 F hm. 

No. of Images. I I 

t+t No of images 4. 

*. 
SENEMBER 1981 ROSGZHANG "L. JASIES 4 N'EISh14N .AND ROLAND T. CHIN 3'3 

than 27 percentage points. In as much as most previousl> used rainfall estimation algorithms depended 

onl1. on variations of radiance features. it is encouragjng 

to pursue the use of textural informatioxi in this analysis. There is also an indication that visible images with 

1 1 km resolution provide somew hat better estimates 

of the occurrance of rain than the visible images with 

2 X 2 km resolution. 

Table 5 presents the classification of rainfall classes 

0 and ( I + 2) in the left-hand column and 0. 1. and 2 

in the right-hand column. This analysis was conducted 

with infrared data only: it therefore simulates the abiliry 

of the present algorithm to classify rainfall rates from 

GOES data at night. The contrast texture feature [XI 

does not appear to affect the skill score. The skill score 

for classification between no rain grid elements in class 

0 and those with rain in (1 + 2) is 67%: this compares 

somehhat unfa\-orabl> with the skill score of 855 ob 

tained from visible and infrared data shown in the most 

right-hand column of Table 4. It should be noted that 

the 4 x 8 Lm resolution of the infrared sensor onl! 

rewals large scale convection while the visible sensor 

can detect smaller scale convective activity. The classification betwen rainfall classes 1 and 2 is ob;ained 

with radiance feaiures [I]. 131 and [4] and the skill 

score for separation into three rainfall rate classes diminishes to 445. It is encouraging that no obvious bias 

is evident in this classification scheme. 

Table 6 presents the classification of rainfall classes 

0 and ( 1 + 2) in the left column and into classes 0. 1 

and 2 in the right column when both visible and infrzircd images are anal>zed. The contrasr texture feature 

[22] appears 10 affect the skill score siightlF: the low 

contrast cases appear to have skill scores about 5% 

higher than the high contrast cases. However. the visible 

images with 1 X 1 km resolution appear to provide 

consistent11 higher skill scores than those with the lower 

resolution. The classification into classes 0. 1 and 2 is 

achieved with a skill score of 65% which is better than 

the 44% skill score that was provided b\ the infrared 

data onl). No obvious bias is evident in this data. 

The abiiit! ofthe algorithm to pro\,ide objecti\e disp1aJ.s ofrainfall distributions is shown in Figs. 4. 5 and 

6. These figures shou results obtained from thunderstorms. a tropica! storm and a humcane. respecti\,el!. 

Figures 4. 5 and 6a. 0 show the visible and infrared 

images from which the rainfall estimates were derived. 

Figures 4. 5 and 6c. d show the rainfall distributions 

denled from the present algorithm and those indicated 

b> the NOX.4 radars respecti\.el> . The gre! scale cding 

in Fig. 6c. d is white = no data. light grey = 0. grey 

= 1. dark grey = I or 2. and black = 2: the cross inTABLE 6. Summan of rainfall clvsification from visible and infrared images 

Rainfall rate class 

Class statistics 

0 6r (I * 2)- 0. 1 6L 2*** 

il 

F 

V si hi!i t> Contrast. 

Coyx) 

Hi& 

High 

Lou 

Lou 

High 

HI& 

Lou 

Lou 

Resolution (hm) Bid;* [ncs ruinfO rain 'I * 2;j 

'Is' A !, I.R.: 4 Y 8 VIS: 2 x - 

1.00 1 0.14 

l.M! 1 0.1 I 

I 00 2 0.02 

0 96 f 0.04 

"lS' I ' 

VIS. 2 x 2 1.R.. 4 \ 8 

'Is' I 

VIS. 2 x 2 I.R.: 4 x 8 

VIS IX IlR 4r8 

VIS 2 x 2 

'Is '"IR. 4x8 VIS 2 r 2 

9; I.; 

90 2 3 

94 I I 

92 5 3 

Skill score. 1% ] 

82 I5 

74 i 4 

85 2 2 

80 1 1 

70 2 12 

41 2 I 

61 14 

58 2 7 

' Panafsh! ana Brier I 1968 1. 

** Separaiior. into rainfall class "no rain". (0). or "rain". (I - 2). for high iontrasi cases were achie\ed uith \'IS I, = [I]. 13j. 1-41. ['I. 

Sepiration into rainfall class "no rain". (0). or "rain". (I + 2). for low contrast cases was achie\ed with VIS I, = [I]. [3]. [4]. [ 141. [IS]. 

**. Subsequent separation of "rain" into classes (1 ) or (2) was achieved with \'IS I, = [I I. [!I. [a]. and 1.R. k = [I]. [3]. [4]. 

[8). [IO]. !!I]. [Ir]. [14]. [15j. [19].[2?]. j24)and I.R. A = [1].[3].[4]. [IO]. [14j. [IS] 

[19].12?].['4]an3i.R.I;= [1].[3].[4].[10].[14].(15j. 
324 

ORI(I!MAL PAGE IS 

OF POOR QUALITY 

JOURNAL OF 2TMOSPIIERIC AND OCEANIC TECHNOLOGY VOLUW 2 

FIG. 1. (a) Visible huge ohtained from the SXlS-2 wtellire on 5 July 1979 at 1700 GhlT which shous 

thundentorms near Galbeston. TX. (b) .As in 4(a) except !hat the infrared image is shown. 

dicates the location of radar and the dashed lines delineate the coasts. While the details of the rain distributionsdiffer in the latter two figures. the broad features 

arc' similar. It may in fact be argued that the elevation 

ansle Of the mdar introduces a bias against heavy rain 

obserx'ations at large distances from the radar. For examplr.. the satellite image (Fig. Sa) suggests that the 

center of the tropical storm was more than 100 km 

south\\est of the Galveston radar. The satellite derived 

rains Are indicated as class 2 to the southwest of the 
FIG. 3 IC) Dcn\ed rainfal! caiego? distnbutior.. uhite = no data. !igh: grc! = 0. gr?! = ! and hlach 

= 2, dark gre! = I or 2 id) Radai image ohtain-d from the Gal\eston radar The grc:, scale IC rht same a> 

for ~ICI. 
FIG. 5. (a) Visible image obtained from the SMS-2 satellite on ZS July 1979 at 1930 GMT which shows 

a tropical storm near Galveston. TX. (b) As in 5(a) except that the infrared image is shown. 

radar while the radar designates them as class 1. A yet the radar suggests that the rainfall diminishes to 

similar effect is evident in Fig. 6c. d where the eye of class 0 at the bottom of Fig. 6d. 

Hurricane Debbie is to the south of Daytona Beach Discrepancies occur most frequently at the edges of 
GHixwb PAGE IS 

OF POOR QUALITY 

SEPTEMBER 1985 ROSGZH4NC N C. JAMES 9 N ElShM4N .4\D ROLAhD T CHIS 

CLASS \ 

11 

-- 

FIG 5 IC) Demed ramfall caregon d~stnhui~on uhte = no data liphi gre! = 0. gre! = I and blach 

= 2 id I Radar mag obtained from the Gaheaon radai The gre! scale IS the same as for 5rcj 

327 

the larger precipitating s\stems and near isolated cells. 

H hers ambiguities in assigning radar-deri\ed rainfall 

rates to grid elements here the greatest because of na\- 

igation problems. It Has on]! possible to indicate that 

rain Has falhng on some isolated or edge grid elements 

because no training sets that adequatel? dwriminated 
328 

~~ 

ORIGINAL PAGE fS 

OF POOR QUALITY 

JOURNAL OF ATMOSPHtRIC AND OCEANIC TECHSOLOGY VOLUME 2 

FIG. 6. (a) Visible image obtained from the SIVIS-? satellite on 3 Scptembrr 1979 at 1800 GMT which 

shows Hurricane Debbie near Da)tona Beach. FL. (b) As in 6(a) except th31 the infrared image is shown. 

benveen rainfall rates were derived for such isolated 

cells. 

5. Summary 

This study has shown that single pairs of visible and 

intixed geostationaq~ satellite images can be objectively analyzed with pattern recognition techniques to 

determine rainfall rates in three operational radar 

rainfall classes. It also showed that radiance features 

provide some separation of rainfall classes in given 

grids; however. a greater skill score was obtained when 

texture features were incorporated into the analysis. 

Specifically, the addition of texture features along with 
SEP;EVBER!~~! ROZGZH4SG NL. J4MES 4 NEI\M4\ AXD ROL4\D T CHIS 3’9 

PAGE 1s 

QUALITY 

FIG. 6. (c) Decved rainfall categor) dk1ribu1io~. white = nn data. light ere! = 0. ere! = 1 and black 

= 2. rdi Radar image ohained from the Da!iona Reach radar. The ere! scale 1s the sane as for hic;. 
330 JOCRNAL OF ATMOSPHERIC 

radiance features raised the skill score in identifying 

rain from 56 to 83%. Moreover. the classification into 

three classes could be achieved with a skill score of 

-66%. The best .results were obtained from images 

with high spatial resolution which re\ealed texture on 

a scale of a few kilometers. 

The results of this stud! are still preliminar).. More 

work is required to determine hoH well the rain rate 

classification technique works in other regions and at 

other seasons. Better pattern recognition algorithms 

ma! be developed as more experience is gained. 

,qcX-no\r,icdg??icnl5. We wish to thank Ms. Bonita Lee 

for pio\.iding us with the algorithm that she employed 

in her rainfall study. Dr. D. u'. Manin for pro\.iding 

meteorological insights regarding the satellite imagen. 

Dr. R. M'. Spencer for pro\,iding some ofthe radar data 

used in this stud!. and Messrs. M. R. Howland and 

D. .4. Santek for providing assistance in the use of the 

McIDAS. 

Suppon for this research was ohtainzd from National 

Science Foundation Grant ATM 82 19414. 

REFEREKCES 

Adler. R. F.. and E. B Rodgen. l97-: Satellite observed lateni heat 

release in a tropizai c!clone. -\fun I4e~ Ret. 105. 956-962 

.4nihes. R. A.. 1982: 7-rciprc.a; C:,:c-la~i Their Eiilurrnn. Siruciurc 

ad E%c.ic.\ .iIcienr .21oncgr.. 50. 19. Amer. Meteor. Soc.. 208 

PP. 

4tlas. D.. and 0. 'A'. Thiele. Eds.. 1981: Precipitation measurements 

from space. UorLshop Rep.. Goddard Space High: Center. 

Greenbelt. MD. 3.58 pp 

Barerr. E. C.. and D 'A'. Sfartin. I98 I. Tlit- i'sc c!'Saielliic. DGIO in 

RoicfG!! .\fon!!oring .Academic Pres. 340 pp 

Fcilanshee. W' 4.. and V .I Oli\er. i3-5. A conipanwn oflnfrared 

indeer! and \ideo pictures in !he estimatix of dail! rainfal! 

AND OCEANIC TECHbOLOGI' VOLUME 2 

from saiellite data. Y0.4.NNESS Tech. Memo. NESS 62. 14 

PP 

Gnffith. C. G.. W. L. Woondle!. P. G Gruhe. D. 'A'. Manin. J. Stout 

and D. S. Sihdar. 1976 Rain estimation from geosynchronous 

saielliie imagen-visible and infrared studies. .Won Li cu Rm.. 

106. 113-1 171. 

Ler. B G.. and R T Chin. 19F3: Classification of rain cells in satellite 

imagen. IEEE Con: or? Crirnyuicr 1'rTic)n c;nd Pa:icrn Recopnr:it)n Washington. DC. 405406. 

Lo\ejo!. S.. and G. L. Ausiin. 1979. The delineation of rain areas 

from visible and IR satellite data for GATE and mid-latitudes. 

.4mios Otcpan. 17. 11-92. 

Muench. H S.. 1981: Calibiation of posbnchronous satellite video 

sensors. AFGL-TR-8 I -0@50. 4ir Force Geophysics Labraton. 

Hanscom A.F.B.. MA. 25 pp. 

Negn. A J.. R. F. .Adler and P. J. H'etzel. 1984: Rain estimation 

from satellites An examination of the GnRth-Wocdle! technique. J Clitnzrc .+pi .Iferror 23. 102-1 16. 

Panofsh!. H. .A,. and G W. Bner. 1968: Sim~ .-lppl/catron? d.90- 

iiiirc.5 IP .\fivcoroiogi Penns!l\ania State Cni\ersit!. 224 pp. 

Panhh. 1.. 1977: A compara:i\e s!ud! of cloud clzssihcation iechniques. RCVIO~C SL'PI.! ~til'l~Ot1 6. 67-8 I. 

Raszhi,~. E.. and W' R hndtxn. 1966 Bzobrlihtung-n dcr reflcxims 

eigmschaften des s!'sirms erdc-aimcsphirr und de: heuol'hung 

iikr den aqualorialen Pazifih \'on cinern s> nchronen atelliten 

aus. .inn .\feicor.. 4. 200-205. 

Smith. W. 1.. 1. D Herman. T. Schreiner. H B. Howell and P. 

blenzel. 198 ! Radiation budge! charactenstics of the onsei of 

ihr Summer hlC1il5~3On /,I! Con: on hrii Rc,5idi.i (*' Fbbt 

andLorpc S~aic .tspc.c':.< o!i/it' ,\ffin.<tw €.ypcrimm! Tallah=- 

see. (6-16!-(6-26\ pp. 

Suumi. \ E.. R. J Fox. S S Lima?? and W 1. Smith. 19?3 McID-ZS 

Ill: ,A maderr interazine data access and anal!.sis system. J 

Climuit .4ppi .lfrrcor 22. 766--:8. 

Swain. P. H.. and S. AI. Davis. 1962. Rrniorc Si'rising Thr Qunnii- 

;uri~c' .4,~prc~nc 11 McGrau-Hill. ?96 pp. 

W'eszha. 1 S . C. R Dyer and A Rosenfeld. 19%: A comparative 

stud! ortexture meawres far ierrain clasification I€€€ Triim 

I'Au. \I. K . and R R Rogsn. 1484 4n in;ersion probiem on infi+ng in: SIX d!s:nSu:i~n of precipiratio:! are= from rdin-uuge 

mcasurtnent5 J VI,;:, k, , 11. 4?~-44- 

Si.>: .\J.L?: C j ,!h,rr. S\lCI. 260-26 

ORIGINAL PAGE IS 

OF POOR QUALITY 
2 

AUTOMATED RECOGNITION OF OCEANIC CLOUD PATTERNS. PART 1: 

METHODOLOGY AND APPLICATION TO CLOUD CLIMATOLOGY. 

by 

LOUIS GARAND 

Recherche en Prevision Numerique. 

Dorval, P. of Quebec, H9PlJ3 

January 1987 
ABSTRACT 

A scheme is presented for the automated classification of oceanic 

cloud patterns. - The twenty classes reflect the rich variety of 

morphologies that are detectable from space. A training set is defined 

by 2000 samples-of size 128 X128 km taken over the Western Atlantic in 

February 1984. The method uses visible and infrared images from a 

geostationary satellite. Class discrimination is obtained from 

thirteen features representing height, albedo, shape and multilayering 

characteristics of the cloud fields. Two features derived from the 

two-dimensional power spectrum of the visible images proved essential 

for the detection of directional patterns (cloud "streets", rolls) and 

open cells. Based on the assumption of multinormal distributions of 

the features, a simple classification algorithm is developed. From 

1020 independent samples, the consensus of two out of three expert 

nephanalysts agreeing or not with the machine result suggests an 

overall accuracy of 79% with the machine answer at least second best 

89% of the time. The cloud climatology in twenty classes for January 

and February 1984 are compared. Retrieved cloud fraction maps are also 

compared with the observed fields from ships. _The scheme is proposed 

as a candidate for the International Satellite Cloud Climatology 

Project . 

: 
1 

1. INTRODUCTION 

In this first of two articles, the design of an automated and 

efficient cloud classification scheme is presented. The twenty classes 

reflect the rich variety of morphologies that are detectable from 

space. The cloud patterns are quantified with measures extracted from 

visible and infrared images. These measures, or features, describe the 

cloud fraction, height, albedo, shape and multilayering characteristics 

of the cloud fields. The vector of features permits an objective 

classification. Class-dependent relationships between cloud features 

and meteorological parameters of interest are developed in the second 

Various cloud classification schemes have been proposed in the 

past. Scientists responsible for the International Satellite Cloud 

Climatology Project (ISCCP, see Rossow et al., 1985). have conducted a 

comparison between the existing techniques in order to decide which one 

should be used for a global five year climatology. One of their 

conclusions is that "all methods, which work well for some cloud 

types or climate regions, do poorly for other situations". The number 

of classes for most algorithms is notably low. The cloud fields or 

individual pixels are typically assigned one of three height classes: 

low, middle and high. The ISCCP requirements presently include a fourth 

class: deep convective. In addition, cloud optical depth and cloud size 

distribution are also mentioned as desired parameters. This is an 

implied admission that a more detailed classification, that includes 

morphological considerations, is desirable. 
2 

A notable exception with regard to the number of classes is the 

work of Shenk et al. (1976) who designed a 10-class scheme. Their fourchannel method was successful in detecting cirrus and multilayered 

systems but the coarse resolution, 55 km at nadir, did not allow the 

detection of much morphological detail in boundary layer cloud fields. 

