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This article was downloaded by: [Umeå University Library]On: 18 November 2014, At: 10:58Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Cloud mapping with ground‐basedphotogrammetric camerasG. Seiz a b , J. Shields c , U. Feister d , E. P. Baltsavias a & A.Gruen aa Institute of Geodesy and Photogrammetry , Swiss FederalInstitute of Technology ETH , ETH‐Hoenggerberg , 8093 Zürich,Switzerlandb Federal Ortice of Meteorology and Climatology MeteoSwiss ,Kraehbuehlstrasse 58, 8044 Zürich, Switzerlandc Scripps Institution of Oceanography (SIO) , University ofCalifornia , San Diego (UCSD) , 9500 Gilman Dr., La Jolla,California 92093‐0701, USAd German Weather Service (DWD) , Meteorological ObservatoryLindenberg , Am Observatorium 12, 15848 Lindenberg, GermanyPublished online: 16 May 2007.
To cite this article: G. Seiz , J. Shields , U. Feister , E. P. Baltsavias & A. Gruen (2007) Cloudmapping with ground‐based photogrammetric cameras, International Journal of Remote Sensing,28:9, 2001-2032, DOI: 10.1080/01431160600641822
To link to this article: http://dx.doi.org/10.1080/01431160600641822
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Cloud mapping with ground-based photogrammetric cameras
G. SEIZ*{{, J. SHIELDS§, U. FEISTER", E. P. BALTSAVIAS{ and A. GRUEN{{Institute of Geodesy and Photogrammetry, Swiss Federal Institute of Technology ETH,
ETH-Hoenggerberg, 8093 Zurich, Switzerland
{Federal Ortice of Meteorology and Climatology MeteoSwiss, Kraehbuehlstrasse 58,
8044 Zurich, Switzerland
§Scripps Institution of Oceanography (SIO), University of California, San Diego
(UCSD), 9500 Gilman Dr., La Jolla, California 92093-0701, USA
"German Weather Service (DWD), Meteorological Observatory Lindenberg, Am
Observatorium 12, 15848 Lindenberg, Germany
Ground-based digital imager systems in the visible and near infrared region of
the solar spectrum have the potential to nicely complement existing instruments
and observation networks of National Weather Services with very accurate, high
spatial and temporal resolution, 2D and 3D macroscopic cloud data such as
cloud amount, cloud-base height and 3D cloud-base motion. This paper discusses
two current approaches to ground-based cloud sensing: the prototype instrument
used at ETH/MeteoSwiss within Cloudmap and Cloudmap2 for stereoscopy
tests, and a Daylight Visible/NIR Whole Sky Imager (WSI) system developed
and fielded by the Scripps Institution of Oceanography (SIO). The article
includes descriptions of the radiometric and geometric calibration methods.
Cloud amount, cloud-base height and cloud-base motion results from two ETH/
MeteoSwiss measurement campaigns and from the operational WSI use at the
German Weather Service (DWD) are shown. Finally, a case study with
coincident satellite and ground data illustrates that ground-based digital imager
systems are an interesting technique to validate satellite-based cloud-top heights
and cloud-top motion winds of vertically thin clouds.
1. Introduction
Clouds play a pivotal role in the interaction between the Earth’s climate andanthropogenic inputs. The Intergovernmental Panel on Climate Change (IPCC) has
highlighted the large scientific uncertainty associated with the role of clouds either to
accelerate or mitigate effects associated with increases in greenhouse gases. For a
detailed assessment, accurate global measurements of the relative location,
distribution and character of clouds (which have a strong impact on both the total
incoming radiation at the surface and the reflected radiation above the cloud field)
are necessary as described in the rationales of the EU projects Cloudmap and
Cloudmap2 (Cloudmap 2001, Cloudmap2 2001). For acquiring global coverage,satellite-based methods are used, but they benefit from calibration and validation
with ground-based measurements. In addition, ground-based sky imagery can
provide high-resolution images with a much higher sampling rate of less than
1 minute per image than present day satellite-based imagers and conventional cloud
*Corresponding author. Email: [email protected]
International Journal of Remote Sensing
Vol. 28, No. 9, 10 May 2007, 2001–2032
International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2007 Taylor & Francis
http://www.tandf.co.uk/journalsDOI: 10.1080/01431160600641822
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observations. Due to the high variability of cloud cover and cloud optical depth in
time, both transmitted solar radiation and infrared atmospheric as well as terrestrial
radiation can vary in short time scales. Therefore, small uncertainties in time
integrals and averages of cloud parameters over time would also require small
sampling time steps in acquiring the individual data (e.g. Feister et al. 2003).
Ground-based cloud observations can also provide valuable information needed to
interpret and analyse solar irradiance measurements in different spectral regions
(e.g. Feister and Gericke 1998, Vasaras et al. 2001).
However, systematic and accurate ground-based measurements of the 3D cloud
field at high spatial and temporal resolution are not generally available from the
existing observation networks. At most climate stations of the national networks,
cloud macroscopic properties – mainly cloud cover, cloud type(s) and cloud-base
height – are still visually observed. Only at airports and some specific measurement
sites are automated weather sensors, ceilometers and more sophisticated instruments
(e.g. cloud radar, raman lidar) used to determine some cloud parameters
automatically and continuously.
To date, cameras and photogrammetric methods are rarely used within the
ground-based observational networks. Recent developments in the digital camera
market, including lower prices and larger image formats, are leading at the National
Weather Services (e.g. MeteoSwiss, German Weather Service DWD) to a revival of
the idea of photogrammetric cloud observation station networks, which was already
in discussion more than 100 years ago during the International Cloud Year 1896/
1897 (Koppe 1896). Operational use of analog images was not practical for most
applications due to the enormous amount of time necessary to analyse a single time
step by first scanning stereo image pairs with a photogrammetric scanner and then
finding corresponding points in the images by manual measurements. The new
digital systems have the major advantage of reducing significantly processing time
down to minutes, which gives them the potential of deriving cloud parameters in
near-realtime. Using fisheye lenses to image the full sky down to the horizon or
near-horizon, they have the potential to provide far more accurate measurements of
cloud fraction and distribution than e.g. ceilometers, because they acquire data over
the full sky simultaneously. For stereo camera systems, automatic, faster and more
reliable matching methods to solve the correspondence problem are a second
advantage. Finally, there is a qualitative advantage of data from a ground-based
photogrammetric system: these data are easier to interpret visually for a forecaster
and to link with the current synoptic situation than the point measurements from
active remote sensing instruments like ceilometers, lidars and radars.
In this paper, we demonstrate how the data from ground-based digital imager
systems in the visible and near infrared region of the solar spectrum system would
nicely complement existing observations of macroscopic cloud parameters, with
high spatial and temporal resolution data in 2D and 3D. Thereby, two research
efforts are discussed in detail: (1) the ETH prototype stereoscopic system for
determining the 3D cloud-base geometry and motion, and (2) the Whole Sky Imager
(WSI) systems developed at Scripps Institution of Oceanography (SIO), operation-
ally working in several design versions at several measurement sites world-wide since
the 1980s. These WSI systems are currently used for high spatial and temporal
resolution determination of cloud amount and type, and can potentially be used in
the future for determination of cloud optical thickness and, in stereo configuration,
3D cloud-base geometry and motion.
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After an overview of the ETH prototype system and the WSI system in §2, the
radiometric and geometric calibrations of the two systems are described in §3. These
calibrations are an important prerequisite for obtaining accurate cloud parameters.
In §4, the retrieval methodologies for cloud amount, cloud type, cloud-base height,
cloud-base motion and cloud optical thickness are explained. In §5, the results of
these cloud parameters from two ETH/MeteoSwiss measurement campaigns and
from the operational WSI use at DWD are shown.
2. Camera and system requirements for cloud observation
The choice of the camera and other sensor hardware is crucial and depends in large
part on the desired features of the final system. This section will discuss two
photogrammetric imaging systems. The ETH prototype system was designed as apreliminary research system that would provide adequate image quality over a
moderate field of view, in order to evaluate stereoscopy algorithm approaches. The
WSI systems were designed to respond to a number of applications; for this article,
our primary interest is in its ability to operationally determine the 2D cloud
distribution (cloud amount and type) over the full sky.
