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major concern. Consequently, the observation of

the visibility is an important task that has to be

performed at regular intervals.

Traditionally, the visibility has been estimated

by a human observer. This procedure is still applied

for operational purposes today, e.g. at the GermanWeather Service (DWD) for visibilities 410 km.

One of the advantages of this method is the lack

of need for complicated and expensive instru-

ments, and, therefore, we nowadays have access to

long-term visibility observations at many different

sites.Lo hle (1941)has already found a relationship

between air pollution and the visibility in the

Rhine valley. Recently, for instance, Cheng et al.

(2005) identified a correlation in the increase

in aerosol load, decreasing visibility and the

decreasing precipitation trend in Southern China

since 1960.On the other hand, there are notable disadvan-

tages of the observation by a human observer. First,

this procedure is not automatable which leads to

high costs in the long-term and occasional data loss.

Second, the procedure is dependent on the indivi-

dual vision of the human observer, which causes a

limitation of the quality and the comparability of

the estimation. Middleton (1968), for example,

tested 1000 people, and he showed that the thresh-

old contrast varied from 0.01 to 0.20. The threshold

contrast is the minimum contrast an object has tooffer so that it can be still seen by an observer. He

followed that this clearly would lead to completely

different visibility estimations by these people in

comparison to the meteorological range with a

threshold contrast of 0.02. Therefore, a method is

desirable that is independent from human observa-

tions. There are some instruments that measure a

rather local extinction coefficient and derive the

visibility by extrapolation, as the telephotometer

(Horvath, 1981, 1995) or the transmissometer (e.g.

Hammond et al., 1995).

In this paper, we present a method that does not

extrapolate local data but integrates horizontally as

the human observations do. Our method determines

the visibility by processing digital photographs

taken by a customary panorama camera. In

contrast to Caimi et al. (2004), who suggested the

use of digital image sensors for the measurement of

the visibility by presenting a concept study, we used

the complete 3601 panoramic view. Different from

methods that use the colour difference method, as

by Kim and Kim (2005), our technique processes

greyscale images.

In the following sections, our method is described

in detail, and an application of the method at

Karlsruhe, Germany for a 6 month period is

presented. The quality of the determined visibilities

is quantified by a comparison with data from the

nearby station of the DWD.

2. Determination of the visibility

In the following text, our new method will be

abbreviated by VISIDIP which stands for VISIbi-

lity determined with DIgital Photographs. The

panorama camera that we have used for the

determination of the visibility is the Axis 2420

Network Camera. Every 10 min, it takes 20 digital

photographs within 2 min. Each single photograph

has a resolution of 704 576 pixels. By means

of the software of the company SeeTec a digitalpanoramic photograph with a resolution of

4826 576 pixels is automatically created using

these 20 single photographs. This image is saved as

a file in the jpg format on the hard disk of a

standard personal computer system. We have

chosen to develop a procedure based on the jpg

format because this format is usually used for

archiving images. Thus, the method can be easily

applied to yet existing time series of images at other

sites. Current panorama photographs at our site

can be found on the Internet (http://www.imk.uni-karlsruhe.de/english/seite_799.php). In the fol-

lowing subsections, the necessary procedures in-

cluding the calibration are described.

2.1. Measurement site and selection of visible targets

in the surrounding

As in the case of human visibility estimations,

first a set of visible targets in the surrounding area

of the measurement site with a wide range of

distances has to be selected before the automatic

method can be applied. The nearby ones are

buildings or parts of buildings situated at the urban

area of Karlsruhe in southwestern Germany.Fig. 1a

shows a section of the city map of Karlsruhe with

numbers giving the visible targets in the close-up

range and the position of the measurement site. The

measurement site is located on the roof of the so-

called Physikhochhaus, a building with a height of

approximately 60 m at the University of Karlsruhe,

at a height of about 180 m above sea level. The more

distant visible targets are mountains or parts of the

low mountain ranges (Fig. 1b). These are the Black

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D. Baumer et al. / Atmospheric Environment 42 (2008) 259326022594
http://www.imk.uni-karlsruhe.de/english/seite_799.phphttp://www.imk.uni-karlsruhe.de/english/seite_799.phphttp://www.imk.uni-karlsruhe.de/english/seite_799.phphttp://www.imk.uni-karlsruhe.de/english/seite_799.php