Parikh (1976) presented a review of various cloud classification 

techniques. Basic concepts on tree structures, extraction of features 

and the use of discriminant functions applied to the cloud classification problem are described. Among the new approaches that have been 

tried, clustering in multispectral space (Desbois et al, 1982) is one 

that has shown skill in segmenting cloud pictures into homogeneous 

areas associated with cumulonimbus, cirrus or mixed cloud types. A 

drawback of that method however, and of pixel by pixel methods in 

general, is that by nature they are not suited for the detection of 

mesoscale cloud patterns. The differentiation between open versus 

closed cells, cloud "streets" versus ordinary cumulus fields or 

stratus versus stratocumulus cannot be achieved satisfactorily from 

scrutinizing multispectral histograms. The effect of cloud shape on 

visible-infrared signatures (Platt, 1983, Schmetz, 1984) has been 

investigated with limited practical success. For realistic cloud 

-\ 

fields, multilayered systems in particular, several possible interpretations of the signatures is the rule rather than the exception. The 

basic problem of discriminating between broken cloud fields and overcast ones with variable optical depth still remains. In short, for the 

purpose of detailed cloud classification, it appears that a set of 
3 

features specifically designed to 'see' the morphology is required. 

Such features are in the realm of pattern recognition in the literal 

sense of the term. 

The work of Rochard (1978) and Brard (1980) represents a rare 

effort aimed at the recognition of cloud field morphologies in detail. 

Their scheme discriminates 13 classes from 8 features. The boundary 

layer classes included open and closed cells, stratus and cumulus. 

Directional patterns were however not considered. 

Preferably, the scale of the analysis should be compatible with 

numerical weather prediction models or general circulation models. The 

ideal scale for cloud pattern analysis, as it turns out, is similar to 

that of those models. In their work on image modeling of cloud fields, 

Garand and Weinman (1986), found that scale to be between 100 and 250 

km. Such a scale (mesoscale) is large enough for the patterns to be 

recognizable, the texture primitive of the patterns being of smaller 

dimensions, yet small enough to insure a high probability of honogeneity of the cloud pattern and its associated weather conditions. 

One incentive to proceed with the development of this classification scheme was the realization from the aforementioned work, that two 

simple power spectrum features provided reliable means to detect directionality and therefore cloud streets and rolls and to detect, in 

addition, the presence of open cellular patterns. It remained to be 

shown however that these features could permit the discrimination of 

mesoscale cellular patterns from all other cloud types. A general cloud 

classifier was therefore developed. 
4 

This study is limited to oceanic regions only. We limit our 

attention to the winter season in the Northern Atlantic. We make use 

of a large data set covering an area of 20 degrees latitude by 20 

degrees longitude for the sixty days of January and February 1984. 

Chapter 2 defines the twenty classes to be recognized. In Chapter 3, 

following the description of the features for classification, the 

pattern recognition algorithm is presented along with the results on a 

training set of 2000 samples and on an independent test set of 1020 

samples. Monthly and seasonal maps of cloud fraction are shown in 

Chapter 4 along with a comparison between satellite-derived and 

observed fields (from ships) of total cloud fraction. Also, the climatologies in twenty cloud classes for January and February 1984 are 

compared. 

2. DEFINITION OF THE CLOUD CLASSES 

The nominal scale of analysis employed in this study is 128 X 

128 km; the nominal resolution of the visible sensor is 1 km. Power 

spectrum analysis, using fast Fourier transforms, is more efficient 

with sizes that are powers of two: this is the reason for choosing the 

particular number 128. This scale is too small to assign synoptic scale 

classes such as cyclone vortices or fronts but it is large enough to 

include several multilayered classes. With the inclusion of these 

multilayered classes, there is actually no need to search for homogeneous areas and the analysis can be performed on a fixed grid of 
5 

contiguous areas. Also we emphasize that the classes are defined from 

the viewpoint of a geostationary satellite. Thus, when compared to 

ground observations, both the scale and the classes may be different. 

For example, ground observers cannot report open cells or cloud 

streets, two cloud types that are among those investigated. Invoking 

this argument, human interpretation of the satellite pictures rather 

than shipboard observations of cloud type will serve as "truth". Three 

expert nephanalysts were assigned to the verification of the classification scheme on independent samples. The definition of the classes 

and the assignment of a class number to the training set samples were 

accomplished by the author. 

The classes were first selected by experience from a series 

of images: the samples of each scene were labeled into as many 

classes as the cloud fields could be discriminated. The analyst (the 

author) sought the mesoscale cellular patterns mentioned above, the 

discrimination between cumulus and statocumulus, the detection of thin 

cirrus, multilayered systems and cumulonimbus clouds. Once a total of 

20 classes was reached, the analyst felt hard pressed to define more 

classes without encountering a large number of ambiguous cases. The 

thresholds in height and albedo were defined during the analysis 

process.' A formal definition of the classes implies the description of 

the discriminating functions. At this stage of the presentation 

however, in order to clarify the goal of this study, it is appropriate 

to describe the various morphologies to be recognized in words and 

images, rather than by equations. The number ordering from from 1 to 
6 

20 closely follows the mean cloud top height from lowest to highest. 

Class names are generic, except for mesoscale cellular convection 

classes 7 to 11. The names streets, rolls, open and closed cells are, 

however, pervasive in meteorological literature, occasionally mentioned 

by name in cloud atlases such as those of Scorer (1972) or the World 

Meteorological Organization (WMO, 1969), although these books are 

concerned with cloud patterns as seen from the ground. Scorer's (1986) 

book on cloud patterns as seen from satellite is closer to the needs of 

this study. An entire chapter (six) is devoted to mesoscale cellular 

patterns: these cloud patterns were also the scientific object of two 

recent field experiments, the Air Mass Transformation Experiment 

(AMTEX, 1975, East China Sea region, see Sheu and Agee (1977)) and the 

Convection and Turbulence Experiment (KONTUR, 1981, North Sea region, 

see Bakan (1985), Hoeber (1982)). 

In the proposed classification scheme, the generic name 

cirrus corresponds to any cirriform class (cirrus, cirrostratus, cirrocumulus) and the generic name altocumulus corresponds to either 

altocumulus or altostratus. Cumulus, stratocumulus, stratus, 

nimbostratus and cumulonimbus are, however, recognized specifically. 

The abreviations VIS and IR are used to designate the visible and 

infrared pictures, respectively. The algorithms for the estimation of 

cloud fraction, height and albedo are described in Chapter 3. Fig. 2.1 

provides one example for each class of visible-infrared pairs. The 

reader is invited to examine these examples while reading the 

description of the cloud.classes. 
7 

Class 1. CLEAR 

The cloud fraction is less than 1%. When clouds are present at such a 

low cloud fraction, they are usually small cumuli. VIS: dark. IR: 

dark. 

Class 2. STRATUS 

Low cloud deck with smooth surface, usually one cloud entity without 

holes. 

VIS: Albedo typically moderately bright (50 to 80%). 

IR: Quite dark, sometimes clouds are not discernable from background. 

Mean height is 1.4 km and does not exceed 2.5 km. Above that height, 

this morphology belongs to class 12 (nimbostratus). 

Class 3. SCATTERED CUMULUS 

Low clouds with grainy texture. Cloud cover less than 50%. 

VIS: Albedo relatively low, 30 to 45% although the middle of the cloud 

elements may be bright. The individual clouds are usually small and 

numerous. 

IR: Dark or grey, depending on height, and fairly uniform. The typical 

height is 1.8 km and can reach 3.5 km. 

Class 4. BROKEN CUMULUS 

Very grainy texture with cloud fraction higher than 50%. The clouds 

seem packed against one another resulting in numerous small background 

areas. 

VIS: Grey to semi-bright, mean albedo 43%. 
8 

1.R: Grey, fairly uniform except for some towering cumulus not exceeding 

5 km. Typical height: 2.5 km. 

Class 5. SCATTERED STRATOCUMULUS 

Low cloud deck with cloud fraction less than 50%. The clouds tend to 

be connected in sizeable blobs so that most of the cloud fraction is 

made up of a few clouds only, as opposed to the cumulus class. 

VIS: Average brightness 40%. When brighter than 60%. the class becomes 

11 (bright closed cells). 

IR: Dark to grey. Mean height of 2.4 kin, not exceeding 3.5 km. 

Class 6. BROKEN TO OVERCAST STRATOCUMULUS 

Same as class 5 but with cloud fraction higher than 50%. 

VIS: Average brightness of 46%, not exceeding 60%. 

IR: Mean height of 2.7 km. 

Class 7. CLOUD STREETS 

Low cloud pattern with obvious directionality. Typical cloud fraction 

of 58% but may vary from 15% to 90%. The 'streets' normally appear 

made up of cumulus clouds and the texture is grainy. 

VIS: Mean albedo of 43%. When compared to class 8 (rolls), the directional pattern usually has a shorter wavelength and the albedo is less. 

IR: Fairly uniform height, 2.3 km on the average. 

Class 8. ROLLS 

This obviously directional pattern is at mid level. Fairly thick, large 
9 

roll clouds are aligned usually with little spacing between them. Some 

open cells may be embedded in the field. 

VIS: Bright, mean albedo 75%. Mean cloud fraction of 93% and usually 

not less than 60%. 

IR: Grey to whitish, typical height: 4 km, less than 6 km. 

Class 9. POLYGONAL OPEN CELLS 

Open cells presenting a honeycomb or polygonal texture with sizeable 

holes or internal cavities in the middle of the cells. Small cumuli 

are also often present. Differentiated from class 10 by a lower cloud 

fraction and larger holes. The most convective cloud elements are 

individual towering cumuli as opposed to the entire doughnut shape 

cloud pattern in class 10. The cloud ring surrounding the hole may be 

incomplete whereas it is typically complete for class 10. 

VIS: Moderately bright, average albedo 55%. 

IR: Greyish brightness, darker in regions of small cumuli and brighter 

for the more convective cells which may reach 5.5 km. Typical height 

3.5 km. 

Class 10. STRONGLY CONVECTIVE OPEN CELLS 

Bright mid level cloud field characterized by doughnut shaped cloud 

elements with holes a few km in diameter. 

VIS: Bright, typical albedo 63%. Mean cloud fraction 93% and rarely 

lower than 60%. The texture appears lumpy. 

IR: Greyish to whitish with the holes usually perceivable. Mean height 

of 4.4 km, may reach 6 km. 
10 

Class 11. BRIGHT CLOSED CELLS 

Bright mid level cloud field with large blobs. May appear lumpy 

indicating towering cumulus. This class also encompasses the edge of 

class 12. VIS: Bright, mean albedo 63%. Typical cloud fraction 80% but 

may vary between 30 and 95% if the texture is lumpy and between 30 and 

85% if the texture is smooth (edge of class 12). Above these upper 

limits in cloud fraction, the sample belongs to class 12. 

IR: Greyish , typical height 3.4 km. 

Class 12. NIMBOSTRATUS 

Overcast bright stratum at mid level. The texture is usually smooth but 

a somewhat more lumpy texture with cloud fraction higher than 95% will 

also be labelled 12. 

VIS: Very bright. Typical albedo 77%. Typical cloud fraction of 98% and 

never less than 85%. 

IR: Greyish to whitish, smooth. Mean height 4 km, may vary from 2.5 to 

6 km. 

Class 13. ALTOCUMULUS 

Greyish mid level cloud deck, apparently single-layered. 

VIS: Usually quite grey, typical albedo 34%. Typical cloud fraction 45% 

but may vary from 1 to 100%. 

IR: Greyish to whitish. Fairly uniform. Typical height of 4.2 km and 

may vary from 3.5 to 6 km. 

Class 14. CUMULUS WITH ALTOCUMULUS 
11 

This cloud field consists of a lower cloud deck of cumulus type and of 

a mid level cloud deck (between 2-6 km). 

VIS: Typical albedo 47%. Higher deck made up of sizeable patches 

whereas lower deck is typically grainy (cumulus). 

IR: The lower deck is dark, the mid level deck is greyish to whitish. 

Mean height of 4.8 km, not exceeding 6 km. 

Class 15. STRATOCUMULUS WITH ALTOCUMULUS 

Cloud field with apparent multilayering occuring between 2 and 6km, 

with lower cloud deck typically stratocumulus. 

VIS: Semi-bright, typical albedo 47%. Typical cloud fraction 72%. 

IR: Varying tones between greyish and whitish. Mean top height 5.5 km, 

not exceeding 6 km. 

Class 16. THIN CIRRUS 

Fibrous texture, semi-transparent. 

VIS: Low albedo, 25% on the average and less than 35%. Presence 

detected from milky veil and sometimes not detected at all. Lower 

cloud deck may be present. 

IR: Whitish, often with considerable structure. Mean apparent height of 

6 km with most of the cirrus field typically appearing in the infrared 

image at lower heights. 

Class 17. MULTILAYERS WITH CIRRUS 

High clouds are obviously present from the infrared image and most 

often lower level clouds as well and the visible image is greyish. 
12 

VIS: Typical albedo 41%, covers the range 35-55%. The typical cloud 

fraction is 87% but it is sufficient that 5% of high clouds (above 6km) 

be present for the field to be classified 17. The overcast version of 

this class will be'classified 19 only if the percentage of high clouds 

is greater than 85%. 

IR: Considerable structure from greyish to white. Mean cloud top height 

of 8.7 km. 

Class 18. BRIGHT MULTILAYERS WITH CIRRUS/CUMULONIMBUS 

General description as for class 17 except that the visible picture is 

moderately to very bright (albedo greater than 55%). This class 

encompasses isolated cumulonimbus or edges of cumulonimbus. 

VIS: Bright, typical albedo 72%. Typical cloud fraction 94% but 5% or 

more high cloud fraction is sufficient for the field to be classsified 

18. The overcast version will be classified 20 only if the percentage 

of high clouds is greater than 85%. 

IR: Same as 17. Mean height 8.9 km. 

Class 19. DENSE CIRROSTRATUS 

Overcast field of high clouds with less than 15% of the pixels at 

height below 6 km. 

VIS: Grey, typical albedo 50%. Typical cloud fraction 97% and no less 

than 90% (classified 17 in that case). 

IR: White, possibly a small greyish region, indicating some mid-level 

cloud deck. Mean height 10.3 km. 

Class 20. OVERCAST CUMULONIMBUS 
13 

H’igh and “right overcast cloud field. Usually represents deep cumulonimbus in the core of a vigorous cyclonic system or cold front. Less 

than 15% of the pixels are below 6 km. 

VIS: Very bright: typical albedo 87%. Typical cloud fraction 100% and 

no less than 90% (classified 18 then). 

IR : White. Typical height 10.7 km. 

Fig. 2.2 summarizes the thresholds in height and albedo 

mentioned in the description of the classes. The standard deviations 

are also shown. The plot is an anticipation of the results obtained in 

Chapter 3 with a large training set of cloud patterns. It should be 

kept in mind that the natural limits of height and albedo are about 

twice the standard deviations presented on the graph. The figure 

indicates that although some classes (e.g. 1 and 2) are relatively well 

separated, it is obvious that the discrimination cannot be accomplished 

from albedo-height characteristics only. Boundary layer classes 3-7 

form a closely packed group as well the higher albedo mid-level classes 

8-12 and the altocumulus classes 13-15. Features describing the 

morphology and the state of multilayering are essential to separate 

those classes. 
I 

5 

2 3 

7 

9 10 11 

4 

8 

12 
13 

17 

e- ..... 

i- 

-. ... 

14 

18 

. L. ., 

15 

-. .LC-r..... ..... U.-’ 

...... - ,.--.I ..-.. . -.a *. . . 

... .. .. ... .. * -. ...... - .... ... - . .__ --- - .- . 

0KiGl;9Ab PAGE 3s 

OF POOR QUALITY 

... _.. .. ..... .... 

.. .- - .. . ..... 

.. ..... 

._ .. 

.... .. ... 

. c 

--- ..... . I - ..- ....... 

. -.. .... .. . - . -..- . ....- I 

- .._ __. -. 

16 

... 

c 

20 

Fig. 2.1 Examp!e !~f 2 visible and infrared ?ai- cf iwges +or each 

cloud class, indicated by its number. The visible image is on top o+ 

the ctrrespondirs ;nirareci imaee. !TZSO cizo: 122 X 123 km. 
14 

13 

12 

11 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

0 

I I I I I I 1 I I I 

1 

I_ I 

1 1 -I 

1" c 

I 

1 I 1 I I I, I I 

0 .1 .2 .3 .4 - I I I I I 1 

.5 .6 .7 .8 .9 1 .o 

MEAN CLOUD ALBEDO 

Fig. 2.2 Mean height of uppermost cloud layer (km) versus 

mean cloud albedo for each cloud class, indicated by 

its number. Associated standard deviations shown by bars. 
14 

3. DESIGN OF A CLASSIFIER 

3.1 Data set 

In order to recognize all of the twenty classes just 

defined, a large data set was required. Twenty nine images were 

analyzed, one per day for the 29 days of February 1984 at 16 GMT, about 

11-12 AM local time, for the Western Atlantic region defined in Fig. 