2.1 ETH research system
The ETH camera system (figure 1) consists of a color digital CCD camera, currently
a Fujifilm S1 Pro, connected to a laptop computer with precise time information
from a GPS receiver or radio clock. For our first prototype, which was used during
the MAP campaign in October 1999, the KODAK DCS460 camera was used. A
main reason for having switched to the Fujifilm S1 Pro camera for the
Figure 1. ETH camera system.
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measurements at Zurich-Kloten airport in April 2002 was the fact that the KODAK
DCS460 camera unfortunately did not fulfill the stability requirements (a small
movement of the chip occurred during the MAP measurement campaign and was
possible because the chip is only attached along one chip border). The camera is
protected within a heated box (1–2uC above air temperature to avoid condensation
on image sensor) that is mounted on a tripod. Three leveling screws at the bottom of
the camera housing allow precise horizontal adjustment of the camera image plane.
Approximate adjustment of the camera azimuth parallel to the baseline was done
via a small telescope as the different cameras are always within sight of each other. A
more precise relative adjustment was then done by taking simultaneous sample
images with both cameras, overlaying the two images in an image processing
software and determining any residual rotation angle. Attached to the camera box is
a moving sun occultor device to prevent blooming effects caused by the sun. A
Nikon 18 mm wide-angle lens with a nominal viewing angle of 100u was used. As the
system is a prototype for research campaigns, the mechanical details of the system
can still be improved. Currently, an extension is in development to close a cap over
the camera lens automatically if it starts to rain/snow. Under these conditions, no
measurements are possible with this system.
The CCD array of the KODAK DCS460 is a KAF-6300 with 307262048 pixels,
each 969 mm2, with a Bayer colour filter (Bayer 1976). In the KODAK image
processing software the 6 rows and columns around the edge of the array are
discarded. The RGB values of the remaining 306062036 pixels are calculated with
the KODAK proprietary Active Interpolation algorithm from the red, green and
blue filter values (Adams et al. 1998). The dark current noise of this sensor is quite
substantial (10pA/cm2 at 25uC) and especially influences the long exposure night
images used for exterior orientation determination with stars (see §3). Therefore,
images were taken at various exposure times between 0.002 and 240 seconds with the
lens cap closed for each camera to analyse the dark current noise. It was shown that
the dark current noise is camera-dependant, spatially variable, temporally stable and
increases with longer exposure times.
The Fujifilm S1 Pro CCD array is a SuperCCD with 3.4 million octagonal pixels
and a special color filter as described in Tamayama et al. (2000). In the proprietary
Fujifilm processing software the octagonal color array counts are interpolated to
304062016 square pixels with a pixel size of 7.5 mm. The new CCD design attempts
to increase sensitivity, dynamic range, signal-to-noise ratio and image quality. The
30 s night images show much less noise than the Kodak DCS460 images; stars can be
extracted directly from the 30 s images without any previous flat field subtraction.
To obtain exposure times of up to 150 s, the camera offers a feature of taking a
sequence of up to five images without storage delay (temporal storage array) that
can then be summed up and clipped above the maximum grey value by image
processing software.
2.2 Daytime VIS/NIR WSI
Digital fully automated Whole Sky Imagers were developed by Scripps Institution of
Oceanography (SIO) at the University of California, San Diego beginning in the
early 1980s. Seven of these Day WSI instruments were fielded in the mid- to late
1980s, acquiring data once a minute simultaneously at several sites, in order to
determine cloud distribution (Shields et al. 1989, Johnson et al. 1989, Shields et al.
1993a).
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Following several years of experience with the Day WSI, a Day/Night Whole Sky
Imager was developed at SIO in the early 1990’s (Shields et al. 1993b). This system is
designed to acquire cloud and sky images under all conditions over a 24-hour
period, and to meet a variety of research needs (Shields et al. 1998a). A sample night
image of the Day/Night WSI is shown in figure 2.
More recently, a new Daylight Visible/NIR WSI was funded by DWD. It was
designed to have many of the features of the Day/Night WSI, but only operate
during the day and under somewhat more benign conditions (Feister et al. 2001,
Shields et al. 2003) (figure 3). This instrument is designed to acquire cloud amount
and distribution, as well as sky radiance distributions in up to 7 wavebands in the
visible and NIR. Like its predecessors, it is fully automated and environmentally
hardened. A sample image of the Daylight Visible/NIR WSI acquired at SIO is
shown in figure 4. Most of this discussion concerns the Daylight Visible/NIR WSI,
which will be designated the Daylight WSI or the current Day WSI for the
remainder of this article.
The original Day WSI used an 8-bit CCD, however it was necessary to acquire
two sets of images, offset by 0.5 log using neutral density filters, to provide onscale
data over the full sky under many conditions. One of the design criteria for the
current Day WSI is that the darkest part of the sky have no more readout noise than
1% of the signal, and a resolution of at least 1% under most conditions. Using the
Day/Night WSI daytime data acquired with a 16-bit CCD, we evaluated the desired
range, and found that a 12-bit CCD would be most appropriate for the new
Daytime Visible/NIR WSI. Further evaluations of readout noise, dark noise,
exposure mechanisms, chip size, and system linearity led to the choice of
Photometric’s Sensys 1600 camera. This camera is cooled to a stable temperature
Figure 2. A sample image acquired with the Day/Night WSI at night with no moon (18December 1997, site: SGP, exposure: 60000 s). Note the presence of transparent clouds in theimage.
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of 10uC, yielding a true 12-bit dynamic range and the stability required for
radiometric sensing.
The remainder of the optical system includes two filter wheels, a neutral density
filter, and a 180u field of view fisheye lens. A custom optical lens relay system
integrates these components and provides the appropriate image size and placement
for this camera system. The current Day WSI, like its predecessors, includes
environmental hardening. The camera and optics are housed in a nitrogen-purged
camera housing, and the camera housing and electronics are housed in an
environmental housing with an air conditioner/heater to protect the components. In
addition, a fan blows heated air onto the dome to remove condensing water or rain
droplets from the dome surface. One of the important features of the WSI is the
solar occultor that shades the front optics of the system, thereby minimizing stray
light and providing optimal image quality. This occultor is designed to shade not
just the portion of the lens which images the direct sunlight, but the full front optics.
In order to provide automation and maximize the flexibility for general research
the system may be controlled either manually or under automated computer control
via in-house designed electronics. Under automated control, an internal flux control
algorithm selects the exposure times in order to optimize the data quality in all
filters. Although the instrument is still in the early stages of development in
comparison with the Day/Night WSI, quite a bit of development of processing
capabilities has been done. In post-processing, the instrument can provide calibrated
radiance measured simultaneously in each of approximately 740,000 directions, as
well as providing irradiance over the full sky and irradiance and average radiance in
selected regions of interest. This can be done in up to 10 selected regions of interest.
The resolution in the center of the image corresponds to less than 0.15u per pixel and
increases to about 0.25u per pixel close to the horizon (figure 5).
Figure 3. The Daylight Visible/NIR Whole Sky Imager fielded at DWD’s Potsdam Site.
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The radiance distribution can be measured in up to 7 wavebands. Figure 6(a)
shows both the spectral filter transmissions measured in the DWD and MPL labs
and the CCD normalized spectral responsivity. In figure 6(b), the net normalized
system spectral responsivities of the different channels (based on the product of the
(a)
(b)
Figure 4. Sample images acquired at 650 nm from the Daylight Visible/NIR WSI. Left: 9July 2002, 10:50 UTC, contrails and 5 octa Ci partly developed from contrails; right: 13September 2002, 13:50 UTC, 5 octa Cu and 2 octa Ci fib; as a result of strong forwardscattering of solar radiation by cloud particles, the clouds at angular positions closer to thesun’s position appear to be brightened.