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Forest in the south and in the east, the Palatinate in

the west, the Kraichgau in the northeast, and the

Odenwald in the north. The number of the visible

targets is 39 in our case to provide a good

resolution. The selection of the visible targets has

to be carried out only once for each site.

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Fig. 1. Maps of the numbered visible targets in the close-up range at downtown Karlsruhe (a) and the surrounding area (b). The filled

circle in (a) gives the measurement site on top of the roof of a high building at the University of Karlsruhe.

D. Baumer et al. / Atmospheric Environment 42 (2008) 25932602 2595

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2.2. Conversion of the panoramic image to a

greyscale image

In Fig. 2, a flow chart of the method VISIDIP

(Versick, 2004) is presented. The panoramic image in

a resolution of 4826 576 pixels is processed. As afirst step, it is converted into a greyscale image simply

by averaging the single values for red, green, and

blue. The greyscale comprises values from 0 (black)

to 255 (white). The method presented here worked as

well with coloured images, but the image processing

had to be carried out three times which takes three

times longer but does not lead to better results.

2.3. Application of an edge detection algorithm

An edge detection algorithm is applied to the

greyscale image. We tested different operators,

namely the Sobel operator, the Prewitt operator,

the Roberts operator, and the Laplace operator(Sobel, 1970; Prewitt, 1970; Gonzalez and Woods,

1992). We obtained satisfying results with the Sobel

operator and therefore it is exclusively used in this

paper. The Sobel operator performs a 2-D spatial

gradient on an image and therefore emphasizes

regions of high spatial gradients that correspond to

edges. It is used to find the approximate absolute

gradient magnitude at each point in an input

greyscale image. It consists of a pair of 3 3 convo-

lution masks. The Sobel operator at a position

(x, y), |G(x, y)|, reads as follows:

Gx;y jPx1;y1 2Px1;y Px1;y1

Px1;y1 2Px1;y Px1;y1j

jPx1;y1 2Px;y1 Px1;y1

Px1;y1 2Px1;y Px1;y1j. 1

The Pi,j in Eq. (1) denote greyscale values of a

panorama image. This operator is applied pixel-wise

to the complete greyscale images with the exception

of the boundaries. This would require additional

assumptions, but since we are not interested in the

boundaries of the images and it is not reasonable todefine visible targets right there, they were excluded.

The output of the edge detection procedure is a

new image showing the positions of all the edges in

the original image.Fig. 3illustrates the effectiveness

of the Sobel operator under very good visibility

conditions. Fig. 3b shows the result of the Sobel

operator |G| applied to a greyscale image (Fig. 3a).

This image still contains different greyscale values

since the operator gives a form of horizontal

gradient of the greyscale image values.

2.4. Calibration

Under such clear conditions, the method was

calibrated by comparison with visibility estimations

by ourselves, which led to the definition of a

function F that applies a threshold value T to the

greyscale images after application of the Sobel

operator:

F 1 if Gj jXT;

0 if Gj jo

T:( (2)

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Fig. 2. Flow chart for the new method VISIDIP.


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Fig. 3. Example of the image processing under good visibility conditions. Greyscale image of an original display detail of a digital

photograph (a), the result of the Sobel operator application without a threshold value (b), the related functionF(Eq. (2)) with a threshold

value of 20 (c), and same as (c) but with a threshold value of 100 (d).