3.1. The region is between 26 and 45 degrees latitude north and 60 to 

80 degrees longitude west; the west side of the domain is defined by 

the east coast of the United States. 

This region and time of the year were chosen after the hard 

copy archives were examined (Space Science and Engineering Center 

Library, University of Wisconsin). Winter brings several cold air 

outbreaks in this area; these systems produce a significant number of 

occurrences of mesoscale cellular patterns. February 1984 was 

particularly interesting from that perspective. A secondary goal was to 

produce the cloud climatology for that month, in order to illustrate 

both real time and climatological applications of the method. 

Exactly 204 contiguous boxes were analyzed for each scene 

(one per day), as indicated in Fig.3.1, except for 3 days where small 

parts of the images were missing. In all. 5845 samples were analyzed 

for a coverage of 98.8% for the month (completeness implies 29 X 204 = 

5916 samples). Analysis here refers to the extraction of features for 

classification, a topic to be discussed in the following section. With 

such a data base, all twenty cloud classes were represented a 
Fig. 3.1 Region of analysis seen in satellite projection. 

Asterisk indicates Bermuda. The grid shows the 204 contiguous areas to be analysed. Size of areas: 128 X 128 

visible pixels at nominal lkm resolution. 
15 

significant number of times. A grid was superimposed on the satellite 

image and a sample number was placed in the bottom of each box. The 

author then subjectively assigned a class number to each sample 

identified by its number and keyed in that information in the record of 

the features just extracted. The decision on the class was taken after 

switching back and forth between the visible and infrared pictures. 

A hard copy image of each scene was taken for both the visible and 

infrared images and the record of the features was also printed. Thus 

the analyst had all material for further analysis in hand. 

Each visible image consists of m = 64 X 64 = 4096 pixels at 2 

km resolution and each infrared image consists of n = 32 X 32 = 1024 

pixels at 4 km resolution, the nominal resolution of the infrared 

sensor (the actual footprint is 4 X 8 km with the same digital count 

assigned to two 4 X 4 km pixels). It was deemed appropriate not to 

degrade the visible resolution to that of the infrared in order to 

preserve information on morphology. On the other hand, the full 1 kn 

resolution was not kept for computer time considerations. A philosophy 

applied throughout in the feature extraction process is not to rely on 

collocated visible and infrared pixels but rather to match cloud 

fraction thresholds in both histograms. The visible image was reduced 

to 2 km resolution by sampling every other line and row, a method 

preferable to averaging to preserve cloud properties. Eight visible 

sensors are disposed vertically to scan eight lines at a time as the 

optical system scans from the left to the right side of the planet. It 

was found by expanding full resolution visible images several times 
16 

that horizontal stripes were often present at eight line intervals 

indicating that one of the eight sensors was in error by a few digital 

counts. That sensor turned out to be associated with odd lines; this 

problem was then ‘eliminated simply by sampling over even lines. 

Each infrared grey level count corresponds to a radiance 

which can be transformed into a temperature via the Planck function. 

This temperature is called the brightness temperature. This transformation implies the assumption that the emitting surface is radiatively 

black, i.e. its emissivity is unity. 

3.2 Extraction of features for classification 

Thirteen features are used to classify the cloud fields in this 

study. About three times that number were initially defined as we 

could not know beforehand which features would provide best results. A 

classification algorithm should use as few features as possible in 

order to simplify the decision process and to shorten the computer time 

devoted to feature extraction. In addition, the number of samples 

necessary for reliable statistics increases rapidly with the number of 

features as well as the number of classes. The variety of features 

finally selected reflected a balance of information on height (cloud 

top, aultilayering), shape (directionality, size distribution) and 

albedo. 

In choosing the features, obvious physical significance is a 

desirable asset. Cloud fraction at low, middle and high altitude, cloud 

top height and mean cloud albedo are among the features selected. In 
Table 3.1 Designation of the features used for classification in this study with associated 2-letter identification 

code. Image source in parenthesis: IR: infrared, VIS: 

visible, B&W: binary corresponding to visible cloud fraction, PS: power spectrum. D, DC*, m defined in text. 

Code 

CF 

Lo 

MI 

HI 

HT 

La 

AL 

NC 

ML 

BC 

cc 

ST 

SE 

Description Limits 

Total cloud fraction (IR, VIS) 0-1 

Low cloud fraction (IR) 0-1 

Middle cloud fraction (IR) 0-1 

High cloud fraction (IR) 0-1 

Cloud height of uppermost layer (IR) 0-14km 

Fraction of cloudy pixels with D < Dc* (VIS) 0-1 

Mean albedo of cloudy pixels (VIS) 0-1 

Number of clouds (B&W) 0-m/2 

Multilayer index (IR) 0-1 

Background connectivity (BtW) 0-1 

Cloud connectivity (S&W) 0-1 

Streakiness factor (PS) 0-1 

with wavelengths between 20-40km (PS) 0-1 

Fraction of spectral intensity associated 
, 17 

addition, less conventional cloud field descriptors are employed such 

as a multilayer index, a streakiness factor and connectivity indices 

that are easy to relate to the visual appearance of the images. This 

is an advantage over features that represent moments taken over 

pictures, (see Haralick, 1976) as these features are often difficult to 

interpret. 

Every feature is identified with a 2-letter code for further 

reference. Table 3.1 presents those features, along with their 

physical limits. 

3.2.1 Cloud fraction and height features (LO, MI, HI, CF, HT) 

Because a good algorithm to define cloud fraction is essential, 

considerable attention was devoted to this problem. A basic requirement is that the scheme should be usable throughout the day, from 

shortly after sunrise to shortly before sunset. Therefore, the geometry of the system has to be considered appropriately. Fortunately, 

this problem is well understood and a good treatment of it can be 

found in the series of three papers by Minnis and Harrison (1984, 

hereafter MHA-a, b, c). Three angles define the geometry: the solar 

zenith angle (3 1, the satellite-viewing zenith angle (8) and the 

viewing azimuth angle (cy) (see MHA-a, Fig.3). The three angles are 

obtained from knowledge of day of the year, time of the day, satellite 

position and field of view location. Considering those angular 

factors, the visible brightness (or digital count) corresponding to a 

reflectivity t at nadir is (MHA-a, Eq.6): 
18 

(3.1) 0 

where ( 5) is the visible normalized directional reflectance and 

is the visible, anisotropic reflectance correction factor; 

Both &and X3values for water were 

obtained empirically from a large data set of GOES images to gather 

&( 5, 

Vrefers to the spectral band. 

, 9) 

statistics from all possible angular combinations. A reasonable value 

for t is 7% for water. The existence of this model, defined by its 

tables of &and &, was a decisive incentive to design our scheme to 

be used with the natural satellite projection. The tabulated values of 

&and 7I)for water were provided by the authors of MHA a-c. The 

constant B(0.t) is the heaispherically integrated brightness count at 

on the other hand, is the dark current constant. Its 

meaning will become explicit below. In MHA-c, the average clear-sky 

albedo (ocean plus atmosphere) over our region of interest (their 

Fig.10, derived in November 1978) is 15%. Therefore, we shall use the 

constant t = 0.15. So, Eq.(3.1) simply answers the question: given the 

three relevant angles, what is the digital count that the satellite 

would register corresponding to a clear-sky reflectivity of 15% at 

nadir? The visible calibration is defined by the relation: 

5= 0. Do ' 

E - a'D2 + b' (3.2) 

where E is in W/m& and D is the satellite-observed digital count with 

range 0-255. For GOES-EAST in early 1984 (subsatellite point: equator, 

75 W, height of 36,000 km), the values suggested for a' and b' are 

0.005393 and -2.67 W/m respectively. The value of I$ is thus 496. a# 
19 

The reflectivity is obtained from: 

where b= 336.43 W/m z is the fraction of the solar constant within the 

spectral bandwidth of the visible sensor. A reflectivity of 15% corresponds to 50.46 W/m* from (3.3) which in turn corresponds to a digital 

count of 99.26 from (3.2). Using this value in (3.1). one gets the 

value of B(0,15%) = 29408. Eq.(3.1) is universal and can be applied 

over all oceans uncovered by ice. Only the satellite related 

constants, 00 and B(0,t) need to be adjusted. The tables of and 

are much more complicated over land as they depend on the reflective 

characteristics of each type of surface. 

As in MHA-a, the boundaries between low, middle and high clouds 

are 2 km and 6 km and the height assignment is based on an assumed 

vertical temperature lapse rate of 6.5 deg/km with the sea surface 

temperature, Ts. as the lower boundary. The sea surface temperature is 

found in the following manner: 

1) The percentage of pixels in the visible image below Dt(with t = 

0.15). f(t), is calculated. If f(t) is greater than 5%. the average 

brightness temperature of the warmest f(t) pixels in the infrared 

histogram, T(t), is calculated and Ts is estimated through Ts = T(t) + 

4. The offset of 4 K approximates the atmospheric correction: it was 

obtained from comparisons of ship measurements of Ts versus infrared 

brightness temperatures in clear areas observed in several images. In 

Part 2, the accuracy of this technique for single retrievals is 

- - 
20 

evaluated at 2.69 K (rms) but may be improved to 2.2 K upon subtracting 

a class-dependent bias after classification. If the inferred value of 

Ts is off by 5 K or more from the monthly climatology, the climatological value is assigned. More accurate and physically based methods 

of sea surface estimation exist. For example, Bates and Smith (1985) 

claim errors of less than 1 K. However, images from three infrared 

channels are required; these were not always available. Also, it must 

be kept in mind that, in our region of interest, for about 509; of the 

time, the climalogical value of Ts is used due to excessive cloudiness. 

In those cases, it is not possible to use infrared methods such as that 

of Bates and Smith (1985). 

2) If the percentage of totally clear pixels, f(t) is smaller than 5%, 

we assumed that there are no holes sufficiently large in the cloud 

field for an accurate surface temperature assignment. Therefore, in 

that case, the climatological value for the month of February was 

assigned (one degree scale). The mean of Ts for a particular month is 

usually within 1.5 K from the all-time climatology for that same month 

(see the National Oceanic and Atmospheric Administration (NOAA) publication: Monthly Oceanographic Summary for one-degree grids of Ts and 

deviation of Ts from climatology for our region of interest). NOAA 

polar orbiting satellites continuously monitor the sea surface temperature globally: available values for Ts are usually less than a week 

old. When possible, one may prefer to use this product as opposed to a 

climatological value in overcast conditions. In any event, for the 

purpose of cloud classification, the accuracy on Ts obtained by the 
21 

present method is sufficient. 

Two modes are used to find the cloud fraction: a visible 

mode based on an albedo threshold and an infrared mode based on a 

height threshold. We define Dc as the cloud-no cloud threshold in the 

visible histogram and Tb as the cloud-no cloud temperature threshold 

from the infrared histogram. The usual mode (83% on the February data 

set of 5845 samples) is the visible mode. 

1) Visible mode 

In the visible mode, the digital count threshold, Dc* is obtained 

from : 

Dc* - Dt + Dm (3.4) 

where Din is an offset equal to 20 counts. This corresponds to an 

albedo threshold of 22% (for utilization with other platforms, it is 

suggested to use B(0, 22%) in Eq. (3.2) and Dm = 0 in Eq. (3.4)). 

Corresponding to Dc*, one gets the cloud fraction CF = CF(V1S). The 

offset was obtained from extensive experiments with cloud fields of all 

types with various viewing geometries. Images consisting of 64 X 64 

pixels at 2 km resolution were thresholded to binary images (black and 

white) based on Dc*. Both the real image and the binary images were 

enlarged to fill the entire video screen of the MCIDAS system (Suomi et 

. 

al., 1983). Then the analyst would switch back and forth between the 

two images, collocated on the screen, to subjectively estimate the 

quality of the thresholding procedure. It was found that Dm = 20 counts 

systematically provided a satisfactory threshold. Between Dt and Dc*. 
22 

it is admitted that clouds may be present but the assumption is that 

the field of view of pixels with such grey counts is less than 50% 

cloud covered. No attempt was made to calculate the cloud fraction of a 

single field of view within the limits 0 and 1. Thus, the cloud 

fraction defined here may be termed "effective". We assume that, to a 

large extent, possible errors due to underestimation of cloud fraction 

within a field of view when the pixel is assumed completely clear are 

compensated equally by possible errors of overestimation when the pixel 

is assumed completely cloud filled. 

2 ) Infrared mode 

The visible threshold Dc* fails for low-albedo pixels which may 

be completely cloud filled with semi-transparent clouds. Cirrus fields 

often present such characteristics: a low albedo (lower than Dc*) in 

the visible matched by the appearance of a veil of variable brightness 

in the infrared. Thus, a second mode is necessary. 

In the infrared mode, any pixel corresponding to a height of 1 km 

or more is assumed cloudy. We then find the infrared cloud fraction 

CF(1R). If CF(IR) is greater than CF(V1S) by 5% or more, then the 

infrared mode is chosen. The value of Dc (equal to Dc* up to now) is 

lowered so that CF(V1S) = CF(1R). Since errors on sea surface 

temperature are typically much less than 6.5 K, 1 km is a safe margin 

for such errors resulting in errors on height (recall that a lapse rate 

of 6.5 K/km is assumed for height estimation). On the other hand, 

pixels of albedo lower than Dc* and of apparent height below 1 km can 
23 

safely be assumed clear in most cases. 

The cloud top temperature, TT, is obtained from the brightness 

temperature corresponding to the 97th percentile in the infrared 

histogram. For clear skies, TT = Ts. The cloud top height, HT is 

obtained through HT= (Ts - TT)/6.5. It is understood that the true 

height may differ from HT. The temperature lapse rate for boundary 

layer cloud fields is often close to 10 K/km' (dry adiabatic), as 

opposed to 6.5 K/kn, and the surface air temperature is more 

appropriate than Ts as lower boundary. As a classification feature 

however, HT is not required to represent the true height and a 

unique, robust definition of each feature is desirable. After classification, one may estimate the true height from an assumed classdependent temperature lapse rate. In addition, it should be noted that 

feature HT is not designed to obtain the representative cloud top 

height of the entire cloud field but that of the uppermost layer. 

When there are large sea surface temperature gradients, such as 

in the midst of the Gulf Stream, the infrared mode may be selected even 

in clear conditions. To avoid this problem, if the sea surface 

temperature difference from climatology across the box is greater than 

6.5 K (thus apparent cloudy pixels above 1 km height may be registered) 

and if HT < 2 km, the infrared mode is skipped: sea surface temperature 

differences acoss a box will never reach the 13 degrees corresponding 

to a 2 km apparent height. 

From knowledge of the cloud fraction, CF, an important physical 

parameter is the apparent cloud base temperature, Tb, which is the 
24 

temperature corresponding to CF in the infrared histogram; pixels of 

brightness temperature higher than Tb are assumed clear. Having 

extracted Ts and Tb, the formal definitions of the low cloud amount 

(LO), the middle cloud amount (MI) and the high cloud amount (HI) 

follow: 

i) LO is the percentage of pixels with brightness temperature T such 

that Tb < T 6 Ts - 13 with LO = 0 if Tb < Ts - 13. 

ii) MI is the percentage of pixels such that 2 km < HT4 6 krn. 

iii) HI is the percentage of pixels with HT > 6 km. 

From the above definitions, it is clear that CF = LO + MI + HI. 

To avoid redundancy, all four cloud fraction features were never used 

th 

together. 

3.2.2 Visible reflectance features 

The mean cloud albedo, AL, is computed ove ot 

the entire scene). The mean value of the digital count squared over 

all cloudy pixels, Gs, is first calculated (Eq. 3.5 below). Then the 

value of AL is obtained from Eq. 3.6. Designating the number of pixels 

in the visible histogram with grey count i by hv(i), we can state 

formally that: 

Gs = c i2 b(i) / 1 hv(i) 

I-D c ,25 5 l=Dc,255 

AL = (a'Gs + b')/(Io cos(C)) 

cloudy pixels ( 

A second albedo related feature used in this study is the percen- 
25 . 

tage of the cloud fraction for which the pixels have a reflectance 

below Dc*. Obviously this number, LR (for low reflectance) is zero if 

the visible mode is taken since in this case Dc = Dc*. Formally, for 

Dc < Dc*: 

LR = {i hv(i)} / C {i hv(i)) i=Dc, Dc* i=Dc, 255 (3.7) 

The feature LR is most useful in detecting thin cirrus clouds. 

3.2.3 Multilayer index c ML) 

A simple multilayer index feature, ML, proved very useful for 

quantifying the degree of multilayering in a cloud field. As will be 

shown in section 3.4, mutilayered systems are present about 40% of the 

time and this importance is reflected in our classification by several 

multilayered classes (14 to 18). 