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sensor responsivity and the transmittances of the spectral filters and neutral density
filter) are illustrated.
3. Camera calibration
The radiometric and geometric calibrations are an important prerequisite for
obtaining accurate cloud parameters. Radiometric calibration is necessary for cloud
amount, cloud type and cloud optical thickness retrieval, while geometric
calibration is needed for stereo cloud-base height and motion determination. The
WSI instrument is radiometrically and geometrically calibrated, while the ETH
system is only geometrically calibrated.
3.1 Radiometric calibration
The determination of the 2D cloud fields with the WSI does not currently utilize the
calibrated radiance images, however portions of the radiometric calibration results
are used in the 2D cloud algorithm and the absolute results have potential in further
cloud algorithm development. Perhaps the most important aspect of calibration is
designing an instrument that acquires appropriate data for calibration, i.e. with
thermal stabilization, stray light control, the ability to acquire dark images, and so
on. The design considerations and the calibrations are discussed in detail in Shields
et al. (2003); in this section, a brief overview of the radiometric calibrations is given.
The radiometric calibrations used with the Daylight WSI include the following:
N Dark field correction, to correct for the thermally generated electrons and the
electronic bias. Although dark calibrations are evaluated in the laboratory,
the actual dark images are acquired in the field and applied immediately to the
field data.
N Flat field correction, to correct for spatial non-uniformities in the CCD. These
are acquired using a 1 m integrating sphere. For the Daylight WSI, the flat field
deviation was insignificant, and no correction was made.
Figure 5. Spatial resolution of the Daylight Visible/NIR WSI based on its geometricalcalibration.
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N Exposure corrections, to correct for shutter- and camera-timing effects. These
are acquired on a 3 m bench using calibrated lamps traceable to NIST, and a
calibrated reflectance plaque. The exposure correction is about a 10%
correction at exposures of 100 ms and 1% at exposures of 1000 ms.
(a)
(b)
Figure 6. (a) Spectral transmissions of the filters as well as normalized spectral responsivityof the CCD of the Daylight Visible/NIR WSI. (b) Normalized spectral responsivities of the 7Day Vis/NIR channels derived as spectral combinations of transmissions of broad-bandfilters, neutral density filters and camera responsivity.
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N Linearity corrections, to correct for radiometric sensor non-linearity.
Linearities are measured on the 3 m bench. Corrections are about 2% or less
in mid-range and about 5% at the extremes of the range, except in high gain
where it is larger.
N Absolute radiance calibration, which converts the values to absolute spectral
radiance in SI units. The absolute calibration data were acquired on the 3 m
bench at SIO using lamps calibrated to absolute standards at the Physikalisch-
Technische Bundesanstalt (PTB) in Germany. In order to process the absolute
calibration, it is also necessary to characterize the spectral characteristics of the
filters, the CCD and any other spectrally-significant components in the system.
N Rolloff corrections, which correct for the change in system sensitivity as a
function of angle from the normal. These changes result primarily from the
changes in solid angle per pixel, but can also be affected by other losses such as
vignetting within the lens.
The calibration accuracy depends on the calibration lamp, which typically has an
uncertainty of 1–3% depending on the wavelength region and hours of use since
calibration. The overall calibration accuracy depends largely on how much
redundancy is used. Measuring the performance in multiple ways and evaluating
it with multiple means enables one to minimize stray light and other error sources.
The Daylight WSI calibration uncertainties were estimated at between 2 and 4%. A
field calibration device, for enabling more convenient recalibration of fielded
instruments, has been developed for the Day/Night WSI but not yet adapted for the
Daylight WSI.
3.2 Geometric calibration
3.2.1 ETH system. The calibration and orientation of the ETH camera system is
described in detail in Seiz (2003). It includes (1) the determination of the interior
orientation in the laboratory, and (2) an on-site measurement of the camera location
with GPS and orientation with so-called ‘sky control points’.
The interior orientation parameters are determined with a close-range photo-
grammetric reference field of 4.26261.2 m with 77 signalized and 20 coded points
at ETH, Institute of Geodesy and Photogrammetry (IGP). For each camera, 15
images were taken: from 5 camera stations (left high, left low, center, right high,
right low) at three different roll angles (290u, 0u, + 90u). The parameters were
calculated simultaneously with camera orientation data and 3-D object point
coordinates, employing a self-calibrating bundle adjustment. Ten additional
parameters were used to model systematic errors (Brown 1971): three parameters
of interior orientation (focal length offset dc, principal point coordinate offsets dxp
and dyp, five parameters modelling radial and decentering lens distortion (radial
coefficients k1, k2, k3; decentering coefficients p1, p2) and two parameters for a
differential scale factor and a correction of the non-orthogonality of the image
coordinate axes (Beyer 1992). The differential scale factor, the non-orthogonality
factor and some of the lens distortion coefficients proved to be insignificant.
The exterior orientation of both cameras had to be determined at the
measurement locations. As the cameras were horizontally mounted and the field
of view only includes clouds and sky, the traditional methods with ground control
points could not been used. Instead of ground control points, ‘sky’ control points
had to be measured. Two independent sets of sky control points were established:
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during daylight, an airplane equipped with DGPS was flying along a specific flight
pattern; during clear nights, stars could be seen in images with long exposure times
and be taken as sky control points. The coordinates of each camera station were
additionally measured with GPS.
With the first approach, a previously calculated flight pattern was flown by the
KingAir of the Swiss Army, to get artificial sky control points. The flight lines were
parallel to the baseline of the two cameras. The highest line was 4000 m and the
lowest line at 1000 m above ground. The lines were along the left and right border of
the images and along the middle. The DGPS antenna of the airplane was manually
measured in every image. From the exact acquisition time of the image, the position
of the point could be determined from the GPS calculations and used as a control
point. With a bundle adjustment (with fixed interior orientation parameters from
the testfield calibration and station coordinates from static GPS), the orientation
angles were estimated together for both cameras. The image residuals showed an
accuracy of ,3 pixels across-track, consistent with the poor manual measurement
accuracy (due to the oblique viewing angle, the recognition of the plane and even
more of the position of the antenna was very difficult), but an accuracy of 10–
15 pixels along-track which was caused by the error in the precise acquisition time
(at a mean velocity of the KingAir of 100m/s, 10 pixels correspond to a time error of
about 100 ms at the mean flight height which is about the accuracy of the laptop
time). The stability of the angles in the bundle adjustment was improved with tie
points on clouds near the edges of the image.
The second method, with star images, was used to determine the exterior
orientation angles only. Additionally, the inner orientation parameters (same 10
parameter set as described above) of the camera were estimated, which is promising
for longer measurement periods where the inner orientation of a theoretically stable
camera could eventually change over time. This second method is also more realistic
for an operational sky imager network where recalibration of the orientation angles is
probably necessary at regular time intervals. The sky images with long exposure times
were taken during clear nights. When the exposure time was longer than about one
minute, the paths of the brightest stars could be seen between the noise (figure 7).
Although the noise represents a sum of dark current noise and sky background (i.e.
atmospheric scatter light, etc.), it could in this case, with very low sky background, be
modeled as dark current noise image alone, taken with a similar exposure time.
The enhanced star paths images were then further processed by specialized
software (Schildknecht 1994) to identify the stars corresponding to the Position and
Proper Motion (PPM) star catalog. From the star positions, the orientation angles
and the interior orientation parameters of each camera were calculated using the
same software package. The optimal exposure time is based on two factors: the
Figure 7. Left: star path and dark current noise, center: dark current noise, right: differenceimage.
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detectability of the linear feature (star track) within noise pattern, which increases
with increasing exposure time, and the linear form of the star path, which decreases
with increasing exposure time. The star path is assumed to be a straight line by the
processing software which finds the central point of each path by a centroid
operator. Only these central points are used in the calculations. The accuracy of the
calculated photogrammetric angles v, Q and k from the star images is ¡400.