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The threshold separates the edges from the

background noise. Fig. 3c and d give the resulting

images of the function F with threshold values

T 20 and 100, respectively. These images solely

contain binary information, meaning that a pixel is

black (F 1) or white (F 0) depending onwhether the value inFig. 3bis higher or lower than

the respective threshold value according to Eq. (2),

but it possesses no greyscale value anymore. For our

purposes, the threshold value T 20 (Fig. 3c)

produced the best results over a wide range of

different visibilities in comparison to human ob-

servations and therefore this value is used in the

VISIDIP method, whereas a threshold value

T 100 (Fig. 3d) did not provide enough details.

This threshold value might depend on the type of

the camera but has to be determined only once at

each site.Fig. 4 demonstrates the application of the Sobel

operator and the threshold value under hazy

conditions. At that time, the visibility was 5 km.

2.5. Comparison of the edge image and the

visible targets

The next step is the comparison of the output of

the edge detection procedure with the assigned

visible targets, starting with the nearest target

(see flow chart inFig. 2). In doing so, it is checked

if there are detected edges at the known positions of

the targets. The nearer targets comprise up to some

10 pixels, while the more remote targets only a few

pixels, which is a consequence of the relative size of

the targets with regard to their distance. A toleranceof three pixels in each direction is allowed to take

account for a measurement inaccuracy that could be

caused, for instance, by vibrations at high wind

speeds or thermal expansion. This tolerance is an

empirical value and has resulted from comparing

automatic and manual visibility observations. If

there are any pixels at the same position in the

comparison, the target is classified as successfully

detected. If the nearest target is detected success-

fully, it is attempted to detect the second nearest

target, and so forth. If a target is not detected, first it

is checked if the sun is shining into the camera. Thisis done using formulae for the elevation and the

azimuth of the current position of the sun according

to Stull (2000). This is indispensable since the

aperture is automatically decreased when the

camera looks towards the sun. This effect would

lead to determined visibilities that will be too low if

it is ignored. If the distance between the target and

the sun is o700 pixels and the sun elevation is

o501, the target is not taken into account. This

procedure resulted from empirical testing. If the sun

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Fig. 4. Example of the image processing under hazy conditions. Greyscale image of an original display detail of a digital photograph (a),

and the related function F(Eq. (2)) with a threshold value of 20 (b).


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elevation is higher than 501, it is not necessary to

check the exact sun position because it is not close

to any target. If the target could not be detected and

the sun was shining into the camera, the algorithm

jumps to the next target. If the target could not be

detected and there was no influence of the sun, thevisibility is set to the distance of the last identified

previous target. The more remote targets are not

tried to be detected anymore in this case, since the

goal is to rather find the minimum visibility in the

surrounding than the maximum visibility. If even

the last target in a distance of about 60km is

identified successfully, the visibility is set to 70 km,

meaning that the visibility is approximately 70 km

or more, which is also the highest visibility class

used by the German Weather Service. Finally, the

determined visibility is saved, and the next image

can be processed (Section 2.2). The completeprocedure is fast enough to be carried out in real-

time operation. The temporal resolution of 10 min

used in this study can be reduced to 5 min if desired

since the complete procedure takes o5 min.

3. Results

In this section, several results of VISIDIP and

comparisons of visibilities observed by the German

Weather Service (DWD) are discussed. The DWD

station Karlsruhe (WMO-No. 10727) is located at adistance of about 5 km north-west of the panorama

camera in a suburban area. For visibilities below

10 km, a Videograph III measuring instrument is

used by the DWD which derives visibilities from

backscatter measurements. The measurement height

in this case is 2 m above the ground. If the visibility

is greater than 10 km, this measuring technique is

not appropriate since there is not enough back-

scattered light for a satisfactory determination of

the extinction coefficient and the visibility based on

that. In that case, the visibility is estimated by a

human observer, located at a height of approxi-

mately 30 m above the ground. The human observer

of this DWD station uses 28 visible targets. In

comparison to VISIDIP, the combination of two

completely different methods is less consistent,

particularly as low visibilities are derived from

extrapolations of a local measurement in contrast

to the real visibility observation during high

visibilities. Additionally, the estimation of the

visibility by a human observer is not completely

objective because it is dependent on the individual

vision of the observer.