The multilayer index is defined as the percentage of the cloud 

field with height 2 km or less below the cloud top height HT. Defining 

g(z) as the grey level corresponding to height z and hir(i) as the 

number of pixels with grey level i in the IR histogram, ML is defined 

as : 

Thus a single layer cloud field, by definition, is one with all of 

its cloudy elements within a 2 km thickness slab in the atmosphere with 

an upper limit approximately at the cloud top (in fact the upper limit 

is the coldest pixel, we recall that 3% of the pixels correspond to an 
26 

apparent height above HT). Cloud fields with ML < 0.7 correspond to 

infrared images with significant structure. ML is a good descriptor of 

the variability of grey tones that one can perceive in the infrared 

picture. A similar index, defined from z(Tb) and upwards as opposed to 

HT and downwards could also have been defined. We preferred however to 

give emphasis to the presence of upper level decks than to lower level 

decks in our definition of multilayering. For example, a cloud field 

with CF = 50%, HI= 5% and LO = 45% will have a very low value of ML, 

ML = HI/(HI+LO) = 

lower layer. 

3.2.4 Connectivity 

These features 

0.1, even though 90% of the field belongs to the 

features c PIC, 89 

are derived from binary images, the black and white 

pictures corresponding to CF. Pixels with the same color connected 

only diagonally belong to a different cloud (background) entity 

wheareas pixels with the same color connected to the east, west, north 

or south neighbors belong to the same cloud (background) entity. The 

number of clouds, NC, and the number of background areas, NB, are 

defined in this manner. We then obtain the cloud si2.e distribution as 

well as the background size distribution. Let us define by hc(i) and 

hb(j) the number of pixels associated with cloud or background entity i 

and j, respectively: i = 1, 2...NC and j -- 1, 2, ... NB. Further let 

all clouds and background areas be ordered from smallest to largest. 

Then the cloud connectivity, CC, is defined as: 

CF L 0 (3.9) 

CF * 0 
27 

where k is the cloud entity such that the inequality: 

hc(i) 2 mCF/2 

111 ,k 

is satisfied from the summation of the cloud entities ordered in size. 

Similarly, the background connectivity, BC, is defined as : 

hb (k ' / (m( I-CF) ) CP 3 1 

1 CP - 1 

BC =[ (3.10) 

where k' designates the background entity for which the inequality: 

is satisfied from the summation of the background entities ordered in 

size. Thus in the above equations, hc(k) and hb(k') are the median 

sizes of the NC clouds and NB backgroud areas, respectively. Fig. 3.2 

illustrates the extraction of the connectivity features. 

Theoretically, NC (or NB) may reach a maximum value of m/2 

(checkerboard pattern). In the present case with m = 4096 and 2 km 

resolution, the maximum number of clouds encountered was 340. The 

reader interested in vertical retrievals will find it useful to know 

the location and size (hb(NB)) of the largest hole in the cloud field 

obtained here. CC is most useful for differentiating cumulus and 

stratocumulus with CC smaller for cumulus (highly disconnected elements) and higher for stratocumulus (clouds more highly connected into 

a few entities). For stratus decks, CC is normally near unity. BC 

turns out to be a good detector of holes and is notably low for open 

cells. 
a 

b 

Fig. 3.% 

pixels grid. 

NC = 13, 

open cells with NC = 3, 

4/26 = .154. 

Examples of connectivity indices on an 8 X 8 

Clouds are black. a) Simulated cumulus with 

b) Simulated 

BC = NB = 1, CC = 1/16 = .063, BC = 1. 

NB = 8, CC = 33/38 = -868, 
28 

3.2.5 Power spectrum features [ 5TJ5€) 

' Two-dimensional power spectra are very useful to obtain information 

on the extended structure of a cloud field, that is on the dominant 

wavelengths and orientations characterizing it. Garand and Weinman 

(1986) have provided examples of two-dimensional power spectra taken 

over cloud areas. Utilizing the same notation as in their paper, the 

FFourier transform of the image f(k,l) is: 

R-1 L-1 

with K,L as spatial dimensions. The power spectrum intensity PS(u,v) = 

:Q(u,v)l , is defined as the sum of the squared values of the real and z 

imaginary parts of the Fourier transform (Rosenfeld and Kak (1982)). 

The orientation, 9 and wavelength x assocjated with any (u,v) 

component are readily obtained from: --j 

4 - tan -1 (u/v) (42 s 4 s n/2, off x axis) (3.12) 

and 

(3.13) x = SP(U2*2)'1 

with t the linear 

dimension in pixels of the scene, assumed square. The power spectrum 

is symmetric with respect to the origin. It is clear that streaky 

patterns have streaky power spectra and circular patterns, such as 

those of open cells, tend to have their dominant components all around 

the origin. Overcast or clear fields will tend to have all the energy 

at or near the origin (infinite wavelength). From this experience, a 

unit resolution of the pixels and S = K = L is the 
29 

streakiness factor is defined in the following manner: 

i) 

ii) Threshold all values of PS(u,v) below 20% of PSm to zero. This 

eliminates the influence of high frequency components which are 

irrelevant to the appearance of the mesoscale morphologies and 

contribute only to local grey tone variability. To eliminate a possible 

false peak due to having one of the eight sensors off (producing horizontal stripes), the component corresponding to such 8 km periodicity 

(the (0, 16) in our case as derived from (3.13)) is set to zero. 

Find the maximum value, PSm of PS(u,v), (u,v) # (0,O). 

iii) From this thresholded power spectrum, estimate the absolute value 

of the correlation coefficient, weighted by PS(u,v), as a measure of 

streakiness: 

(3.14) 

with: Sy = C C V~PS(U,V) Sxy = C uvPS(u,v) Sx = I: Z U~PS(~,~), 

uv uv uv 

u=O*1*...S-1 ; v=O,l,...s-1 ; (u,v) * (00) . Recognizing that if the pattern is oriented in the north-south or eastwest direction, the measure is misleading as ST = 0, ST is also 

evaluated from a set of axis rotated 45 degrees from the nominal set 

and the maximum of the two estimates of ST is retained. ST is an 

essential feature for the detection of directional patterns: classes 7 

and 8. 

Observation of open cellular patterns have shown that the typical 

size of open cells is 30 km (Agee and Dowel (1976). Beniston (1985)). 

Considering this, we designed our second power spectrum feature, SE for 
30 

spectrum energy or intensity, to be the fraction of intensity in the 

power spectrum characterized by wavelengths between 20 and 40km. Let 

the radius rd in the power spectrum be defined as: 

rd - (u2 + v2)' (3.15) 

then, defining TE as the total intensity in the power spectrum, 

excluding the (0,O) component (which represents the mean grey level) 

SE is defined as: 

(3.16) 

with (u',v') such that: Idt rd(u',v') < rd 

2 

The thresholds that we used in rd are 3.1 and 6.1 corresponding to 

wavelengths of 41.3 km and 21.0 km, respectively, from Eq. (3.13). The 

value of SE turned out to be clearly higher for open cellular patterns, 

classes 9 and 10, as opposed to all other classes. 

Bunting and Fournier (1980) were among the first to test spectral 

classifiers. Their results were somewhat disappointing: an accuracy of 

46% for a 6-class scheme. Later, their work was pursued by Logan et 

al. (1983). Our experience reveals that power spectra do reveal unique 

information on the mesoscale organization; however, it is not a good 

idea to rely uniquely on such features. In near overcast situations, 

power spectra reveal little information. 

3.2.6 Feature extraction example 
a 

C 

Fig. 3.3 Example of a cloud field belonging to class 7 

(cloud streets) with parameters: CF = .363, LO = -320, MI 

= -043, HI = 0, HT = 2.33 km, LR = 0, AL = .581, NC = 179, 

ML = 1, 036, ST = .915, SE = .228, Tb = 

280.5 K Ts = 288.2 K. a) visible, 

b) binary, C) power spectrum of the visible image (only 

the 32 X 32 center portion of the 64 X 64 spectrum is 

shown) and d) infrared. 

BC = -208, CC =. 

The four images are : 
. I 31 

. 

.. 

.. 

Fig. (3.3) shows an example of the visible, infrared, binary and 

power spectrum images for a cloud field along with the 13 features 

(see Table 3.1) extracted from those images. The sample is a typical 

class 7 field (cloud streets). The field is single layered, has a high 

streakiness factor and correspondingly a streaky power spectrum and 

numerous small clouds which result in a low cloud connectivity. 

The quality of the thresholding procedure to obtain the cloud 

.fraction can be appreciated by comparing the visible and binary images. 

The major component at (1, 8) in the power spectrum corresponds to an 

orientation of -7 degrees off the x axis and a wavelength of 16 kn from 

Eqs. (3.12) and (3.13), in agreement with the hard copy image. 

Except for CF = 0 indicating class 1, no one feature, alone is 

decisive in inferring a class. Together however, this set of features 

is very powerful, as we are about to demonstrate. 

3.3 Classification methodology and results 

3.3.1 Multivariate Gaussian discriminant function 

A sample is defined by its features. Let x represent the vector 

of features and c the number of classes. A common way to design a 

classifier (Duda and Hart, 1973) is to opt for some discriminant 

function g;(x), i-1, ... c and to assign the sample to class i if 

Dp to its analytical tractability, the multivariate normal (or 

Gaussian) density is the most widely used discriminant function. The 
32 

general multivariate normal density is written as: 

(3.18) 

with E the expected value operator and Xt the ensemble of samples 

belonging to class i. In Eq. (3.18), d is the number of features or 

dimensionality, &is the d-dimensional mean vector of the features and 

fiis the d X d covariance matrix. 4 is symmetric and positive definite. 

Thus, such a classifier is completely defined by 4' and &;for each class 

i with g(x) = p;(x) used in Eq. (3.17). The notation Y = N(N,Ci) 

indicates that the vector Y belongs to a multivariate normal population 

characterized by4iand;fi. This type of classifier is the basis of our 

classification algorithm to be described in section 3.3.3. 

3.3.2 Theoretical separability estimates from Monte Car lo 

simulations 

A very useful tool to test the theoretical separability of the 

data is to generate artificial samples, that is, sample populations for 

each class having the same mean and covariance matrix as the real data. 

These artificial samples are then classified by using Eq. (3.18) in Eq. 

(3.17). The result is an upper limit to the separability of the 

classifier. If this limit increases as we change the features, it 

means that the new choice of features is more appropriate. If on the 
33 

other hand the theoretical limit decreases while the apparent separability of the real data increases, this is an indication of 

'overfitting' and results on independent data',are prone to be poorer 

than those claimed from the training set. If we decompose the 

covariance matrix g; into the lower triangular matrix A such that AA f 

= f{ (Martin et al., 1965), a vector Y belonging to the desired 

distribution N( 4, t; ) is generated from: 

Y= AT+ 

-v 

(Scheur and Stoller, 1962) with each of the d components of r obtained 

from an ordinary univariate Gaussian random generator belonging to 

N(0.1). We used the Box-Muller method (see Forsythe et al, 1973, 

chapter 10). 

3.3.3 Classification algorithm and results on a dependent 

data set 

The classification process was supervised in this study. In 

supervised classification, the analyst labels each sample with a class 

number and trains the classifier to assign the proper class. The 

algorithm is built in two stages, as shown in Fig. 3.4. In Stage 1, the 

sample is assigned a temporary class using a multivariate normal 

discriminant function. Thus, Stage 1 is a one-step method as opposed to 

a hierarchical method. For that reason, it is very efficient as well as 

simple. Stage 2 has an error-correcting function only, that is, the 

result of Stage 1 is checked according to some simple criteria based on 
FEATURE 

EXTRACTION 

STAGE 1 : 

Mutivariate 

Normal 

Classifier 

STAGE 2: 

Error 

Correcting 

................................ * * * LO, MI, CF, ML, ST, SE, * * BC, AL, LR, HT, NC * * * ............................... I I 

I 1 

I I 

\I/ I ............................ I 

*I * Assign 11-D sample 'ji to * I 

* class i if:- *I * pi (st) > pj (XI, all j # i * I 

1,. .. ,20 *I 

*I ............................ I 

I I 

I I 

I I 

* 

*i= * 

********* * * 

* cc, * * HI * * * ********* 

I 

1 

i 

I 

I 

1 

I 

I 

I 

I 

I 

I 

I 

I 

I 

\I/ \I/ \I/ ........................................... * * * Maintain i or assign ?to a new class j * * if feature-based conditions contradict * 

* * ........................................... 

* i while favoring j * 

Fig. 3.4 Supervised classification scheme designed for 

this study. 
34 

the most discrim-natory feature(s) between the class found in Stage 1 

and the class that was selected second best. Stage 2 was designed 

after it was realized that the vast majority of errors were samples 

that belonged to the second best choice from Eq. (3.17). This was 

fortunate for it saved much effort in trying to find the ultimate tree 

structure. With 13 features and 20 classes, the number of possible 

combinations is enormous. A revealing example of this situation is the 

work of Mui and Fu (1980) on automated classification of nucleated 

blood cells in 17 classes. 

a) Stage 1 

A summary of the classes assigned to the 5845 samples extracted 

from February 1984 pictures revealed that most classes had at least 100 

samples. From images taken in January 1984 over the same region, a 

small set (96) of extra samples were taken so that each class now had 

over 100 samples. Then, exactly 100 samples per class were randomly 

selected to define a training set of 2000 samples. 

In order for all the features to have similar weight, they were 

normalized to zero mean and unit variance. The means of the features 

and their standard deviations over all the 2000 samples are given in 

Table 3.2. The feature vector x is transformed to x. its normalized 

version, through: 

4 r;. 

with * and e* being the means and standard deviations listed in 

Table 3.2. For the remaining part of this study, the feature vector x. 

.c 
Table 3.2 Means and standard deviations of the 13 

features for the ensemble of 2000 samples forming the 

dependent data set. 

Feature Mean Stand. dev. 

ST 

SE 

AL 

HT 

BC 

Lo 

MI 

ML 

CF 

NC 

LR 

cc 

HI 

. 5595 

1821 

. 5165 

4 . 7470 

-5781 

-3843 

-8127 

30.58 

0479 

07944 

1317 

1600 

6760 

2684 

1571 

.1977 

2 . 9740 

.3665 

. 2258 

3394 

.2776 

.3222 

41.08 

1361 

03147 

2842 
35 

refers to the normalized feature vector and the circumflex accent is 

dropped. Table 3.2 was obtained following the final labeling of the 

samples (not the initial one with 100 samples per class). The features 

are not normally distributed over the ensemble of the data. This is 

not a problem since the only concern is that the assumption of normal 

distributions holds for the features within each class. 

Extensive tests were made to find out the optimum set of features to 

be used collectively in Eq. (3.18). A useful criterion is the 

divergence DijKbetween two classes i and j for feature k: 

(3.21) 

It is easy to see (from the overlap of circular areas centered at 

dikand &Hand of radius ViKand ri~ , respectively) that a feature with 

D<jk> 1 has discriminatory skill whereas one with Di)y< 0.5 has poor 

separating power. Another useful tool in selecting the features is 

their correlation coefficient between themselves over the ensemble of 

data,- as shown in Table 3.3. Cnly one out Of two or more-highly correlated features ( r > 0.7) is retained. Using the divergence criterion, 

the information from Table 3.3 (and similar information from other 

features being investigated) along with Monte Carlo testing of the 

separability, we ultimately found an optimal set of features to be 

used in Stage 1. The number of features in the set is 11; they are ST, 

SE, AL, HT, BC, LO, MI, ML, CF, NC and LR. The other two features 

defined in chapter 2, CC and HI, will be used in Stage 2 only. One can 

see from Table 3.3 that the maximum correlation between any two 

features among the eleven selected for Stage 1 is 0.63 only. Most 
6 

cv 

0 

u 

u '. 

ac 

d 

00 

I sa, 

4JVI 

3 

0990 

Inof-* 

.... 

0000 

I 

II 

c) ka 

=1E: 4Ja 

a 

a,u &*+r-drn uu 

NdC+)h(O 

00000 

I 111 

..... 

tntn~0O~d 

ndocv~om 

0000000 

I 111 

....... 0 

d 

ual 

-4 s 

uu 

U cu' 

m OW 

00 

u 

VI 

Gal 

&Qmd0&Onn&d 

UJ 0~N000~~0d~ 

v) ........... 

00000000000 

IIII I IO 
correlation coefficients are very low indicating minimal redundancy. 

For' the boundary layer classes, numerical instability may occur 

when inverting the feature covariance matrix as CF = LO + MI exactly 

for all the samples of the class (HI = 0). This problem is easily 

solved by adding Gaussian noise in the proportion of one tenth of a 

percent to CF. In this manner, all covariance matrices are invertible. 

In addition, for some classes, a particular feature nay be a constant 

resulting in a zero element on the diagonal of the covariance matrix. 

For example, ML = 1 for all samples of classes 1 to 7. Again, this is 

easily solved by not allowing the diagonal elements of the covariance 

matrix to be below a preset minimum value; the value selected is 0.005. 

This is equivalent to adding noise to that feature in the proportion of 

0.7% of one standard deviation (recall that all features have unit 

standard deviation over the ensemble of data) and therefore this procedure does not affect the separability. 