3.2.2 WSI system. The WSI team has used a different approach to the geometric
calibration issue. A number of lens characteristics were tested, and it was determined
that a simple mapping from object space to image space could be determined by
measurements of objects at known angles with respect to the lens normal. When the
system is focused for infinite distance, objects in object space are in reasonable focus
from cloud distances up to a few inches from the lens. Studies of near-field and far-
field effects showed that a calibration using accurate markers a few meters from the
lens should yield valid results. Markers were placed on the walls of a room, with
approximately 0.1u accuracy with respect to a plumb bob location. Images are
acquired in this room with the full system facing the 0u marker. The system is
mounted on a rotary table, so that additional measurements can be taken at known
orientations to provide redundancy in the measurements. The resulting pixel-
position-to-angle calibration is accurate to approximately 2 pixels.
The largest source of uncertainty in the angular orientation is normally the
leveling and north alignment of the instrument, which are within approximately 1u if
installed by experienced teams. For this reason, the solar occultor was fabricated
with a 4-log neutral density that enabled detecting the solar disk in the image. In
certain applications, particularly with the original Day WSI system, the position of
the sun on the image was compared with the computed position of the sun.
Comparison of these points from images taken throughout the day enables one to
determine the level and orientation errors and make corrections accordingly. A
higher level of accuracy was desired for the Day/Night WSI, and is based on images
of the star field. This technique was developed by our colleagues in the ARM
Program (Tim Tooman, personal communication) and has been further modified
for other applications. It provides results typically accurate to approximately
0.25 pixels. A hybrid of these two techniques could also be applied to the current
Day WSI to yield more accurate geometric calibrations.
4. Retrieval of cloud properties
4.1 Cloud amount and distribution
This section provides an overview of the cloud algorithms that identify the presence
of clouds in the WSI imagery. The early work in daylight cloud algorithm
development using the WSIs in the early 1980s revealed that whereas cloud edges are
a very useful diagnostic, there are often significant edge gradients within clouds, and
weaker edge gradients at cloud edges. Similarly, signal level was useful but not
sufficient, because cloud signals could be significantly darker than sky signals under
certain conditions. An algorithm was developed based on the red/blue ratio. We
found that for opaque clouds, the red/blue ratio was significantly higher than for
clear sky backgrounds, and a fixed threshold in the red/blue ratio was quite effective
in identifying opaque clouds (Shields et al. 1989).
Thin clouds, being optically transparent, have a spatially varying red/blue ratio
which tends to be higher than the clear sky red/blue ratio. Like the clear sky ratios,
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these ratios depend on the scattering angle with respect to the sun and the angle with
respect to the horizon. For thin clouds, a daytime algorithm was developed which
characterizes the thin clouds as a perturbation from the clear sky red/blue ratio. This
algorithm first determines the background clear sky red/blue ratio of the sky, as a
function of site, solar angle and viewing angle, from measured clear sky data, and
then evaluates the field images with respect to these background ratios. The
FASCAT radiative transfer model developed at SIO (Hering and Johnson 1984) was
used to evaluate anticipated background ratio performance, however we found that
the most accurate results were obtained using the background ratios extracted from
the site-specific clear sky measurements. Examples of a cloud algorithm image from
the current Day WSI are shown in §5.1.
Perhaps the most difficult challenge in the development of the WSI cloud
algorithm is distinguishing between aerosol and thin clouds. The shape of the clear
sky red/blue ratio distribution is relatively constant, and the aerosol load can be
treated by using a time- and day- varying normalization constant that modifies the
ratio distribution. This worked quite well with the original Day WSI, however it
required some human interaction in the archival processing. For potential real-time
processing, we installed NIR filters in the systems. These not only have a higher
contrast between thin cloud and clear sky, but they also should enable distinction
between the larger thin cloud droplets, as opposed to smaller aerosol droplets.
Although this NIR data has not yet been incorporated into the realtime day
algorithm for the D/N WSI or the new cloud algorithms for the current Day WSI, it
has been incorporated into an archival processing algorithm for the D/N WSI under
the auspices of the ARM program (Shields et al. 1998b). A night-time cloud
algorithm has also been developed for the D/N WSI, but is beyond the scope of this
article.
4.2 Cloud-base height
Cloud-base height and 3D cloud-base motion can be measured with ground-based
digital cameras in stereo configuration. Allmen and Kegelmeyer (1996) analysed
data acquired with a pair of the original Day WSI’s mentioned earlier, which had
been fielded in New Mexico by the SIO group. Due to the large base length (i.e.
instrument separation) chosen and due to inaccuracies of the exterior orientation,
the matching of the stereo pairs was very difficult. Furthermore, the matching
process was much slower than today due to slower computer processors. To
evaluate multi-view cloud-base height determination, a series of experiments were
run by ETH/MeteoSwiss within Cloudmap and Cloudmap2, using the ETH camera
system designed specifically for the stereoscopy experiments.
4.2.1 ETH system. The choice of an appropriate base length for cloud mapping is
difficult because of the wide height range of clouds (up to 15 km). There is a trade-
off between an as-large-as-possible overlapping area, optimal matching conditions
and an appropriate base-to-height ratio for the specific cloud situation, as will now
be explained in detail.
The overlapping area of a stereo pair is calculated as (figure 8):
overlap~100% 1{bc
xh
� �ð1Þ
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with b: base length [m], x: total width dimension of CCD sensor in baseline
direction, c: focal length, h: cloud height (m above ground).
For the MAP setup in October 1999, with b5850 m, c518 mm, x527.54 mm,
overlapping starts above 280 m cloud height; at 3.5 km, the overlap area is 92% and
at 10km 97%. In addition, shorter base lengths ((1 km) have the advantage that
matching is easier, faster and more reliable and many of the appearance difference
problems reported in Allmen and Kegelmeyer (1996) are avoided. But there is also
an important argument for larger base lengths. The higher a cloud is situated the
larger should be the base length due to the height accuracy sz:
sz~h2
bcspx ð2Þ
with spx: parallax measurement accuracy (m)
For our base length of 850 meters, an assumed parallax measurement accuracy of
1 pixel (59 mm) and a cloud height of 3.5 km and 10 km above ground, we get a
height accuracy of 7.2 m and 58 m respectively.
A solution to this trade-off is the use of more than two cameras, with different
base lengths. This of course also improves the reliability of the matching, and if high
performance is required, faster matching methods can be applied, due to the further
geometric constraints (Seiz 2003).
For the cloud-base height retrieval, different image processing algorithms,
including image matching, were applied. At IGP, many such algorithms have been
developed over the last 20 years and have been employed in matching of various
objects. Our approach within Cloudmap was to first use and test existing methods
and try to adapt them where necessary to model or reduce special problems
encountered with clouds. The use of existing matching algorithms was aiming at
achieving high success rate, accuracy and reliability. Aspects of processing speed
were not considered at this stage, although this is clearly of importance if a system
has to be used operationally and process large datasets. In the following, the
processing steps of the stereo pairs are shortly described; a detailed explanation of
the applied methods can be found in Seiz (2003).
For matching, the RGB images were first converted to 8-bit greyscale images. To
facilitate matching, a Wallis filter (Wallis 1976) was used as preprocessing. Wallis is
Figure 8. Illustration of overlapping area (shaded area) from two cameras.
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an adaptive, local filter, which has been extensively used at IGP in image
preprocessing for matching (see e.g. Baltsavias 1991). The result is a radiometric
equalization of the stereo images and a contrast enhancement, i.e. more texture (see
figure 9).
For point extraction, two different operators were used, the Forstner operator
(Forstner and Gulch 1987) and the Harris operator (Harris and Stephens 1988). The
advantage of the Forstner operator compared to the Harris operator is the
(a)
(b)
Figure 9. Altocumulus cloud (a) in the original image and (b) after the Wallis filtering.
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possibility to extract points only along edges with a certain angle to the epipolar line
(i.e. search space of a point P of image A within image B, given accurate geometric
calibration parameters; otherwise the search space is extended to a so-called
‘epipolar band’, with a certain tolerance from the true epipolar line).