All the observations presented in this feasibility

study were carried out during a 6-month observa-

tion period from January 2004 to June 2004. For

this period, both the VISIDIP and the DWD

visibilities were available as hourly values during

daytime.The complete datasets of hourly values for both

methods are shown inFig. 5. Since the discretisation

of both datasets is different as a consequence of the

use of different visible targets, we averaged both

datasets in 13 visibility classes. The bars indicate

95% confidence levels. Since some of the classes

comprise only one discrete VISIDIP visibility, no

confidence level can be given in these cases. It can be

seen that deviations from the exact agreement

appear in both directions. The highest differences

occur for very low and very high visibilities. In the

latter case, the VISIDIP visibility is on averagegreater than the DWD visibility. The greater

observation height of the VISIDP site (60 m) in

comparison to the DWD station (30 m) possibly can

explain at least a fraction of this deviation, because

high relative humidity in the morning causing mist

or even fog is often limited to a shallow layer near

the ground. For low visibilities, the greatest relative

deviations between both methods are found. To

examine the reasons for this, Table 1 lists a

comparison of both sets for DWD visibilities

45km but o10km, and 410 km. As describedabove, the DWD observations were carried out

using a measuring instrument for visibilities

o10 km, but human observations were performed

for visibilities 410 km. Therefore, the data series

were compared separately for DWD visibilities

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Fig. 5. Scatter diagram of DWD visibilities versus VISIDIP

visibilities for the whole measurement period JanuaryJune 2004.

The bars indicate 95% confidence level where available.


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o10 km and 410 km, respectively. The lower limit

of 5 km is chosen because of the distance of 5 km

between the both measurement sites. The correla-

tion is low (0.40) for low visibilities, but high

(0.73) for high visibilities. The lower correlation in

case of the low visibilities is possibly caused by

horizontal inhomogeneities of atmospheric consti-

tuents over the horizontal distance of the twostations to a certain amount. Consequently, the

mean values are in a good agreement for higher

visibilities, but not in the case of low visibilities. In

order to explain the differences between the

VISIDIP and the DWD visibilities, we analysed

both data series dependent on the current weather

situation as reported by the DWD station Karlsruhe

(Table 2). As expected, different kinds of precipita-

tion events reduce the visibility considerably,

especially in the cases of snowfall or drizzle.

Snowfall leads to a low correlation between both

series, possibly because of the highly variable shape

and size of the snowflakes. During fog events, the

correlation is very low and the average visibilities

are quite different. The local environment is

different at the DWD station near the city limit

which can lead to a different occurrence of fog close

to the ground, the more so as the measurement

height for low visibilities is just 2 m above the

ground in contrast to the VISIDIP measurements at

a height of 60 m. Therefore, at least a considerable

fraction of the deviations between both methods in

the case of low visibilities is caused by locally

varying weather conditions as, e.g. fog or precipita-

tion events.

Fig. 6agives an overview of the monthly means of

the complete datasets. The monthly averaged

visibilities of both methods are in a quite good

agreement. In every month, the VISIDIP values are

marginally higher than the DWD values. In general,

the visibility is higher from April until June, whenspring and early summer proceed, than in the winter

months, JanuaryMarch. This is consistent, for

example, with the results for Vienna presented by

Horvath (1995).

Another aspect that we examined in detail is

if the sun elevation plays a role regarding the

visibilities obtained with both methods. Fig. 6b

shows the average VISIDIP and DWD visibilities as

a function of the sun elevation angles. The

differences between the two visibilities decrease

when the sun elevation increases. This indicates

that the determination of visibilities can be more

difficult in general, when the sun elevation is low

because of dazzling effects. This is valid for both the

human observer and the automatic procedure

VISIDIP that also uses lenses. It should be addi-

tionally mentioned that the sun elevation angle is

not statistically independent from the weather

situation in principle, since, for example, fog usually

does not occur when the sun elevation is high.