The first result was a separability of 54%. This was disap- ~ ~~ 

pointing as we knew from unsupervised classification testing (where the 

computer defines classes with means as separated as possible, see 

Garand (1986, Appendix A) or Duda and Hart (1973, section 6.9)) that 

97% separability could be achieved from the selected set of 11 features. We found that the cause of the low separability was simply 

- imperfect labeling of the samples. Indeed, with twenty classes, many 

border-line cases are to be expected and subjective decisions based on 

height and albedo characteristics are subject to error. Thus a good 

"truth" set is d'ifficult to build from the subjective judgement of 
37 

nephanalysts alone. Therefore, we adopted the following man-computer 

interactive relabeling procedure, a methodology where the human still 

supervises the labeling process, but is helped in his task by objective 

measures extracted by the computer: 

i) Print the identification number of all incorrectly classified 

samples, their actual class number and in which class they are 

apparently aisclassified. From the hard copy images, decide if 

relabeling is desirable. Examine the printout of the features to 

understand the logic of the classifier. 

ii) Resubmit. If the separability still increases, both from the 

actual data and from Monte Carlo simulations based on the updated means 

and covariance matrices, go back to i. 

The first iterations represent long hours of work as each of the 

few hundreds aisclassified samples was considered individually. 

Examination of the features often proves useful since each feature 

provides a different way of considering the sample. The first ~ 

iterations rapidly increases the separability. As the iterations 

progressed, the analyst was training himself in defining precisely what 

was previously all subjective, clarifying all the decision thresholds. 

Theoretical separability topped at about 95% while actual separability 

topped near 83%. It was realized that trying to go beyond that result 

led to overfitting. 

As was previously mentioned, it turned out that 85% of the 

misclassified samples were the second choice of the classifier. This 

led to the error-correcting Stage 2 
38 ' 

2) Stage 2 

The second and final stage was appended to Stage 1 resulting in a 

new series of iterations. Let C1 be the first choice of the normal 

density classifier, in other words the result of Stage 1 and let C2 be 

the second best choice. Based on at most two features, a decision is 

made to change or not or not to change the result to C2. In Stage 1, 

using all 11 features together led to some errors because for most 

pairs of classes, only 1 to a few features are required. The others are 

irrelevant. We opted for a simple decision rule that did not require 

new storage of constants: assign the sample belonging to class i = C1 

to class j = C2 if: 

k=l,kmax u2 

jk 

where k is a feature identifier and kmax is either 1 or 2. Table 3.4 

shows the features used in Stage 2 for all pairs (Cl. C2) to decide 

between %I and C2. Were there is m entry, Eq. (3.22) is skipped. 

This is based on experience that either the ambiguity C1 versus C2 

never occurs or that no improvement in their separability can be 

achieved from Eq. (3.22). Where the entry in Table 3.4 is symmetric, 

only one feature is used (kmax = 1) and where it is not symmetric, two 

features are used (kmax = 2). For example, an ambiguity between 

classes 2 and 12 is resolved from features HT and LO. The reader may 

appreciate that the feature(s) selected in Table 3.4 are the most 

relevant to the pair (Cl,C2) considered. Note that Eq. (3.22) is . 

executed regardless of how large the ratio pc%(x)/pc;~(x) may be from a A 
~IlllllllIlUbIIIIIIII 

llllltllllurIIl I1 Ill 

&me 

04 0 

WE: 

n 

*oQ)Q) 

wz c k 

w 01 
.Table 3.5 Sequence of conditional statements defining 

Stage 2. The decision on the right is executed if the 

statement is satisfied. C: class, C1: class chosen from 

Stage 1; C2: 2nd best class from Stage 1; E: exit Stage 2. 

nb Conditional statement Decision 

1 C1 = 1, CF > 0.01 C = C2, E 

2 CF < 0.01 C=l,E 

3 C1 = 4, C2 = 7, ST > 0.5 C=7,E 

4 c1 = 9, C2 = 14 or 15, SE c 0.1 C = C2, E 

6 C = 9, ST > 0.9 C=8,E 

8 C = 5, 6 or 11, BC > 0.9, CC > -85 

9 C = 5 or 6, AL > 0.6 C = 11, E 

10 C = 5 or 6, HT > 3.5km C = 13, E 

11 C = 14 or 15, HT > 6km, AL > .55 C = 18, go to 23 

12 C = 14 or 15, HT > 6km, AL c -55 C = 17, go to 22 

5 Eq. (3.26) satisfied c = c2 

7 C = 7, ST < 0.5 c = 3, go to 19 

and HT < 2km C = 2, E 

13 C = 6 or 13, HT < 3km, BC c 0.1 c = 4, go to 20 

14 C = 10, AL < 0.3 c=9 

17 C = 6, NC > 80 c = 4, go to 20 

15 C = 9, CF 0.2 C=5,E 

16 C = 5, CF > 0.5 C=6 

18 C = 6, CF C 0.5 C=5,E 

19 C = 3, CF > 0.5 C=4, E 

20 C = 4, CF < 0.5 C = 3, E 

21 C = 4, ST > 0.5 C=7,E 

23 C = 18, AL < 0.55 C = 17, E 

24 C = 11 or 12, HT < 1.5km C = 2, E 

25 C = 12, CF < 0.85 C = 11, E 

26 

27 

28 C = 18, HI > 0.9, ML > 0.9 C = 20, E 

29 C = 17, AL < 0.25 C = 16, E 

22 C_-= 17, AJi -2 l2-55 e = 323, E 

C = 11, CF > 0.95 c = 12 

C = 12, HT > 6km C = 18 
39 

Stage 1; attempts to improve the results using that ratio failed. 

For the sake of elegance and simplicity, it was planned to use 

only Eq. (3.22) in Stage 2. We found however that by adding simple 

conditional statements, the result could still be improved. Eq. (3.22) 

sets decision thresholds. Most of these thresholds are satisfactory. 

We may however insist on a specified threshold. Usual practice separates scattered from broken fields with a threshold on CF of 50%. Thus 

we have set that threshold exactly using a conditional statement for 

pairs of classes (3.4) and (5,6). Similarly we specifically force the 

clear class to have less than 1% cloud cover. Other statements arise 

from experience that Eq. (3.22) is not successful with only two 

features. Note that the sample may be relabeled in a class different 

than C2. In all, 29 such conditional statements were implemented in 

Stage 2. There is no room here to comment on each of them but the 

reader will find the reasoning behind most of these statements fairly 

obvious. The entire Stage 2 is provided in Table 3.5. The fifth 

statement corresponds to Eq. (3.22); it is the most important statement 

since it applies for all C1 values whereas other statements apply for a 

particular value of C1. 

Table 3.6 shows the results after Stage 1 and Table 3.7 provides 

the final result following Stage 2. An improvement of 11% is gained 

from the 83% obtained from Stage 1. Stage 2 is particularly beneficial 

in correcting errors between class pair (3,5) using the new feature CC 

and errors associated with class 17 using the new feature HI with AI, 

and ML. Errors between pairs (6.7) and (3,7) are corrected by using ST 
40 

and the discrimination of open cells from rolls is solved through the 

combination of their most characteristic feature, namely BC and ST 

respectively. Comparing the entries on the diagonals of Tables 3.6 and 

.. 

3.7 reveals that all classes benefit from Stage 2 except a loss of 1 

sample for classes 4 and 14 and a loss of 2 samples for class 6. Note 

that systematic errors have disappeared. In the course of the 

iterations that led to the development of Stage 2, the theoretical 

separability increased only marginally, from 95% to 97%. whereas the 

practical separability inceased to 95%. At that point, we considered 

that our pattern recognition algorithm was final. As depicted in Fig. 

(3.4). a dominant asset of the classifier is its simplicity. The model 

is entirely defined by the feature means and covariance matrices for 

each class presented in Appendix, the definition of Stage 2 from Tables 

3.4 and 3.5 and the normalization constants of Table 3.2. 

From Table 3.7, the new number of samples in each class 

varies from 28 in class 4 to 184 in class 17. This represents a 

~ 

substantial change from the initial partition of 100 samples per class. 

These changes are due not only to labeling errors but also to redefinition of some classes during the analysis process. 

Tables 3.8 and 3.9 provide the non-normalized means and 

standard deviations of the features for each class. Fig. 2.2, the 

plot of HT versus AL, was based on these tables. Table 3.8 reveals the 

quality of the features chosen. One to a few features are maximized or 

minimized in each class. The reader will notice, for example, that the 

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41 

streakiness factor is clearly higher for the directional classes 7 and 

8 while SE is also distinctly higher for open cellular patterns, i.e. 

classes 9 and 10. The background connectivity, BC, is minimized in 

classes 8, 9 and 10 revealing the presence of holes in those cloud 

fields. The multilayer index is low in multilayered classes 14 to 18. 

The cloud connectivity permits ready separation of cumulus and 

stratocumulus. The detection of thin cirrus is greatly facilitated from 

feature LR in conjunction with HT. Tables 3.8 and 3.9 are a compact 

objective description of the classes presented subjectively in Chapter 

2. 

3.3.4 Results on independent data 

In order to test the validity of the pattern recognition 

algorithm, an independent data set made of 5 images with 204 samples 

per scene was assembled. Thus, the independent set has a total of 1020 

samples, all taken at 18 GMT in 1984. The actual dates are January 8 

and 22 and February 7, 8 and 12. 

An example of machine classification for January 8, 1984 at 18GMT 

is provided in Figs. 3.5a,b. We had the data for all days of January 

at 18 GMT and February at 16 GMT and 18 GMT on tape. Since the dependent set was derived from February, 16 GMT data, it was desirable to 

consider January data for the independent data set. Three of the days 

were, however, taken in February 1984 but those scenes differ in time 

by two hours from those of the training set. The reason for not taking 

all images of the test set in January was to provide a good representation of all cloud classes from only 5 images. For example, there were 
tu 

3Q) 

cG 

a -4 bc 

u 

&a 

OE 

ccr 
c, 

5 

a 

. 

m 

fd 
. 42 

no extensive areas of stratus in January (class 2 for which February 12 

is a 

for 

that 

set. 

good case) and no extensive areas of open cells (classes 9 and 10 

which February 7 and 8 are good cases). In addition, we recall 

.. 

only about one third of each image was selected for the training 

Considering those factors, the test set is considered to be at 

least 80% independent of the training set and all 1020 samples differ 

from those used in the training set. 

In addition to the author, two senior meteorologists worked 

on the verification to be presented here. Among other credentials, 

both researchers have had many years of experience analyzing digital 

visible and infrared GOES imagery. We refer to these consulting 

analysts as Analyst 1 and Analyst 2. Each analyst worked separately, 

following the procedure outlined here: 

1) The visible and infrared images are displayed on the video screen of 

MCIDAS. The grid of analysis with 204 grid boxes is superimposed on 

the images. 

2) The analysts have a hard copy of each of the 5 grids with the 

machine class number assigned by the algorithm in the middle of the 

box. They also have the worded description of the 20 classes and the 

examples presented in Section 2. The task is to examine both visible 

and infrared images for each sample and to correct the computer answer 

if need be. If they do find an error, the analysts are asked to 

provide two numbers: the corrected class number and the class number 

that they feel is second best. The purpose of the second number is to 

find out how many times the computer has selected the second best 
43 

choice. 

3) The information provided by each analyst is keyed in the computer 

to derive statistics. 

The results are presented in Tables 3.10 to 3.13. Table 3.10 

shows the overall accuracy inferred from each analyst. These results 

indicate that while the two consulting analysts agreed with an overall 

accuracy of about 73%. the author was estimating that same statistic 

considerably higher at 87%. Having designed the classifier, the 

author certainly had more experience with the particular choice of 

classes selected while the consulting analysts may have had a different 

view of what each class is or should be. It may be argued that the 

author is biassed towards agreeing with the machine result. This 

argument is partially dismissed upon noting that the author flagged the 

samples as incorrectly classified while the consulting analysts flagged 

them as correctly classified for 3.0% of the data set with respect to 

Analyst 1 and for 5.3% of the data set with respect to Analyst 2. More 

importanly, Table 3.11 indicates that the consulting analysts agreed on 

75.2% of the samples to be either right or wrong. Thus for 24.8% of 

the samples, one of them flagged the result as right while the other 

flagged it faulty. Obviously, this means that the machine result for 

those samples is either right or fairly close: if it is ultimately 

considered right, the accuracy inferred from the consulting analysts 

would rise to 83.8%. 

The indication that the machine result is virtually always close 

is provided by Table 3.12. For only 2.9% of the time, all 3 analysts 
Table 3.10 Overall accuracy (%) estimated by different, 

analysts for the 1020 samples of the independent data set, 

with and without considering cases where the machine 

selects the second best choice. 

Analyst Strictly correct At least 2nd best 

AUTHOR 

ANALYST 1 

ANALYST 2 

87.1 

73.1 

72.3 

95.6 

82.5 

81.3 

Table 3.11 Percentage of agreement between analysts on 

samples being correctly or incorrectly classified. 

agree agree overall 

correct incorrect agreement 

AUTHOR/ANALYST 1 68.1 9.9 78.0 

AUTHOR/ANALYST 2 65.0 7.6 72.6 

ANALYSTS 1 and 2 59.0 16.2 75.2 

Table 301% Percentage of the samples for which none, one, 

two or all three analysts agree that the machine result is 

right. Results given for strictly correct and for machine 

result at least second best among twenty classes. 

Number of analysts 0 1 2 3 

agreeing 

Stricly correct 6.1 14.8 24.2 54.9 

at least 2nd best 2.9 8.2 20.6 68.3 

Consensus: at least two analysts agree: 

Stricly correct: 79.1 

at least 2nd best: 88.9 
-Table 5/3: Consensus of correct classification for each 

class. Machine number of samples for each class given ’ next to the number of those samples for which at least two 

out of three analysts agree with the machine result. 

Resulting percentage of agreement is shown in right 

column . Class Machine number Number for which Percentage of 

of samples consensus agrees of agreement 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

22 

20 

90 

14 

41 

17 

78 

79 

78 

56 

102 

40 

9 

30 

24 

61 

72 

123 

22 

42 

22 

20 

89 

12 

39 

17 

72 

74 

60 

36 

77 

30 

4 

19 

19 

61 

71 

122 

22 

43. 

100 . 0 

100 . 0 

98.9 

85.7 

95.1 

100.0 

92.3 

93.7 

76.9 

64.3 

75.5 

75.0 

44.4 

63.3 

79.2 

100.0 

98.6 

99.2 

100.0 

97.6 
44 

agree that a sample is incorrectly classified and does not qualify as 

second best. This results matches the 97% theoretical separability 

obtained from Monte Carlo simulations. Table 3.12 describes a 

consensus that we present as our final estimate of the classifier 

accuracy: two out of three analysts agree that the overall accuracy is 

79.1%. If second best choices are considered as correct, the overall 

accuracy rises to 88.9%. It was previously admitted that the test set 

could have partial dependence with the training set. The results for 

the two January days (408 samples which are totally independent from 

the training set) were very similar to the numbers quoted above: 77.4% 

stricly correct and 86.7% at least second best. Thus. the results 

presented in Tables 3.10 to 3.13 are in all likelihood representative. 

/ 

Table 3.13 presents, class by class, the results of the 

classification based on this consensus adopted as a method of 

verification. It is seen that 12 of the 20 classes scored above 90%. 

Boundary layer classes, including stratus, streets and rolls are 

particularly well classified as well as high-level cloud classes; in 

particular, all thin cirrus (class 16) samples detected by the machine 

were confirmed by the analysts. The result for class 13 (44%) is not 

representative, being derived from only 9 samples (the result was 85% 

correct on the dependent set): most of the errors in class 13 are 

actual cirrus seen as single layered clouds at mid-level. There is a 

problem in overselecting class 10, convective open cells, when some 

small holes are present in a mid level overcast. For multilayered 

classes 14 and 15, an acknowledged problem is their selection when thin 
45 

cirrus overrides cumulus or stratocumulus. The machine interprets the 

higher deck as altocumulus because the apparent height is below 6 km 

and because the cloud albedo is higher due to the presence of the lower 

deck. The solution to this problem would probably require an additional spectral channel sensitive to the presence of ice crystals. 

The need for an objective method of classification is confirmed by 

examination of the agreement between the three analysts which is below 

79%. We stressed that this agreement is on a single number to be 

selected as right or wrong. In an experiment prior to the verification 

experiment described above, Analyst 1 and Analyst 2 were asked, for the 

same 1020 samples, to label the samples with a class number from 1 to 

20, without having the machine result as information (only the description of the classes and the catalog of four examples far each class 

shown in Garand (1986)). The agreement was surprisingly low: 37% on a 

strict basis (as opposed to 75% when they were given the machine 

answer), raising to 55% if their second choice was considered. This 

result shows once again, this time without leaving any doubt. that 

objective classification provides results that are more reproducible, 

less ambiguous than the subjective techniques used in the past. 