For matching, the Multi-Photo Geometrically Constrained Matching (MPGC)
package developed at IGP (Baltsavias 1991) was applied, which is based on Least-
Squares-Matching (Gruen 1985). With the geometric constraints, the search space is
restricted along the epipolar lines, increasing thus the success rate of the matching
and reducing matching problems (see figure 10). Depending on the cloud height
range, 3 to 5 image pyramid levels were used for the hierarchical matching
procedure. In the current tests, a fixed set of matching parameters was used in all
pyramid levels. Based on previous experience and some tests using supervised
matching of manually selected features, the following parameters were chosen as
optimal: patch size of 15615 pixels (pyramid level 0 and 1), 13613 pixels (level 2
and 3) and 11611 pixels (level 4 and 5), use of grey levels for matching, use of three
geometric parameters (two shifts and one scale) and decreasing weight for the
geometric constraints towards the higher-resolution pyramid levels (i.e. search space
is less restricted to epipolar line, but only to a so-called ‘epipolar band’).
For blunder detection, the MPGC software provides several statistical measures
that can be used to detect and exclude gross errors, e.g. cross-correlation coefficient,
a posteriori variance of unit weight from the least-squares adjustment, number of
iterations, etc. None of these measures can safely detect all blunders without
excluding good points. A combination of these quality measures can provide a
better diagnosis. More details about these measures and their use are given in
Baltsavias (1991) and Baltsavias and Stallmann (1993). The tests resulted in a
rejection of a certain percentage of the match points, usually about 5% to 25%,
depending on the matching problems and the selection of the thresholds. The quality
control tests were applied after matching in each pyramid level to avoid propagation
of wrong results to the next level.
4.2.2 WSI stereo feasibility. Although the WSI systems have not recently been
used for stereoscopic determination of cloud-base height, this section discusses
general considerations in converting them to yield this additional data product.
Figure 10. Illustration of geometrically constrained matching. Left: template (green); right:initial position of patch (green) and final solution of patch position (white). The search patchis moving along the epipolar line (blue line).
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There are several important considerations in designing an imager specifically for
stereoscopy research and for later routine stereoscopy field measurements. As a
basic requirement, any set of two imagers should be synchronized. With the WSIs
discussed above, this is achieved via a GPS card on the system controller. The
question of angular resolution is important. A good angular resolution yields a more
precise height resolution in the stereoscopy. This can be achieved with higher
resolution cameras, but this results in more data volume, higher camera (and
potentially computer) costs, and larger processing time. If a full hemisphere field of
view is desired, this has implications both in the engineering of the sensor, and in the
reduced angular resolution. The final choice of angular resolution also directly
affects the flux levels on the CCD, and must be taken into account.
Although the current-generation WSIs have not been used for stereoscopy yet,
most if not all of the above preprocessing and matching techniques should be
applicable to the WSI data. For research into stereoscopy, the WSI has several
additional advantages. The images are acquired in several spectral filters. The NIR
data should be especially useful, because the contrast between sky and clouds is
larger, particularly under loaded aerosol conditions such as may often be found in
continental regimes. The instruments can be used to test the usefulness of calibrated
radiance in the stereoscopy algorithms. The D/N WSI can acquire images both day
and night, for monitoring of sky conditions under all conditions. The cloud
algorithms for cloud detection already exist for this instrument, and are continuing
to be improved. Data quality is very high, with high Signal/Noise ratios. The WSI is
especially suited for research applications requiring both the cloud distribution and
the absolute radiance distribution.
Whereas the large field of view is an advantage for cloud fraction determination,
it is a disadvantage for stereoscopy. Because the field of view and angular resolution
have been optimized for full hemisphere viewing, rather than stereoscopy, the height
resolution is (at least initially) poorer than indicated in §4.2.1. The viewing angle is
given by tan a5h/d, where h is cloud height and d is projected distance on the
ground between the cloud and the instrument. The computed height uncertainty for
the Daylight WSI, with its 180u field of view, is approximately 40 m near the zenith
at a height of 3.5 km, and 320 m at a height of 10 km, with a base length of 850 m
and roughly double these values with the current generation D/N WSI. However,
when the impact of uncertainty in the angular calibration is taken into
consideration, the very accurate angular calibration of the D/N WSI could more
than make up for this difference in the final analysis. In the final analysis, the most
accurate 3D assessment could probably be achieved by a full field of view WSI
which controls a smaller field of view peripheral instrument. The full field would
achieve the best 2D cloud distribution, while the smaller field of view sensor would
achieve the best height resolution. As a result of research using the WSIs as research
instruments, choices could be made regarding the best wavebands to use, and which
other features are most important, and a version of the WSI optimized for the
application could be designed.
4.3 Cloud-base motion (3D)
The same algorithms described in §4.2. can also be used for deriving 3D cloud
motion by matching corresponding features in the stereo image sequences. Various
time intervals between 30s and 120s were tested during MAP to evaluate the limits
for the matching/tracking algorithm. The tests showed that 120s can be too long in
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many cases, as there is not only a large displacement of the clouds given a high cloud
motion speed (which requires good approximations for the tracking), but more
problematic, significant shape changes of the cloud structures (see figure 11).
A further constraint is the amount of displacement, especially for lower clouds, so
that the overlapping area of the subsequent images gets too small. The amount of
displacement (in pixels) can be calculated as:
dx, dyð Þ~Dt u, vð Þ c
h
nr pixels x
xð3Þ
with (u, v): motion speed in x- and y-direction, x: dimension of sensor in baselinedirection, c: focal length, h: cloud height (m above ground).
In reality, cloud motion is usually larger at higher cloud heights so that for usual
motion amounts of 10–20 m/s, the displacement within 30s should be small enough
for still getting a large overlapping area for 3D cloud-base motion retrieval.
4.4 Cloud optical thickness
The WSIs also measure the sky radiance distribution (absolute values of radiantpower per unit area and per unit solid angle) with high spatial and temporal
resolution in several spectral bands. Sky radiance contains information on the
Figure 11. Problems that arise in image tracking if the time difference between twoconsecutive stereo pairs is too large. Top: stereo pair at time t0; bottom: stereo pair at time t1.There is a 2-minute time difference for these images obtained on 13 October 1999.
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atmospheric extinction and may, in the future – in combination with the cloud
distribution – be used to derive estimates of optical features of the atmosphere such
as cloud optical thickness.
5. Results
In the following, results from the operational WSI measurements at DWD-Potsdam
and from two ETH research measurement campaigns, within the Mesoscale Alpine
Programme (MAP) in October 1999 and at Zurich-Kloten airport in April 2002, are
shown.
5.1 Whole sky imager (WSI) at the German weather service (DWD)
A Daylight Visible/NIR WSI with seven spectral channels was installed at the DWD
Meteorological Observatory Potsdam (52u229 E, 13u59 E, 107 m asl) in December
1999 and has been continuously working since that date (Feister et al. 2001). The
instrument worked reliably, with only two interruptions of operation in the summer
of 2000 due to an incorrect alignment of filters in the filter wheels and due to a
failure of the air conditioner. Dew and ice on the dome at winter time periods have
been removed by an additional fan blowing hot air from inside the environment
housing on the dome, though it has not been capable of removing ice that built at
times from rain droplets on the occultor arm with air temperatures below 0uC. The
daily schedule has been chosen as a compromise between sufficient sampling rate
and storage capacity with sequences of 7 images taken every 10 minutes between
sunrise and sunset. Shorter sampling rates of 1 sequence taken every two minutes
were chosen for selected time periods, when comparisons with other instruments
were performed. In an uncompressed mode, one image has a size of about 1.9
Mbytes. With an average amount of more than 1 GB of data per day, more than 1.5
Tbytes of dark corrected images have been collected over four years of operation.
While radiance files are even bigger with about 7.5 MB for each of them, the
processed files that contain the cloud cover for optically thick and optically thin
clouds are much smaller than the image files (a few kB per day only) and can be
processed quite quickly. An example of a processed cloud decision image for
optically thick clouds is shown in false colors in figure 12. The total cloud fraction
derived from this image is 0.36, with 0.21 of opaque and 0.15 of thin cloud fraction.