Therefore, the complete deviations shown inFig. 6b

cannot be attributed to the effect of different sun

elevation angles.

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Table 1

VISIDIP and DWD visibilities and their standard deviations, separately for DWD visibilities 45km and o10km, and 410 km,

respectively

Visibility DWD (km) Number of data Average visibility VISIDIP (km) Average visibility DWD (km) Correlation

510 228 10.92713.23 7.8571.69 0.40

410 1303 32.25717.90 30.91713.69 0.73

Table 2

VISIDIP and DWD visibilities and their standard deviations for different weather situations

Weather at the DWD

station (if available)

Number of data Average visibility

VISIDIP (km)

Average visibility

DWD (km)

Correlation

Shower 10 29.03718.39 24.10711.69 0.82

Snowfall 39 5.9875.63 6.5874.18 0.39

Rain 49 17.65715.55 17.42710.42 0.63

Drizzle 2 11.00 4.60

Fog 7 6.3176.86 1.5971.69 0.11

No weather pattern 1123 31.04718.28 30.03714.39 0.77


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4. Conclusions

This feasibility study presents a new method

(VISIDIP) that determines the visibility in the

atmosphere based on an automatic processing of

digital photographs taken by a standard panorama

camera. A comparison with alternatively measured

visibilities at a nearby DWD station proves the

sound applicability of the method. Over a wide

range of visibilities, the values of both methods

agreed quite well. Differences between both meth-

ods could be partly explained with local effects as,

e.g. fog or precipitation events and the different

measurement strategies and heights of both meth-

ods. Dazzling effects during low sun elevations are

taken into account by our procedure, and we could

show that an overestimation of the visibility in such

cases is limited to a few kilometres at maximum on

average. Nowadays, panorama cameras are widely

spread anyway, and they can be successfully used

for this task without any modifications of the

hardware. Naturally, the panorama cameras are

set up at appropriate locations with a good

panoramic view, offering a view to various potential

visible targets in many cases, as we suppose. The

prize of a panorama camera is rather low in

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0

5

10

15

20

25

30

35

40

0

5

10

15

20

25

30

35

40

Visibilityinkm

Visibilityinkm

January February March April May June

VISIDIP

DWD

Below 10 10 to 20 20 to 30 30 to 40 40 to 50 Above 50

Sun elevation angle

VISIDIP

DWD

Fig. 6. DWD visibilities and VISIDIP visibilities as monthly averages for JanuaryJune 2004 (a), and depending on the sun elevation

angle (b), respectively.


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comparison to other measurement devices that

measure local extinction coefficients and extrapolate

the visibility. VISIDIP is a completely objective

method that can be applied in an automatic manner

without the requirement of a human observer, and it

measures real visibilities without the need for anyextrapolation at the same time. Airports, for

instance, can use the technique to complement

current transmissometer measurements successfully

and to avoid errors regarding the spatial representa-

tiveness that can be caused by these extrapolations.

Using this method can help to monitor the air

quality since the visibility is a good indicator,

especially for the aerosol content of the atmosphere.

Another example of use of this technique is the

possibility to identify long-term changes of the

visibility that are supposed to appear in the wake of

changes in the aerosol content. This is an importantpoint in the continuous monitoring of the air

quality, especially when the success of air pollution

mitigation measures is to be evaluated. Further-

more, also the short-term changes of the visibility,

such as air mass exchanges or the formation of fog,

can be indicated and investigated. For this task, the

technique presented here is appropriate since the

temporal resolution is 10 min in contrast to human

estimations that are usually carried out once an

hour. The temporal resolution can be even im-

proved to 5 or less minutes for many panoramacameras. Therefore, the method can be used for

meteorological case studies such as comparisons

with model results, but as well for safety-relevant

fields of application in aviation or ground-based

transportation.

Acknowledgements

The authors thank G. Bru ckel for the main-

tenance of the panorama camera and his coopera-

tiveness.

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