3.3.5 Computer time 

We now examine the computer time required to use the proposed 

feature extraction and classification scheme. It requires 2.5 s per 

box on a medium size computer (IBM 4381). Virtually all the time is 

devoted to feature extraction, the classification taking less than 0.1 

s. The most time consuming part, about 1.2 s (it varies with 
46 

' morphology), goes into the estimation of the connectivity indices as 

each cloud or background element is catalogued individually. The 

algorithm is not optimized for efficiency; the author gave only 

reasonable attention to this aspect. Yet, the 204 boxes of our 20 X 20 

degree domain can be classified in about 8.5 minutes. At the scale of 

(128 km) , the entire planet, land and ocean areas included (31130 

boxes), could be analyzed in 21 hours with the same computer, if the 

scheme was generalized for usage over land masses and for all seasons. 

Optimizing the code and using a larger computer, we hypothesize that a 

factor of 2 could be gained. Daytime only application leads to another 

reduction factor of two. Further, a gain by a factor of 3 to 4 would 

be obtained if it was shown that the results of the classification are 

not significantly deteriorated from using a resolution of 4 km in the 

visible as opposed to 2 km. Still, the task is costly for a single 

computer as maps are desirable every three hours. However, this task 

is perfectly suited for parallel processors since each box is 

considered independently from the others. We conclude that, with 

vectorized machines or cooperation between various satellite data 

centers, the method could satisfy the time requirements of ISCCP. 

3r 
47 

4. CLIMATOLOGICAL APPLICATIONS 

4.1 Cloud type climatology for January and February 1984 in twenty 

classes. 

Table 4.1 provides the number of occurrences of each cloud type 

for January and February 1984. Table 4.2 gives the means of conventional cloud parameters for the same period. Although the cloud fraction for the two months was virtually the same, 63%, January was 

characterized by a value of HI 6.9% higher, compensated by a LO value 

5.4% lower than in February. As a consequence, the mean value of HT is 

higher in January by 700 meters. The mean albedo is similar for the 

two months. Cumulonimbus cloud classes, 18 and 20, account for 25.1% 

of the samples in January whereas they account for only 16.5% in 

February. February was characterized by several cold air outbreaks, 

including a spectacular event that lasted four days between the 7th and 

the 10th. As a result, mesoscale cellular patterns (classes 8, 9, 10) 

are more numerous in February. Stratiis, class 2, tend to occur 

extensively or not at all; extensive stratus occurred February 12 and 

13 but no such cases happened in January resulting in a marked 

discrepancy for that class. Another remarkable result, which applies to 

both months, is that broken cumulus occurs less than 1% of the time as 

opposed to 11% for scattered cumulus. 

As will be shown in Part 2, each cloud class is associated with 

a different probability of precipitation. Having a scheme as detailed 

as this one permits more accuracy than using only global statistics 

<such as those provided in Table 4.2. 
Table qt/ Number of occurrences of each cloud type for 

January and February 1984, and resulting percentage of 

occurrence. 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

Total 

325 

20 

638 

29 

296 

48 

190 

123 

126 

53 

304 

182 

113 

195 

192 

662 

681 

906 

2 57 

576 

5916 

5.49 

0.34 

10.78 

0.49 

5.00 

0.81 

3.21 

2.08 

2.13 

0.90 

5.14 

3.08 

1.91 

3.30 

3.25 

11.19 

11.51 

15.31 

4.34 

9.74 

289 

141 

641 

50 

473 

184 

166 

141 

193 

112 

315 

295 

149 

210 

210 

412 

729 

601 

170 

364 

5845 

4.94 

2.41 

10 . 97 

0.86 

8.09 

3.15 

2.84 

2.41 

3.30 

1:92 

5.39 

5.05 

2.55 

3.59 

3.59 

7.05 

12.47 

10.28 

2.91 

6.23 

Table 4% 

1984 over the entire domain. 

Cloud parameter means for January and February 

Feature January February 

CF 0.638 0.630 

Lo 0.073 0.127 

MI 0.327 0.337 HI 0.239 0.170 

HT (km) 5.8 5.1 

AL 0.526 0.503 
Table g3a; Given a cloud type observed from ship, probability of presence of other cloud types in winter (Dec., 

Jan., Feb), for the oceanic region bounded by 30-45N 

latitude and 60-9OW longitude (after Hahn et al., 1982). 

Ac: altocumulus, As: altostratus, Ci: cirrus, Sc: stratocumulus, Cb: cumulonimbus, St: stratus, Cu: cumulus, Ns: 

nimbostratus. A X indicates a pair of types that are never 

reported together by observers. 

Given Probability of cooccurrence 

As/Ac Ns cu sc Cb None 

cs 60 12 22 44 6 18 

As/Ac 0.4 21 58 8 12 

Ns 1 63 2 23 

cu X X 57 

Sc/St X 40 

Cb 48 

Table q& Probability of occurrence of each loud type 

for same time period and region as in Table &l , and 

percentage of occurrence alone or not (after Hahn at al., 

1982) . 

Type % of occurrence alone multilayers 

. Clear 

Ci 

AS/AC 

Ns 

cu 

St/Sc 

Cb 

8 

29 

39 

9 

26 

50 

5 

5.2 23.8 

4.7 34.3 

2.1 6.9 

14.8 11.2 

20.0 30.0 

2.4 2.6 
48 

An attempt was made to compare our results to statistics of 

cloud type obtained from ships. Hahn et al. (1982) provided a comprehensive study of cooccurrence of cloud types over the ocean, based on 

twelve years of observations. Fortunately, one of their grid boxes 

represents the same region as the one studied here. Their results are 

condensed in Table 4.3. Among other questions, we are interested in 

knowing the percentage of multilayered systems. According to Table 

4.3b. that percentage is 42.8% since single layered cloud fields, 

including clear sky, occur 57.2% of the time. We assume that cloud 

classes 1 to 11 and 13 are single layered. From Table 4.3a, if we 

further assume that cirrus associated classes 16, 17 and 19 are multilayered 82% of the time, nimbostratus (class 12), 77% of the time and 

cumulonimbus classes (18 and 20) 52% of the time, then, by pooling the 

data for January and February together, we obtain that 41.1% of the 

cloud systems are multilayered. This result compares well with the 

climatological estimate of 42.8% based on ship observations. 

Other comparisons are directly possible. The clear class 

occurred 5% of the time versus 8% from ship climatology. Perhaps, the 

difference is the result of the scale of analysis, which is much larger 

from satellite (128 X 128 km) than from the ground (about 40 X 40 km). 

As another example, Hahn's data suggest that cumulus alone happens 

14.8% of the time whereas our limited data yield 14.5%, if classes 3, 4 

and 7 are pooled as cumulus-alone classes. A more relevant exercise 

would .be to compare directly coincident satellite versus ship-observed 

cloud classes, especially multilayered systems. For example, the data 
49 

for classes 14 and 15 suggest that altocumulus (Ac) occurs the same 

proportion of the time with either cumulus (Cu) or stratocumulus (Sc) 

whereas Table 4.3a reveals that the pair Ac/Sc occurs almost three 

times more often than the pair Ac/Cu. From such studies, one could 

fill in, statistically, what the satellite cannot see, namely what is 

underneath the higher cloud deck. 

4.2 Monthly , seasonal maps of cloud fraction 

4.2.1 Monthly maps 

Figs. 4.la and 4.1b show the mean cloud fraction for January and 

February 1984. The interpolation procedure is that of Hibbard and 

Wylie (1985) and the computer system used is MCIDAS (see Suomi et al., 

1983). The maps are produced from 29 satellite measurements for each of 

the contiguous 204 grid boxes. As seen in the previous section, the 

overall cloud fraction for the two months was the same. The detail 

shows noticeable differences, however. January is characterized by a 

marked north-south gradient, south of latitude 35 N, whereas no such 

feature occurs in February except in the south-east part of the domain. 

Both months have a strong maximum (above 80% ) in the latitudinal belt 

37-40 N south of Nova Scotia. January shows a second maximum of 83% off 

Cape Hatteras. The extended region of high cloud fraction in January 

indicates the mean track of the frontal systems that frequently 

occurred in that month. The lack of gradient of cloud fraction towards 

the tropics in February may be explained by the few cold air outbreak 

episodes that penetrated far south. 

4.2.2 Seasonal maps and comparison with ship data 
b 

Fig. ll.1 Satellite retrieved mean cloud fraction for: a) 

January 1984 (N = 5916) and b) February 1984 (N = 5845). 
50 

We hove compared our estimates of cloud fraction with ship 

observations. Figs. 4.2a and 4.2b are seasonal maps, with January and 

February pooled together, of cloud fraction obtained from ships and 

satellite respectively. These maps are produced from 1275 coincident 

ship reports and satellite retrievals at 18 GMT. The matching procedure 

is explained in Part 2. We recall that ground observers report the 

cloud cover in octas, that is with increments of 0.125. The results 

show good agreement in the general pattern and its details. However, 

the cloud fraction observed from ships is often 10 to 20% higher than 

the satellite estimate. It is a known fact that ground observers 

overestimate (often by one to two octas) cloud fraction due to angular 

perspective, espe.cially for low cloud fields with true cloud fraction 

in the range 30-70%. Minnis and Harrison (1984a) reported similar 

findings (see also references herein). 

Fig. 4.2~ presents the satellite retrieved cloud fraction for the 

two months using all the data (11,761 retrievals). While Figs 4.2b and 

4.2~ used the same method for the estimation of cloud fraction, Fig. 

4.2~ was produced with complete sampling: 9.2 times more data samples. 

The result is a smoother pattern where, in particular, the closed low 

near Bermuda vanishes. This result emphasizes the need for good 

sampling and resolution for a geophysical field as variable as cloud 

fraction. We have presented maps of cloud fraction (CF). Maps of 

other parameters of interest, such as LO, MI. HI, HT, Ts, Tb and AL are 

also available from the present method. 

c-3- 
I I 65W 

75w 

b 

7 OW 65W 75w 

Fig. (j1% Mean cloud fraction for January + February 1984. 

a) Observed from ships b) retrieved at the location of the 

ships (N = 1275, distributed over 32 grid points). 
C 

75w 65W 

Fig. q& Continued. c) Satellite retrieved mean cloud 

fraction for January + February 1984 using complete 

sampling (N = 11761, distributed over 204 grid points)# 
51 

5. SUMMARY AND CONCLUSION 

An automated algorithm to classify clouds in twenty classes has 

been ,presented. A novel aspect is the attention given to mesoscale 

cellular patterns: directional patterns such as cloud streets and rolls 

are detected (we believe for the first time) as well as open cells from 

features derived from the two-dimensional power spectrum of the visible 

images. Multilayered systems, including thin cirrus, are separated into 

several classes. In addition, cumulus, stratocumulus and stratus are 

specifically recognized. A consensus of three expert nephanalysts 

estimated the accuracy of the scheme at 79% with the machine answer at 

least second best among twenty classes 89% of the time. The classifier 

itself is very simple. A first stage tentatively assigns a class from 

a one-step decision rule that finds the class with highest probability 

of existence, given an eleven-dimensional vector of features. The 

second and last stage merely corrects possible errors of the first 

stage based on a small subset of features nost relevant io the class 

selected in the first stage. All the features bear physical meaning, 

can copies. 

The features provide a detailed description of the cloud field in terms 

of effective cloud fraction and albedo, height, multilayering, size 

distribution, shape, directionality strength, existence of holes, 

connectivity etc ... Thus, the classification is quantitative, that is, 

it does not merely provide a class number but it also provides a set a 

physical descriptors that can be further utilized. 

easily be interpreted with what can be seen from the hard 

A comparison was made of the climatologies of twenty cloud classes 
52 

for January and February 1984. Significant differences in the 

distribution of the cloud classes were found in the two months even 

though the mean cloud fraction and mean albedo were virtually 

identical. Moreover, the cloud fraction maps for the two months were 

significantly different despite, as mentioned, the same overall mean 

cloud fraction. 

The cloud classification algorithm should be applicable over all 

oceans uncovered by ice. Perhaps the classification is too detailed 

for use over the tropics where, for example, rolls and open cells are 

rare, but having too many classes is not a problem since related 

classes can be merged. 

The computer time, 2.5 s per 128 X 128 km box (on a medium size 

computer: IBM 4381) or 8.5 minutes for the 200 boxes of a 20 X 20 

degree region, may satisfy the requirements of ISCCP, especially if 

vectorized machines are used. 

The scheme will be used in the First ISCCP Regional Experinent 

(project FIRE, in 1987) to quantify marine stratocumulus fields off the 

coast of California. It is also intented to generalize the scheme for 

use over land. This will require regularly updated maps of surface 

albedo and knowledge of the surface temperature. 

The quantification of cloud fields in terms of fractional 

coverage, height and albedo is fundamental to the understanding of 

climate mechanisms. But cloud fields are also spatially organized in 

recognizable mesoscale morphologies which may be revealing of the 

physical state of the atmosphere. This aspect is the object of Part 2. 
53 

ACKNOWLEDGEMENTS 

The author thanks Dr. J. A. Weinman for countless scientific 

discussions and for revising this text. Drs. Don Wylie and David Martin 

were very helpful in their quality of expert nephanalysts. 

Atmospheric Environment Service of Canada and NASA grant NAG5480 

provided the necessary funding for this work. I thank the staff of the 

Space Science and Engineering Center (Madison, Wisconsin) and especially 

its director, Professor Verner Suomi, for all the resources which I had 

access. 

author’s Ph.D. dissertation (University of Wisconsin). 

The 

The material presented here was developed as a part of the 

APPENDIX. Covariance matrices of the features. 

Table A.l provides the normalized variances of features HI and CC, 

which may be used in Stage 2 of the classifier. 

normalized covariance matrices of the 11 features used in Stage 1. 

normalized means can be derived from Table 3.8 using Eq. (3.20) and the 

normalization constants of Table 3.2. 

Table A.2 provides the 

The 
Table h.1 Normalized variances for CC and HI. Some of 

these are used in Stage 2, with minimum value set to 

0.005. 

Class cc 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

1.257 

0.007 

0.137 

0.861 

0.465 

0.340 

1.239 

0.053 

0.638 

0.000 

0.219 

0.005 

1.325 

1.113 

0.567 

0.793' 

0.262 

0.004 

0.000 

0.000 

HI 

0.0000 

0.0000 

0.0000 

0 0 0000 

0.0000 

0 . 0000 

0.0000 

0 . 0000 

0.0000 

0.0000 

0.0000 

0.0000 

0.0001 

0.0002 

0.0002 

0.0812 

0.8612 

0.8244 

0.0467 

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54 

REFERENCE3 

Ageer E. M. and K. E. kwe! 19 i’i74: 35ser.vatisna! studi.es of 

mesoscale cellular convectionr J. Appl. Meteor., 13, 16-53. 

Bakany S.r 1985: On the structura of open rei!u!ar cowection as 

revealed by time series of surface observations: a case 

study, Beitr. Phys. Atrnosph., 567 11-15. 

Bates1 J. J. and W. L. Smith) 1985: Sea suriace temperatures: 

observat 

Re5. J 90 

aeniston, M. 

on5 irom geostat 

C61 11609-11618. 

1985: Organ i zat 

onary satell 

on of convect 

tzs 

on 

rnesoscal- model as a function of initial and 

conditicns, Beitr. Fhys. 4:mspn.~=a, 31-52. ,- 

J. Ceophys. 

n a numerical 

ower boundary 

Brard, 4.1 1930: Sur une analyse automatique simple des 

images d’Gn sate1 Iito motoarolcgicue geostationnaire, 

Ministere des Transports,Direction de la Meteorologie 

France, tech. ntte 77, hbuve I i o sor i e 9 46~~. 

Bunting, J. T. and R. F. Fournierj 1780: Tests of spectra 

cloud classification using DWF fine voce sato! lite data 

Environmental Reasearch Papers, no. 704, Air Force Geophys. 

Lab., AFGL-TR-BO-Olel, 4Z,-p. 

Desbois, M.3 G. Seze and G. Szejwach, 1982: 4utornatic classificetion oi clouds 3n PETEEAT imasery: ApFi on to highlevel clouds. J. Appl. Meteor., 21, b01-412 

Dudaj R. 0. and P. E. ?art9 i973: Fatt2t-n C 

Scene Analysis, John lriiiey 8 Sons, 1562~1. 

cat 

ass ficatior! and 
Forsythe, G. E.,M. A. Malcolm and C. 8. Moterg 1777: Cornputer methods far mathematical ccrnputatinnst :-renticeHall, 259~~. 

- 

Garand, L.J and J. A. bJeinrnan9 1986: A structurai-stc=hastir 

model +or the analysis and synthesis of cloud imagesj J. 

Climate Awl. Meteor., 25, 11152-1c368. 

Garand, L., J. A. Weinman and C. M. Moeller, 1987) Automated 

recngnition oi oceanic cloud pstterns. cart 2: application to 

remote sensing oi meteorological parameters, J. Climate Appl. 

Xetecr: 26, PO. 

Hahn, C. J.7 5. G. Warren, J. London, R. M. Chervin and R. 

Jenne, 1982: Atlas cf Sirnultanecus Occurronce =f Eifferent 

Cloud Types mer the Ocean) NCM Tech. Note T!+20ltSTR, 

21Zpp. (NTIS FB83-152074.) 

Haralickr R. M.) K. Shanmugarn and I. Dinstein, 1973: Textural 

features for image classification, IEEE Tt-aqs. Systems, Man) 

Cybernetics, 39 610-621. 