Conventional cloud observations provided a total cloud fraction of 0.25.
5.1.1 Conventional cloud observations. Ground-based cloud observations are
performed in meteorological networks according to guidelines of the World
Meteorological Organization (WMO 1975–1987). Clouds are classified in 4 different
categories as cloud families, i.e. low clouds (Stratocumulus and Stratus), medium–
level clouds (Altocumulus and Altostratus), high-level clouds (Cirrus, Cirrocumulus
and Cirrostratus) and clouds with vertical extension (Nimbostratus, Cumulus and
Cumulonimbus). The origin of that classification dates back to the English
manufacturing chemist and pharmacist Luke Howard (1772–1864). In the modern
synoptic meteorological code, only 3 categories are in use, i.e. Ns is assigned to
medium-level clouds, and Cu and Cb are considered part of low-level clouds. Each
of the three categories can have one out of nine different states and one out of nine
different cloud covers ranging from 0 octa (cloudless) to 8 octa (overcast). The digit
9 for cloud cover is used if clouds in the respective layer cannot be recognized due to
masking clouds below that layer, or in the case of fog at the ground (visibility less
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than 1 km). To estimate cloud cover, the observer divides the sky in 8 sub-areas of
equal size and counts the cloud-covered parts as ‘octa’ or ‘eighths’, or also called
‘octaves’. It is recommended in the manual of cloud observations that the total sky
should be observed ‘quasi-continuously to recognize the development and move-
ment of clouds and of the cloud cover at different levels over time’. Even if observers
are highly motivated and experienced, many other tasks to be performed at a
modern weather station will probably prevent an observer from completely meeting
that recommendation. Furthermore, the observer has to decide on one hourly value
of cloud type and cloud cover for each of the three categories, even though there can
be a high degree of variability of cloud cover within one hour of time. Therefore, it is
common to report those values that are observed about 10 minutes before the full
hour and assign those values to the full hour when the reports are to be sent to the
respective data center. A cloud cover and cloud type reported and sent from the
synoptic station to a weather center for the time 12 UTC may thus be more
representative for 11:50 UTC. In that respect, the cloud cover estimated by an
observer does not need to be a good estimate of the hourly average of cloud cover,
particularly, if atmospheric conditions are changing fast. In addition to the
uncertainty due to sampling frequency, the observer’s skill in estimating cloud cover
must be added to the total uncertainty of conventional cloud observations. The
Figure 12. WSI cloud decision image at Potsdam on 7 April 2002 at 11:30 UTC; total cloudfraction from the WSI cloud decision algorithm is 0.36, with 0.21 opaque (white and gray) and0.15 thin cloud fraction (yellow). Conventional cloud observations at the synoptic stationestimated a total cloud fraction of 0.25 Cu clouds.
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uncertainty of total cloud cover estimated by a skilled observer at a weather station
is expected to not exceed ¡1 octa, but it may be slightly higher for clouds at higher
levels that are partly masked by cloud layers below that higher level. In addition, the
actual observation time may deviate by up to 5 minutes from the scheduledobservation time, i.e. in the example given above, it may vary between about 11:45
UTC and 11:50 UTC.
5.1.2 Preliminary comparison between WSI and cloud observations. An example of
cloud cover data derived from WSI red/blue image ratios is shown in figure 13. The
sampling rate of 2 minutes selected for that comparison shows much more detail in
short-time cloud cover variations than can be provided by conventional cloud
observations, which are collected every hour at the nearby weather station.
Although it can be expected from any visual observations that they are rather
subjective and dependent on the skill of the observer, our comparison showed a
good correspondence between the visual observations and the results from the skyimagers. Fast moving clouds would require an even higher sampling rate to reflect
the real cloud conditions at a time and not to erroneously bias hourly or daily
averages of cloud cover. Correspondingly, the sampling rate is also critical to other
parameters that can be derived from measured radiances and/or sky images. Shown
for comparison in figure 13 are values of cloud cover that were derived from images
acquired by another instrument type for ground-based daylight cloud imaging. That
instrument, a Total Sky Imager (TSI-440) manufactured by Yankee Environmental
Systems (YES 2000) was available at the DWD in Potsdam for a time period at theend of 2000 on loan from Aero Laser in Munich (Dieter Haaks, personal
communication). It uses a 368-bit color camera with a 3526288 pixel array. The
camera looks down at a slowly rotating spherical mirror that has a black strip to
occlude direct solar irradiance from the camera (but does not shade the mirror). The
Figure 13. Cloud cover (0 corresponds to cloudless and 1 corresponds to overcast sky) fromWSI images (red/blue ratios) for optically thick clouds and from TSI images (low clouds), andcloud cover observations of the Potsdam weather station (low clouds and clouds at medium-level heights), 27 October 2000.
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amount of degrees above the horizon for which the imager collects and processes
data can be selected by the user. For our comparison, it was set to 5u.Both WSI and TSI show similar short-time variations of cloudiness, with some
systematic lower cloud fractions observed by the TSI compared to the WSI, in
particular at low solar zenith angles (cloud cover from TSI is derived for solar
elevation angles larger than 3u only, while WSI data go down to solar elevation of
0u). Figure 13 is restricted to optically thick clouds, i.e. mainly those with low and
medium-height cloud-base height, because the WSI thin cloud algorithm for the Day
Imager was still under development at the SIO at the time of the comparison.
Some statistics of comparisons between total cloud cover derived from a set of
WSI images taken at Potsdam between May 2002 and April 2003, and from the
corresponding observations at the Potsdam weather station are shown in figure 14.
WSI total cloud cover values for the UTC minute 50 were rounded to octa and
compared with the cloud observations of the corresponding hourly cloud
observations. The spatial distance between the observer’s site and the imagers’ site
of 2 km will add to the observer’s uncertainty of ¡1 octa in estimating cloud cover.
It would translate into an angular distance of a cloud in the zenith between about
45u for a low cloud (CHB at 2 km) to an angular distance of 11u for a cloud at a
height of 10 km. In addition, the movement of clouds in connection with the time
uncertainty of the conventional cloud observation of about –5 … 0 minutes around
the observation time, which is ten minutes before the hour, would also add to the
uncertainty. If the clouds move at a speed of 10 km/h, this uncertainty in time
translates into a spatial difference of 0.8 km, which corresponds to an angular
distance of about 22u for low clouds and less than 5u for high clouds. A cloud
moving quite fast at a speed of 50 km/h would shift its location by about 4 km within
5 minutes or by an angular distance of about 64u for low clouds and 23u for high
clouds. A cloud movement of that size could significantly change the actual cloud
pattern. Depending on the direction of cloud movement in relation to the locations
Figure 14. Differences between total cloud cover in octas as derived from N53,817individual WSI measurements taken 10 minutes before the hour, and coincident hourly cloudobservations for the period May 2002 to April 2003 at Potsdam.
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of both sites, this uncertainty due to horizontal movement could either enhance or
reduce the angular difference in the cloud position due to the spatial distance of
2 km between the observer and the imagers. However, as we have not compared
cloud patterns or cloud cover in different sky regions, but cloud cover referring to
the whole sky, both the imagers and the observer will generally see the same clouds
within their 180u fields of view, even though the observations are taken at slightly
different places and with small differences in time.
It can be seen in figure 14 that about 30% of the differences in cloud cover derived
from WSI images and observers are zero, 2/3 of the differences are within ¡1 octa,
and 4/5 of the differences are within ¡2 octa. Neither a systematic difference with
both completely independent types of observations nor a dependence on solar zenith
angle can be seen in the results of the comparison, except for very high solar zenith
angles (H.85u), when the cloud decision becomes more difficult due to scattering of
solar radiation from regions close to the sun position that are very bright and that
result in indeterminate red/blue WSI channel ratios in the cloud decision algorithm.
The comparison also shows that the typical occurrence of cloud cover at the
Potsdam site is quite well reflected by the present version of the WSI cloud decision
algorithm. Figure 15 shows the frequency distribution of cloud cover derived from
WSI data (first sample) and from cloud observations (second sample) for the same
dates and times. It can be seen in the figure that both sources of data show a similar
overall frequency pattern. Differences in the patterns are seen with cloudless
conditions, when the cloud observations show a much higher percentage of cases
than the WSI algorithm. The new WSI postprocessing algorithm can also separate
between optically thick and optically thin clouds. This feature will not be further
discussed here, but it allows us to study the causes of those differences in more
detail. With the thresholds used in the cloud decision algorithm in postprocessing
the WSI images, mostly very thin Cirrus clouds that show up in the images have not
been classified as thin clouds by the cloud decision algorithm. This misinterpretation
Figure 15. Occurrence of total cloud cover in octa for the period May 2002 to April 2003 atPotsdam. Cloud cover was derived from N53,817 WSI cloud decision images taken10 minutes before the hour, and coincident hourly cloud observations.
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could have been avoided by adjusting the thresholds of the clear sky reference values
before processing the images, but on the other hand, that approach could have
enhanced the number of misclassifications for haze conditions. The problem is
expected to be solved by the latest version of the cloud decision that is under
development at the SIO.
5.2 ETH research campaigns
The data acquisition for the ETH system data analysed in this section took place
during two ETH/MeteoSwiss measurement campaigns, (1) within the Special
Observation Period (SOP) of the Mesoscale Alpine Programme (MAP) at Mels,
Switzerland, in October 1999 (MAP Science Plan 1998), and (2) at Zurich-Kloten
airport, Switzerland, in April 2002.
During MAP, our two camera locations were separated by 850 m horizontally and
were visible from each other; the baseline direction was parallel to the valley, from
NW to SE. The choice of an appropriate base length for cloud mapping with only
two cameras is a difficult trade-off, as described in §4.2. For the second campaign,
the airport of Zurich-Kloten was chosen as the measurement site is equipped with
Vaisala 25K ceilometers which allowed an accurate comparison of cloud-base
heights with the results from our system. In §5.2.1 and §5.2.2, the use of radiosondes
and ceilometers as comparison instruments for cloud-base height is shortly
explained; a more detailed discussion can be found in Seiz (2003).
5.2.1 Radiosondes. During the MAP SOP in autumn 1999, several temporary
radiosonde stations were set up in the three target areas. They consisted of two types
of sondes: low-level sondes, measuring temperature and wind, and high-level sondes,
measuring pressure, temperature, humidity and wind. The five MAP low-level
sounding stations did not allow a reliable cloud boundary retrieval from the
temperature and wind profiles, so these soundings were not considered in our
comparison. The high-level sounding station of Diepoldsau (47.37 N, 9.66 E), which
was used for the comparison case of 08 October 1999, was equipped with ground
station P-760 receivers and used the same SRS-400 sonde as the operational
soundings from Payerne (Richner 1999). While the operational version of the SRS-
400 measures the dewpoint temperature with a dewpoint mirror for high precision
humidity values, the MAP SRS-400 sondes were equipped with a carbon element for
relative humidity measurement. Consequently, the accuracy of the SRS-400
sounding data was ¡2 hPa for pressure, ¡0.3 K for temperature and ¡2% for
relative humidity.
Radiosondes do not directly measure the vertical distribution of clouds, so that
cloud layers have to be inferred from their temperature and humidity profiles. A
common visual method to detect cloud layers is to plot the temperature and
dewpoint temperature profiles (see figure 16). At height levels where there is a small
difference between the two temperatures (visible in the plots at heights where the
two profiles approach each other) the atmosphere is saturated and cloud occurrence
is likely. However, it is important to remark that such saturated, or nearly saturated,
layers are no guarantee for the actual presence of clouds. Given the temperature and
humidity sensor accuracies from above, the accuracy of cloud-base heights derived
from radiosondes is about 200–300 m, until a height of about 8 km. Above 8 km, the
humidity measurements are too inaccurate due to the used SRS-400 sonde and due
to the low absolute humidity values at these heights.
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5.2.2 Ceilometers. Today, operational ceilometers are installed at most airports
around the world. A ceilometer measures the height of cloud base above ground
level. It transmits laser pulses vertically into the atmosphere and measures the time
delay for the pulses to return to the ceilometer after reflection by small water
droplets in the clouds. The Vaisala 25K ceilometer (Vaisala 2003) installed at
Zuerich-Kloten airport uses a pulsed diode, InGaAs MOCVD laser at a wavelength
of 905 nm that transmits approximately 59000 light pulses vertically towards the
clouds each second. As the backscatter signal of a single pulse is weak, signals are
summed over intervals of 15 s to 30 s to improve signal-to-noise ratios. Instrument
software processes these signals into cloud-base height values by applying a
threshold on the return signal to distinguish between pure noise and noise summed
with a cloud signal. The resolution of the Vaisala 25K ceilometer is 50 ft (i.e. about
16 m) with an accuracy of ¡2% of the cloud height. The maximum detection height
of the Vaisala 25K ceilometer is around 259000 ft or 8 km.
5.2.3 Cloud-base height. Table 1 shows the result of two selected MAP stereo
pairs: an altocumulus and a cirrus situation. The cases were chosen with respect to
the MAP validation data available. For the 13 October 1999 case, the DSM points
were manually grouped into the two distinct cloud layers to enable comparison to
available validation data. The automation of cloud layer (or object) segmentation as
well as the 3D modeling and cloud visualization are important tasks which will be
further investigated within Cloudmap2.
To get a quantitative evaluation of the matching quality, the computed DSM has
to be compared with some reference data. As no high accuracy ceilometer was
available in the MAP composite observing network, the best validation of the results
Figure 16. Sounding from Diepoldsau, launched on 08 October 1999, at 11:00 (temperature;dewpoint). A strong inversion is visible around 4000m above sea level, indicating a cloud layerwith a base height of 3900–4000 m.
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is the comparison with semi-automatic matched points (i.e. manual location of
approximate value in patch image). Table 2 shows the results of the comparison with
about 60 semi-automatically measured points within the overlapping area of the
stereo pairs. Two versions were calculated for each case, one with the original
images and a second version with Wallis filtered images. As could be expected, the
preprocessed version performed better. For the 08 October 1999 images, with a
mean cloud height of 4000m, the expected z-accuracy according to Equation 2 is
about 8 m; for the 13 October 1999 case, with a mean cloud height of 10000 m, the
expected z-accuracy is about 58 m.
The comparison with other MAP data was quite difficult as most of the other
systems were also research instruments and not operating continuously. The best
comparison data for low and middle clouds (,8 km) were the frequent (every 3h)
Diepoldsau radiosonde data (§5.2.1.), launched only a few km from the camera
locations. Figure 16 shows the MAP sounding of 08 October 1999, launched at
11:00. The extracted cloud-base height and wind data from the sounding are
summarized in table 3.
Table 1. Cloud-base height results from the ground-based imager system for two selectedMAP cases (heights above sea-level).
Date, time Cloud typeMean cloudheight (km)
Cloud heightrange (km)
8 October 1999, 10:58 Altocumulus 4.0 3.8–4.213 October 1999, 10:16 Cirrus, two layers 8.0 7.8–8.1
10.9 10.7–11.1
Table 2. Statistical measures of the differences between the DSM results from the cloud-adapted MPGC and the manual measurements (positive: CBH from automatic
measurements.CBH from manual measurements).
ImagesNumber of
points Average (m) RMS (m) Max (m)
8 October 1999, original images 62 21.77 9.53 247.868 October 1999, preprocessed images 62 21.31 8.69 232.6413 October 1999, original images 47 17.0 98.9 563.813 October 1999, preprocessed images 47 15.6 60.1 413.6
Table 3. Comparison data for the two MAP cases: cloud-base height (CBH) and horizontalwind from radiosonde launched at Diepoldsau, about 30 min after camera measurements;satellite-based stereo cloud-top height (CTH) from ATSR-2 and cloud-top motion wind from
Meteosat-6 5min Rapid Scans (only available during second case).