Hibbard, W. L. and D. F. Wylie~ i7E: An offirient vethod of 

interpolating observations to uniiormly soaced grids, 

Preprint volume oi Int. fen+. or: interzctivo Frocessing 

Systems ior Meteorologyt Oceanography, and fiydrzloo)), Los 

Angeles, CAS Jan 7-11, Amer. Petso-. %t.~ lU-iG7. 

Hceber, H. C.1 1982: KONTU? Convection a?d TurSuiexs Excorimcnt 

Field Phase Report, !-!amburger Gocphysiial isc:--o Einzelschri4- 
*- 56 

ten! Reihe BI Heft 1. 

Lcgan, T. L.! J. R. Hurting, J. R. and D. L. Slackin! 1983, Cloud cover 

typing from environmental satellite imagery; discriminating cloud 

structure with fast Fourier transforms, Jet Fropulsion Lab. 

publication 84-17, Fasadena Calif., 55pp. 

?artin, E. S.J G. Feter and J. P. Witkinson! 1965: 

3yrn~et-i~ decomposition of a positiwe c!sii,ite matrix, 

Nurorische Mathematikj 7, 362-383. 

Minnis, P., and E. F. Harrison! 198La: Diurnal variabiiity gf 

rzoiarai cioud and clear-sky radiative parameters derived 

from GOES data. Part 1: Analysis method. J. Climate Appl. 

Meteor.> 239 993-1011. 

---- ’ and --- 9 1984b: Diurnal warability of regional cloud and 

t!~ar-sky radiative parameters derived frm GOES data. !’art 

I: Ncvember 1978 cloud distributions! ibiaj liI12-1[371. 

----’ and --- 9 i9e4c: Diurnal variabi!it:J g4 regional 

c I oud and c I ear-sky rad i at i ve parameters de!- i ved from 

GOES data. Fart 111: November 1978 t*adiative 33rarneterss ibidy 1032-1051. 

hi? J. K.~ana K-S. Fu! 1980: Autcmateci rlas5i+;=stirn of 

nucleated blood cel Is using a binary tree risssifier, 

IEEi Trans. on Fattern Anairsis 2-c Y2cb. !q?e!l.y 2, 

429-413. 

Parikh, J.9 1977: A comparative study of rlcud r!sssifi=ation 

techniques! Remote Sensing of Environment, 69 67-81. 
, 

Platt, C. M. R.r 1983: On the b 

parameter deter-ination from 53toi 

broken cloud and semitransparent c 

429-439. 

, 

spectral method for clwd 

it= Vi%? data: Separating 

oudj J. Appl. Meteor.9 22, 

!kchard, G.7 1978: Sur quelques parametres simples de la texture 

nuageuse obseruee par satellite) Ministere de5 Transports, 

Direction de la Meteorologiej France7 techn. note 408, Z5pp. 

Ecsen+etdr A. and A. C. Kak, 19E2: Digital picture processing! 

2nd edition, Vol.1r Academic Press9 635pp. 

ROSSOWJ W. 6.9 F. Mosher) E. Kinsellar A. Arking? Pi. Desbois9 E. 

Harrison) P. Minnist E. Ruprecbt, G. Sezey C. Simmer and E. 

Smith9 198s: ISCcP cloud a!sorithm in torcomparison9 J. CIimste Pppl. Meteor.? 241 877-703. 

Schrnetzj J.r 1984: On the parameterizstion of the radiative 

properties oi broken clouds9 19RG9 Tollus, 36A9 4 17-432. 

Scheur, E. M. and D. S. Stoller) i962: On the generation ofnormal random numbers, Techmrretricst 6, 2713-291. 

Scorer I R. S. 1972: Clouds cf the WZI- Idr A Comclete Color 

r rncyclopedia, iothian Fhlishi~g Co Ltd~ Vo!bcurneJ Austrillia 

or David 8 Charles9 Newton Abbot9 UK9 176po. 

Scaror, R. S.9 lSE6: Cloud investigation by satel 

published by Ellis Horwood Liritedr distributed by Ha 

Press, a division ci John ljJi!oy and Scns~ no paging. 

i te) 

stod 

Shenk, W. E.J R. J. Holub and I?. A. Nef+J i976: 4 multispectral 

cloud type identification motkcd cevo!opec ccr tripical ocean 
I 

/ 

. 

---J 

CWSIfICATIOW OF SATELLITE IMAGES OF EXTRA-TROPICAL CYCLONES OVER THE 

OCEAN DY MANS OF COIPUTERIZED PATTERN AYUYSIS 

Ff,?.< : Craig R. Durfrlnd and Jrrrr A. ~ln8& 

QUALITY Deportment of Irtrorology 

.- -- __ Unirermlty of Ulrconrtn-Irdlron 53706 

I" introduction 

t 

f Currently, thoro rrr mrnv reglono 

ovrr tho oceonr. vhrrr no aurfrcr or 

upprr-oir dmto arm memrured by 

convrntionol arthod.. Although uppmr-oir 

trrprroture rnd hurldlty roundlngr cmn bm 

obtolnrd from lnfrrred rnd ricrovrvr 

mrorurrrentr from rmtrllltr-bornr 

radiomrtrrr. tho rurfacr prrmrurr 18 not 

rt prrornt king mrorurod operationally 

.UOlng thrar m.thod8. Accurote 

rurfrcr-prrrrurr rrorurr~eato are nredrd 

to obtoin 9ood wrthrr forrcortr. 

rmrign quontitmtlve prrmretrrr to 

lorge-mcolr vortlcro obrrrrrd in 

rmtelllte vrmther hrgl.. Troup and 

Strrten (1972) devlrrd r cyclonr 

clrooificotlon rcherr recording to 

devrlopmentrl at090 by meonr of an 

objectlrr ret of ruler oppliod to r 

rotrllite plcturr to clorrlfy the 

cyclonr. The rurfrce-prerrure fleld war 

rtatlrtically drrirmd umlng tho cyclonr 

clroo rad tho climatology for thrt clmrr. 

Guymrr (197a) ud both the lrr9e-rcolr 

rhopo and rroll-rcrlr fraturrr to drrivr 

uverrl D~tWrOhg~COl parmrrtrrr. Thim 

rnolyoir worn conducted bv r human 

rnolyrt. Such rnrlyrtr mry &me loam 

rttrntlrr after looking at many ruch 

rrtellitr picturrr. 

Attempt. hrvr been mado to ronumlly 

Yo proviour attaptr hove bean modo 

to OUtOBOtiC8ll~ ClOrOify CyClOnO 18090r 

or to drrlve rrtaorologicrl pmrrmrtrrm 

from ratrllitr irogro by mernr of 

corputrr anrlyrlo. Thio rtudy io r firrt 

rtterpt to ruto~otlcolly cleorlfy 

cycloneo over the ocron. Once on 

objretlvr tuhniqur for obtaining 

parmretrrr from the rhrpm. rizr. end 

oriontation lr devlred, 0 reqrrrmlon 

rchemo uring climatology can bo rppllmd 

to the dot. vlthout concorn for rrrorm 

induced by rub~rctlvr biarrr. 

prroento on outorotlc rmtrllitr-imrpr 

clmmrlflcotlon techalque thot plocem 

cycloneo into flvr clouer which rrr 

comporobfr to the five clrrrrr deflnrd by 

,Troup ond Strrten i1972) Cree Figure I). 

' The o~gorlthr prerentrd here 

clomrlfirr oeronic rxtrrtroplcal cyclonrr 

into on. of throe five C1orrer. hcotor 

tho cyclonr crntrr and tho mpproprlatm 

front.. and drrirrr a rot of objrctive 

pareroterr thet dercribr tho rizm. ahope 

and oriontation of tho rtorr ryrter. All 

thrr ir don. without manual lntrrvrntion. 

four rtrpr: 

1) Tho imago of tho eyclonr 10 rxtrectrd 

from the GOES hrrlrpherlc picture by 

rrenr of the IcIDAS lragc dlrploy rymtrm 

(Suorl ttl sL., 1983). Thir 1. 

done mo thmt onrlyrro con be done on only 

Thlr peprr 

The trchnlqur con ba dlvidmd into 

- 

1 

! 

I 

I 

I 

! 

! 

I 

I 

I 

I 

t 

! 

I 

Fw1:schemabc . diagram of the six doud pattern 

dassifcahnt defined by Tmq~ and Slreten 

(1972). 

lthr rxtrmtropicol cyclonr in qurotlon. 

hhlr prmdurr urrr the porltlon of tho 

124 hour LPX forumrt cyclone-center or on 

:epproximmte fndicotor to lnltlrtr the 

roarch for tho cyclono. 

2) The cyclonr ir plrcrd into one of five 

.Clm88.8. In order to do +hi=, Objrctlvr 

parrrrterr thot dercrlbr tho rppmerrncr 

of the ryrtrm ore colculotrd and urod to 

drtOrBlne the cyclonr type. 

3) Tho cantor of rototlon lr locatrd 

uring tho cyclonr clorr and the vlriblm 

lmmge. 

4) Tho front. aro located uolng the 

clorr. the cyclone center, and the 

,vlrlblm lrogr or input informotion. 

Fiftrrn carer ore rnolyzed in thir rtudy. 

Their tlrr of obrervotlon. locotion, ond 

orlglnol rpetiol rerolutlon are rurrmrlzed in Table 1. 

XI. Preprocerring of GOES Vlrlble ond 

Infrared Immgrr of Extretropicol Cycloner 

At ony given there arc numerous 

cyclone ryrtemr in vmrlour rteger of 

'-. --- 
i' L. 

91 

81 

61 

an I 

bl 

80 

u.u I 

an 0 

aor i 

~ 

I 

I 

I 

I 

I 

I 

I 

i 

i 

I 

i 

i 

! 

I 

I 

I 

i 

N 

I 

rl 

! 0 

N 

? I I; I - ---- --. 

davaloprmnt ovar tha manr. rlrpllfy thr analyrlr, the utratropical 

cyclone. arm exarlnad one et 0 tire. 

Thlr 18 dona by oxtractlag 0 8lnglm 

cyclona fror thr Urn GOES berlrphmric 

picture. Bueura image enaiyrir ir 

corDutatlonrllv lntmrlve. on. drrlrar to 

mnalytr en lrrgr which ham tbe fwrrt 

nurkr of plxrlr powlblr. 

Tha porltlon of the lou-prerrurr 

ernter prrdlcted by the 24 A tF?l forrcaat 

1. ured ar thr flrrt guerr to locate the 

cyclonr center. An area with dlrenrlonr 

of 5120 kr in the lrtltudlnal direction 

and 3840 kr in tha meridional direction 

vlth e pixel rerolution of 16 kr by 16 kr 

lr axtractrd fror Wa full-dirk vlrlbla 

and lnfrrrrd picture. to produca large. 

conrlrtlng of 320 pixrla in the errt-weat 

dlrrctlon. end 240 plxelr in thr 

north-mouth dlrrctlon. Thraa lragar. 

both vlrlbla and lnfrrrad. arm extrocted 

fro8 GOES plcturrr BO that the centrr of 

tha cyclonr la onr-third of thr dlrtanca 

fror the northrrn mdga to the routhern 

edgr and one-half of thr dlrtanca brtwean 

tha errtrrn and wrtern rdger of thr 

320 I 240 Image. There 320 X 240 pixel 

irager arm then remapped into a Hercator 

proyection with the arm number of 

plxalr. 

both tho virible and thr lnfrrrrd lragea 

to rerova lrrge rhrpa dlrtortlonr crured 

by thr location of the rtorr relatlvo to 

the GOES ratelllte locrtlon. Thlr alro 

ell or^ all plcturrr to ba anolyrrd ln tha 

ram projrctlon. which lr lrportant in 

In order to 

Thlr procedure lr eppllrd to 

the drrlvotlon of a rlnglm trchnlque for 

clrrrlfylng .torrs ot different 

letltuder. flnolly, the 320 X 240 plxrl 

image. are degraded by evrraglng over 

B X 6 plrrl boxme to produca i~mgee 

conelrtlng of 40 X 40 plxalr each hrvlng 

?ha elrr of rrttotroplcel cyclonea 

con vary ovu en order of ragnitudr: it 

ir. therefore. drrltoble to rcrlr all 

cyclone lrrgoa 80 that an lragr can be 

crratrd in which e11 porrlble cyclone. covrr rpproriratmly tha eara .ram in tha 

40 X 40 pixel imagr. to producr thlr 

image. rrOllmr cyclonmr arm onlergrd by e greater factor then Iergrr crclonrr. Tha 

0 reaO1UtiOn Of 128 kB bl 96 k8. 

;rrrorurron WAII m aszzerant for each 

!cyclonr plcturr. Thlr ere. norralizrtion 

!io importent oo tbat onlv onr 

clrrrlflcatioa and loertion rehero nmedr 

!to br davrloprd, lnotrrd of hrvlng on ,enelyri8 which dopeadm upon tha rlrr of 

tho norr.ll+atlon proerrr urar rrny 

the cyclona. 

dlgltal imago proea8rlng trchnlquar, 

.Including brlghtnau norrrlltrtion, 

thrarhholdlng to produca a blnory lraga. 

~prtlal flltorlng to rmducr noira. end 

extrocttoa fro8 thr original full 

rrrolutlon vlrlble Ira90 to produce thr 

norrallrmd image. Fro8 the norralitrd 

lmagr. tha onglr of orlentation, dong 

with varlour .Ire pmraraterr are 

drterriamd for the eyclona'r iroge. 

Brightnorm norrrlltotion la parforred to 

correct for airb drrkeniag produced at 

large ualu rrnitb mglrr. 

111. Claulfication of thr Dmgrer of 

Developmaat of ttretropicrl Cyclonrr 

Uring thr Vlalble Irrgr 

I 

Chr8ifiC8t1Olt of the cyclonr'e 

degree of drvrloprent lr aehleved by 

drtarrlnlng nvrrol featurrr of tho 

cyclona lmagar. whieb era thmn umed ea indlcatorr to cleuify the cyclone typa. 

In order to identify the propartier of 

thr eyclona lrrge. thr ualmd cyclone 

image murt b further rirpllfled. 

A duirion tree rtructurr tmee Figure 

2) la ured to clrulfy the rxtratropical 

eyclonrr. At arch nodr of the trre, one 

ferturr lr urrd to deterrlne whether the 

cyclonr fltr e crrtaln char. If ao. it 

ir clorrlflad and thr procedurr lr 

corpleta. Otherwire. the next featura lr 

rxtractrd and onothrr declrlon taker 

place . clarrlfy cyclonr ryrtarr lr the 

orlentation anglr of the rrjor axlr of 

thr elliptical approxiretion of the 

orlglnrl vlriblr lragr. The original 

lrrgr la fit to an elllpre ro thlr 

anrlyrrr can bo completed. 

orientation 1. takm to ba taro if the 

rayor rxlr of the rlliprr 10 oriented 

along an oart-vrrt llnc md 90 degrcer if 

thr cyclone'. rayor rxir lr along r 

north-mouth lhr. Thlr angle 1. urcd to 

datrrrlnr If 9 cyclonr lr In the wave 

rtrge opporrd to rn more developed 

cyclone. Cyclonrr vlth rrrller angler 

Thr flrrt frrturr which la n-dad to 

The angle of 

OHiGIi4RL PAGE IS 

.oE POOR QUALITY 
. 

a A 1 2 - 

/- I \ 

TYPEA TYPESB,C,Dy TYPESGOx 

I I 

A A 

Elongald left arm 

A 

. Figure a : Oecitisocl tree stmtum (or wing wries. ---~- -- are thur clrulfiod am wave-rtege 

cycloner (denoted Y). It la found 

erpirlcelly thrt 0 cutoff of 30 dogreer 

bort repereter urve-atage eyeloner from 

the othrr typo. of cycloner. 

The rrrrining feeturer are derlved 

fro8 en image which 11 a rubret of the 

rcrlod vlriblr irrge. A high-reeolutlon 

lrege which coneirlrta of the center core 

of the cyclone ayrtu la extracted from 

the original Ilercetor virlble h8ga. The 

center point and the alte of thir 

high-rerolution center 8eCtlOn le 

deterrlned from the centroid of the 

degraded rcrled image. Sirller 

degredatlon ir again rpplled to the 

rcaled lmege to produce r noire-free. 

binary image of the center rpirrlr of the 

ryrte8. Thia fine1 18.9e UHd for 

Cyclone C1ee8iflC8tiOn. 

The pattern-enalyrie elgorlthm 

rearcher for dry tongue. by looking for 

the firet continuour. horltontal rtrlng 

of plxelr with e velum of 200 on the meat 

edge of the plcturr. Fro8 thle 

continuour rtring. it rearcher twerde 

the wcet until it find. r clear ere.. It 

then continuer to reerch towardr the vert 

for e cloudy area end repertr the reme 

procedure egaln. each tire counting the 

nurber of pixel8 in the clear erer. Yhen 

e clear area la locetrd. the program 

rearcher towrrde the routh to deterrine 

vhethcr the cyclone her begun to vrep up. 

A cyclone 18 conridered to have vtrpped 

up if there ere et leert two pixelr 

extending toward the mart fro8 the 

routhuertern arm of the ryater. Thia 

procedure ylelda two feeturrr--the number 

of clear alota and en indication ar to 

uhethrr the cyclone her wrapped up. 

The binary imege ir ured to clerrlfy 

the cyclonrr whlch did not fall into the 

Y clrrr. bi looking lor the dry tongur. 

If no dry tongue lr prermnt. then the 

cyclone 1. clerrlfied typo A. If 

dry tongue ir preunt. the r~gorithr 

rerrcher to the oouth to deterrine 

uhrther the cyclone ha8 bmgun to urrp up or checkr for tw alotr of dry rlr 

betwrn the dgee of the entire cyclone, 

thur lndlcetlng thet the cyclone her 

wrapped around itaelf 0 ucond time. If 

elther of tham featurer are prrrent, the 

cyclone ir clearifled ra r type Cs 

othrfwlre. it Clerrlfled rr e type B. 

At thir point a11 cycloner which ern 

br claarlflrd 0. U, A, B. or C have born 

clerrified. Alro, the aIgOrith8 hrr 

reved crrtrin referencr pointr on the 

i809e for later ure in determining the 

cyclone center. In order to clrrrlfy the 

typre DX end Dy, the program 

checkr typmr B and C to roe if they are not rctuelly of type D. mince all type 

D'r vould have been included In the 

clarrification Of typo b or C up to thir 

point. 

Flrrt r cbeck ir rede to deterrine 

whether the wrrpped-around cloud band of 

e type B or C lr connected to the rain 

cloud brnd. The regrentd image ir urd 

to deterrlne if the cloud band ir 

continuour by comparing the label on thr 

center rplrel reglon vlth the lobe1 on 

the frontal band. If the lrbelr are the 

rrre. the cloud band ir eontinuour mnd 

unchanged. If the reglonr have different 

lrbolr. the cyclone ryrtem ir clarrlfled 

rr 0 typo k. If the center rpiral 

ir dirconnuted from the rain cloud band. 

it lr clarrified e8 a dlraipating 

cyclone. 

to deterrlne if the cloud bend to the 

welt. which her not wrepprd around 11 

elongeted. Thlr ir done by corperlng the 

1engthr.of the uertern err rnd the 

eartern err errocirted vlth the cold 

front . 

thr Cyclone Cf.r8lflC8tiOn ir left 

Secondly. type b ecloner are checked 

IV. Locetlon of the Cyclone Center 

A dlfferrnt technlqum for locotlng 

the cyclone center 1. urd for each of 

the five clerrer. Urlng pointr reved 

ipreviouely or etertlng locationr. the 

: progrem rovee predeterrined dlrtancer 

from there 10~etlOn8 to determine the 

i cyclone center. 

1 Thlr rectlon of the prograr taker ea 

.input the three locationr on the rpirelr: 

Ithe location of the routhwertern edge of 

! the dry tongue. the center of the 

:northern edge of the cloud band which ir 

.wrapped eround to the routh. and the 

vcrtern edge Of the cloud bend which her 

wrapped around e recond tire to the mart 

of the recond dry tongue. 

For type Y cycloner. the cyclone 
., 

b 

center 1. located vhere the thickert 

bulge le found in the bond of cloudlnaor. 

Or vherm the curvature Of the cloud bndr 

change. rlgn. In elthmr eoeo, thir $8 

where the cloud bond 1. thlckeot in the 

a.et-uort direction. The -me procedure 

that le urd for trpm Y eyeloner 11 elro . ured for tipa A cyclonrr. The only 

difformncm ir thot the clorrlficotlon of 

typm A cyclone urea the entire lmoge ond 

not only the high-rewlution center 

aoctlon. Tor typo 8 cyclonma, UIe point 

hcetod et the routhurrtern edge of the 

dry tongue ir urrd em rtrrting point 

for locating the cyclone center. Tbl. 

point 10 e100 urn for typo DY. It 

v0r empirlcolly deterrlned that tho 

center of the cyclone rhould k ploed et 

the point one pixel to the mouth of the 

polnt roved ln the above procedure. If 

the cyclone i8 determined to be 0 typo 

Dy, then the cloud band ir reorehod 

to find the eouthern-rort tip of the 

veetern cloud leg of the type Dy. It 

1. thlo point thot 10 given eo the 

cyclone center ln type Dy cyclonao. 

For type C cycloner, tw poroibilltieo 

exiot. The flrrt i8 thot the cyclone her 

only kgun to wrep rround to the .outhe 

but hor not rterted to form the r-nd 

dry tongue. The rocond 18 that it boo 

begun to wrap up. thuo forming e .rand 

.lot of dry air 00 the cyclone io 

troverred form root to wot. For the 

flrrt core. the rlgorithr begin. 

reorchlng from the point on the northern 

edge of the band vhlch hro vrapped to thm 

wuth touardo the northeort until it bar 

located the northeeotern-rort mdge of the 

cloud bend. The cyclone cmnter lr placed 

tw plrelr to the uort of thio point. 

vhich corrropondr to the wortu of the 

rplrelllng bend. In thm rmcond come, the 

cyclone center ir plod one pixel to thr 

north of the point which ir defined or 

the wertern edge of the cloud hnd vhleh 

ham begun to vrop oround r retond tlme to 

the emrt. Thlr once egeln plocea the 

center ot the vertex of the rpirolling 

cloud bend. If the cyclone io cloooified 

a0 e typo Dx. then the cloudy area clooert to the center of rotation lr 

locoted. Thio cloudy area ir 

dirconnectrd from the mein cloud 

otructurm oo indicetod above. The 

centroid of thir region ir determined and 

la taken to bo the cyclone center. 

V. Location of the Front 

The final enalyolo done by thio 

olgorithm io to roughly locate the cold 

front, or the occluded front. In the 

case of the typo U oyotem. the warm front 

io oleo loeatad. It rhould bo pointed 

out thot for dirrlpatlng cyclonao (type 

Der) no front lr located. 

The origlnol Hercrtor vlrible image 

ir the rtorting point ln the frontal 

rnalyrio. A binary image 10 creetad from 

thlo imaga. A filter lr placed over thir 

binary image to remove the noire and 

rough edger while retaining the ganerrl 

rhape of the frontal cloud bond.. The 

front io detrrmlnrd by movlng euay from 

the crntmr of rotetlon In prmdeterrined 

dlrutlone until the frontel cloud benbe 

rrm loceted. Tbeoe polntr ore rerked and 

roved. Aftmr all point. vhich roke up 

tho front ora l#otd, the front 1. 

rmoothod by plocing r running moon 

through the point.. 

VI. Reoul+. 

Tho relrtive ore. of the cyclone 

cloud tTrble 2) ahour no rtrong 

correlation to the type of cyclone. thir 

indlceter that any cyclone typa con vory 

in rlre. Tho welue of theto chongem er 

tho cyclone eoturea. Small rnglea are 

not- in developing cycloner mince the 

cold air lr etill to the north rnd hor 

not kgun to rvrep routhword to overtake 

thr vera air. Ih tbe cyclonrr develop. 

lergmr angler ere found ea the tilt of 

thm ryrtem kcomem *ore negetive due to 

the routhword rvuplng cold rlr. 

dercrlption of the cyclone ryrtrm. 

1 

2 

3 

4 

S 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

C 

C 

1) 

OX 

b 

C 

Y 

b 

A 

A 

Y 

DY 

OX 

DX 

B 

91-77 

540% 

34 -76 

92 77 

105.71 

¶3.% 

76-68 

82-43 

108oS2 

42 S7 

96.76 

104 27 

88.66 

79.64 

86 61 

1 .OO 

1.00 

1.00 

0.94 

0.99 

1.00 

1.00 

1.00 

1 .OO 

1.00 

1.00 

0.71 

0.93 

1.00 

a .oo 

67.1 

1390s 

97.3 

100.5 

74.4 

106.9 

33.6 

101.3 

75.6 

93.1 

33.0 

101 07 

74.2 

114.9 

94.7 

Trblo 2 : Cyclone cloorlficotion urd feoturer 

for arch dot. mat. 

I 

A comprrl-n of the reoulto of thir 

rtudy vlth the rerultr obtolnmd from 

huren rnolyrtr woo performed. Six 

metlorologirto. ell of whom hove had 

experience in enolyzlng rotelllte imagery 

or e rtrong rynoptlc meteorology 

bockground. melysed the 15 cooeo to 

locote the lor prmraure center end thm 

frontr. The ram lou preooure center 

locotlon end the rem front01 pooltion 

along vlth the otandard deviation for 

each of the 15 data retr vere colculetrd 

for the mix mnoly~o. 

thr cyclone anter found by the computer 

olgorlthr ond the mean center location 

found by the rnalyota. Along with there 

valuer. the rtendard deviation for the 

human onrlyotr ond the ratio of the 

I computer'r diotencr from the mean to the 

human enalyoto' rtandrrd deviation 10 i tabulated. Except for one very bod care I (data ret 4). the reoultr rhov that the 

coaputrr analyri8 ir within one rtrndard 

deviation holf of-the tlme ond elvayr 

Table 3 dioployo the dlrtoncr betvecn 

i 
. 

I 

b 

8ubpetlro 

btd. Do*. 

Drtr Sot tkm) 

1 177 

3 1Sl 

4 104 

3 276 

6 98 

7 178 

e 254 

9 444 

10 N/A 

12 167 

12 701 

13 70 

1 13 943 

i 

i 2 91 

----.--. -------- i 

I 

i 

1 14 202 

37 

1% 

103 

407 

141 

161 

258 

217 

614 

1/A 

136 

1086 

32 

101 

268 

art10 

Dat from In 

btd dev 

0.21 

1.70 

1.21 

0.51 

1.64 

1.45 

0.65 

1.38 

I/A 

0.73 

1.5s 

0.16 

a.31 

0.50 

0.78 

Trblo 3 : Comprrlron of rtrndard dovlrtlon of lou prrrruro 

contor poritlon for r group of human m~rlyrtr 

vrrrur dirtrncr from tho rean for tbr corputor 

algorithm. 

I . ._ t 

within tuo rtandrrd dovlrtionr. Thoom 

rrrultr rlro rhow that tho crrrr in which 

the eomputor rlporithr hrd difficulty in 

locrtlng tho crntor end the frontr, tho 

human rnrlyrtr rlro producad r vide rongr 

Tab10 4 giver tho rrmo prrrmotorr for 

tho frontal porltion. The corputor 

rlgorithm dld poorly on data wta 9 rnd 

15. uhllo the dirtrncor for tho romrining 

C~WI rro ~PprOXiD~tOly quo1 to on0 

rtandrrd drvlrtlon or lon. 

rhwrd tho poorort rmrultr for typo A 

CIClonOr, orpeclrlly data rot 9. Thlr 1. 

duo to tho lack of lrrgo-rrlo rtructurr 

mcountrrd in typm A eyclonor vhon 

eorparod to tho other typer. Since tho 

corputor nmedr rtructuro to urvo ma r 

guido in locating forturor of tho 

I 

Of rO8Ult.. 

Ovorrll. tho computmr algorithm 

uUOtrOplCrl @Yl+rB. It Val unrble t0 - --- I - 

perform em wl1 for thoro crror. Alro. 

if more type & cyclone. uould be analyzed 

and urrd rr trrlnlng rotr for tho 

computor algorithm, porformrnce mlght bo 

lmprovod. 

rnrlyrlr on type C yclonrr. Thlr ir 

mainly duo to tho rtructure rrrocirtrd 

with the rpirrlling contor of rotation. 

The rlgoritbr ir ab10 to locrto mrnv 

rofrreoco poiatr on the rpirrlling cloud 

band. to uro in idontifying tho lw 

prrrruro cmntrr. Typo C eyeloner, 

or~irlly data bot I. rlro rhov -11 

drflnod and nrrrw frontrl cloud brndr 

which mako tho rrrotlrtrd frontal 

porltion orry to locato. 

on an IBN AT-99 prrronrl computer 

oqulpped vltb an 60287 coprocorror chip 

and 512 kb of random rccorr momory. 1 

Tho corputor moomod to tiold tbm krt 

Thoro prttorn rnrlyrrr voro conductrd 

The - I 

Obpcti VI 

Dirt. from Morn 

(k.) 

44 

24 

60 

Y/A 

312 

91 

141 

41 

1273 

Y/A 

49 

*/A 

*/A 

YIA 

136 

--------------- 

Ratio 

Dirt from In 

Std drv ------------ 

0.31 

0.28 

1.07 

MIA 

2.50 

0.77 

0.93 

0.36 

7.49 

Y/A 

0.26 

*/A 

W/A 

W/A 

1 e77 

Tablo 4 : Comprrlron of rtrndrrd drvlrtion of frontal 

porition for group of human rnalymtr vorruo 

thr rvrrrgr dlrtmncr from the human frontrl 

poritlon moon for the corputrr algorithm. 

- -- - - -. - 
mxtmndmd -Turbo-Paual" roftuarm apt08 

uaa urmd for programming there 

algorlthmm. the ontirm analymlm took 

approximataly 3 minutrr from boginnlng to 

md for racb cyclonm. Yhllm thm prrronal 

computmr 10 in no ray ormrburdanod by 

thrn procedurmr, the analyaim could k 

minicomputmr oqulppod ulth a porallrl 

array procmuor. 

POrfOrBOd much DOTI rapidly On 

VII. SUBDOry 

An algorithm waa dmvmlopmd that 

objmctivmly analyard rynOptiC-aCal. 

aatmllitm iregea depicting vrriour rtogea 

of dmvmlopment of oxtratropieal cyclonma. 

Thm lrrgrr wrm analyzod to a88lgn 

category to thm cyclonr bared on itm 

.tag0 of drvrlopmmnt. OnCr Chuifimdr 

thm rotation cmntmr md the front. 

rrrociatod uitb thm cyclonm worm located. 

Thlr elaaaificatlon rchmme along with 

thm porrmmterr derivmd from thm utellite 

1DOg.r Day UltiDotm1y used to mDplriCol1y 

drrlvm aurface-mrearure fieldr. Thur. 

ithm aurfocr-prmmaure field could k 

derivrd from thm lrtga-acole fmrturar of 

the mxtratropical cyclone imagra. Tblr 

I uould be iirilar to thr uork.done by 

;mlther Yrgle and Hryden (1971) or Troup 

i 8nd Streten (1972). Thlr tmchniqum wuld 

,be mapmcially urmful owmr data-rparam . 

,rmgiona of thm ooana. whmrm littlm 

rurfacm-prmreurm data arm avallablm. 

Thir rtudy domonrtratma tbat pettun 

mnolyria tochnlqumr ray permit tbm 

devrloprent of rapidly mxecuted,. 

ObJrCtlVO CODpUtU-Orri8tod ana1Tw8 Of 

rrtrlllte ireger. Tbmom anolaywa ue 

able to dirtinguirh qclonrr la of 

their rtagea of devmlopment. 

rmrlized that not all cyclone rhapm8 ulll 

It la 

be 'onalytmd correctly,. 

cycloner arm analyzed, 

improved to aCCODodatm .- -- 

but am more 

thm program CIII be 

thr more 

- 

I 

I 

I 

I 

eomplicated cloud qhmpoa aaaociatmd uith 

veriour eyclonm ayatmmr. tbl8 ir only 

thm boginning of tbe gleaming' rtage for 

thm computmr algorithm. ft ir rvident 

that thm program can kcomm much morm 

prrclw ra mora utmllite tragmr arm 

rarlytmd 

REFERENCES 

Guymmr. LO., 197a: Opnational Application 

of brtellitm Imagmry to Synoptic 

Analyaia In thm Southern nmDl8phOrm. 

Tmchnical Report 029. CoDDOnwmlth Buroau 

of Irtooro1ogy, Imlbournm. AUtralla, 83 pp. 

Kmlly, C.A.H.. 1976: Intmrprmtatlon of 

Sotellltm Cloud lorricr for Southern 

Hemlrphmrm Analyria and Reforenee 

Vmsthmr Rm viw, a70-8as. 

Maglm R.E. and C.H. Rayden, 1971: Tbm 

Uro of Srtmllito-Obnrvd Cloud 

Pattmrnr 18 Nortbmrn ~mmlrphrrr 500-rb 

hmmrlcel Analyrir. MOAA Twhnical 

Report, NESS SS. U.S. Doportrent of 

kVm1 S~Cif&C.tiOD. 

. CODBIrCI. Uaahiagton D.C.. 2s pp. 

SUODi. oV-E* R. FOX. S.S. L&DOyr and Y.L. 

Smith, 1963:McIdoa 111: A Modern 

Interoctlvm Data Accerr and Anolyair 

SYrt- ~U€m~~C~sBA&~!

;u. PP. 166-7743. 

Troup A.J. md R.A. Strmtmn. 1972: 

Satmllttm Obsmrvod Southmrn Hamriphmrm 

Clwd Vorticmr in Rmlatlonahip to 

Conrrntional Obwrvationr, j-o~tll.L9f917. 

4lRRusd-lt PP= -- 

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4 

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