DateRadiosondeCBH (km)
Radiosondehoriz. winddirection (u)
Radiosondehoriz. windspeed (m/s)
ATSR-2CTH(km)
M6 CMWdirection
(u)M6 CMWspeed (m/s)
08 October1999
3.9 296 8.5 – – –
13 October1999
8.5 280 18.8 11.0 275 19.3
–* 275 27.4
*No higher dewpoint measurements (only up to about 9–10 km)
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In figure 17, the 3D cloud field results of 08/10/1999 is presented, to show the
height distribution over the whole overlapping area of the two cameras.
For vertically thin clouds (13 October 1999), the additional use of satellite data
was helpful. The cloud-top height and motion was extracted from the satellite data
of ERS2-ATSR-2 and Meteosat-6 according to the methods described in Seiz(2003).
In the following paragraph, one case study from the Zurich-Kloten airport
measurements in April 2002 is presented, to show a comparison of our stereo cloud-
base height retrievals with coincident ceilometer data. For the quantitative
(a)
(b)
Figure 17. Cloud-base height result from stereo images of 8 October 1999. Left: orthophoto;right: cloud-base height (CBH) field.
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comparison, seven time intervals with a spacing of 5 min were analysed between
10:30 UTC and 11:00 UTC on 12 April 2002 (table 4). The nearest 10 cloud points to
the location of the runway (RW) ceilometer were extracted from the stereo cloud-
base height results and averaged. Except for the 10:55 UTC time interval, all points
were from the same cloud layer, while at 10:55 UTC the points were grouped into
two distinct layers. For the 15 s ceilometer data the values within a small window of
¡60 s (i.e. 9 values) were averaged. As the results in table 4 illustrate, the stereo-
derived CBHs are quite consistent with the ceilometer values. However, we can see
that the ceilometer had problems with broken clouds when clear sky was reported.
The cameras with their larger FOV have a clear advantage for such broken cloud
cases.
5.2.4 Cloud-base motion. Table 5 shows the extracted motion parameters direction
and speed for (a) the 08 October 1999 dataset, with 30s interval and (b) the 13
October 1999 data set, with 120s interval. Although there are many blunders in the
13 October 1999 results due to the reasons mentioned above, the two distinct cloud
layers already identified in §5.2.1 with the cloud-base heights, are also clearly visible
here, with significant motion speed differences between the two layers, but about the
same motion direction.
The validation of cloud-base motion is even more difficult than the validation of
cloud-base height, as most of the conventional instruments measure the wind speed/
direction of the air and not of the clouds. In some cases, the cloud motion can
correspond to the wind at the cloud base, but in most cases, the comparison can be
misleading (e.g. orographic foehn clouds). Therefore, for the vertically thin cirrus
case (13 October 1999), the use of satellite data was again helpful. The horizontal
Table 4. Comparison of stereo camera results with ceilometer time series data, 19 April 2002,10:30–11:00 UTC.
Time (UTC)Camera system (nearest
10 points) (m ASL)Ceilometer (2 min average)
(m ASL)
10:30:00 2519¡9 2375¡4210:35:00 2123¡114 2198¡20510:40:00 2001¡3 1957¡1610:45:00 2168¡88 2080¡5610:50:00 2167¡114 2181¡18110:55:00 2211¡63 2106¡33
3877¡5011:00:00 5698¡114 – (clear sky)
Table 5. Cloud-base motion wind results from the ETH ground-based imager system for twoselected MAP cases.
Date, timeFrequency
(s)Horizontal motionwind direction (u)
Horizontalmotion windspeed (ms21)
Vertical motionwind speed
(ms21)
8 October 1999,10:58
30 310 10.2 0.5
13 October 1999,10:16
120 274 17.8 –1.1
276 25.8 1.3
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cloud-base motion is compared to the cloud-top motion extracted from the MAP
Meteosat-6 5min Rapid Scans (see table 3). For the vertical cloud-base motion, no
comparison data was available. However, the calculated values seem to be
reasonable.
6. Conclusions
The results from the three ground-based digital photogrammetric camera systems
show the potential of such systems to complement existing measurement networks
with highly valuable cloud data.
In the retrieval of cloud amount, the WSI is shown to be capable of providing
cloud cover data for the whole sky as well as up to 10 readily selectable sky portions
for both optically thick and thin clouds with a sampling rate of down to about 1–
2 minutes. The version of Daylight Visible/NIR WSI applied in the German
Weather Service has been continuously used to collect sky images and radiances
over a period of four years. Post-processing of the large amount of raw data will
provide a data base of cloud cover and sky radiances that can be used to validate
satellite-based cloud retrieval techniques as well as to develop methods for
extracting other atmospheric parameters from the measured radiances. Further
operational use of this instrument at the DWD Radiation Center and studies using
its data can also be helpful in the design of simpler instruments that are suitable to
provide synoptic and climatological cloud data at networks of automatic weather
stations.
The stereoscopy tests with the ETH systems proved the high accuracy potential of
such a stereo camera system in the cloud-base height and motion measurement.
With the current system based on simple off-the-shelf components, the achieved
height accuracy is already about 0.5–1% of the cloud-base height (depending on the
matching accuracy for different cloud types). The system proved furthermore its
usefulness for comparison with satellite-based cloud products for vertically thin
cloud situations.
Finally, it has to be noted that ground-based sky imaging in the visible and near
infrared region bears the potential to derive other atmospheric parameters in
addition to the ones discussed in this paper. Among them are macroscopic and
optical cloud features such as cloud types and cloud optical depths, and even
microphysical cloud features such as droplet distributions as well as atmospheric
aerosol information, e.g. its thickness and size distribution.
7. Outlook
Improvements in the systems may concentrate on different aspects that depend on
the planned application of the specific instrument type. While a radiation and
climate monitoring station would probably need as many atmospheric parameters as
possible to be derived from the sky images and radiances (with low uncertainties,
high spatial resolution and high temporal resolution), a subset of parameters such as
total cloud cover, cloud-base height and cloud structures may be sufficient for
unattended field weather stations, which could be derived from much simpler and
therefore more cost-effective instrument versions. In any case, all the stations
between those two extremes mentioned that perform regular cloud observations
should be equipped by fully automatic and reliable instruments that keep instrument
system failures and the need of manual data processing at a minimum. Therefore, a
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combination of the WSI operability with the ETH system’s processing software
would be a good collaboration for operational stereo-photogrammetric retrieval of
cloud-base heights. Furthermore, in addition to improvements in the imaging
systems themselves, there is a need to develop software tools that make processed
data easily available to the potential user without time delay.
Considering these operational aspects, the WSI instruments would then be useful
for upgrading the existing prototype camera networks of the National Weather
Services to additionally measure quantitative cloud parameters operationally, such
as cloud amount, cloud height and cloud motion.
Acknowledgements
The MAP data was provided by the various principal investigators of the MAP
Rhine Valley target area and the Swiss Meteorological Institute. We thank Kodak
and FHBB Muttenz for providing the DCS460 cameras, the Swiss Army for the
calibration flights and the Astronomical Institute Bern (Martin Ploner, Thomas
Schildknecht) for the star processing. The work of ETH described in this paper is
funded by the Bundesamt fur Bildung und Wissenschaft (BBW) within the EU-
projects CLOUDMAP (BBW Nr. 97.0370) and CLOUDMAP2 (BBW Nr. 00.0355-
1).
We additionally thank Robert Fugate of Starfire Optical Range, Ann Slavin of
Boeing and Tim Tooman of Sandia National Labs for their technical advice and
feedback during the development of the Day/Night WSI. Other members of DWD
who were very helpful in the development of the Day Vis/NIR WSI were Klaus
Dehne and Rolf-Dieter Grewe. Andrew Beauto at Roper Scientific has been very
supportive in these efforts. We also thank the current members of the Atmospheric
Optics Group at SIO’s Marine Physical Lab who do development work on the
WSI’s and their algorithms: Richard Johnson, Monette Karr, Art Burden, Justin
Baker and Jerry Crum.
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