This study uses a digital camera to detect and measure nighttime cloud occurrence above Manila Observatory. Images are taken every 5 minutes, converted to grayscale, and pixels with values over 17 are counted as clouds. Preliminary results from June and October 2014 show mean nightly cloud occurrence percentages and their variations over selected dates. Continuous imaging will provide baseline cloud data for future studies in this region.

1. Ground-based Cloud detection using a digital camera above Manila Observatory (14.64N, 121.07E)
Gacal,GlennFranco1
;Francis,Tan1
;Antioquia,Carlo1
;andLagrosas,Nofel1,2
1
Ateneode ManilaUniversity,KatipunanAve., QuezonCityPhilippines
2
ManilaObservatory,AteneodeManilaUniversitycampus,KatipunanAve.,QuezonCityPhilippines
Cloud detection during nighttime poses a real problem to researchers because of a lack of optimum sensors that can
specifically detect clouds during this time of the day. Hence, lidars and satellites are currently some of the instruments
that are being utilized to determine cloud presence in the atmosphere. These clouds play a significant role in the night
weather system for the reason that they serve as barriers of thermal radiation from the Earth and thereby reflecting this
radiation back to the Earth. This effectively lowers the rate of decreasing temperature in the atmosphere at night. The
objective of this study is to detect cloud occurrences at nighttime for the purpose of studying patterns of cloud occur-
rence and the effects of clouds on local weather. In this study, a commercial camera (Canon Powershot A2300) is op-
erated continuously to capture nighttime clouds. The camera is situated inside a weather-proof box with a glass cover
and is placed on the rooftop of the Manila Observatory building to gather pictures of the sky every 5min to observe
cloud dynamics and evolution in the atmosphere. To detect pixels with clouds, the pictures are converted from its na-
tive JPEG to grayscale format. The pixels are then screened for clouds by looking at the values of pixels with and
without clouds. In grayscale format, pixels with clouds have greater pixel values than pixels without clouds. Based on
the observations, a threshold pixel value of 17 is enough to discern pixels with clouds from pixels without clouds.
Figs. 1a & 1b are sample unprocessed pictures of cloudless night (May 22-23, 2014) and cloudy skies (May 23-24,
2014), respectively. Figs.1c and 1d show percentage of occurrence of nighttime clouds on May 22-23 and May 23-24,
2014, respectively. The cloud occurrence in a pixel is defined as the ratio of the number times when the pixel has
clouds to the total number of observations. Fig. 1c shows less than 50% cloud occurrence while Fig. 1d shows cloud
occurrence more than what is shown in Fig. 1c. These graphs show the capability of the camera to detect and measure
the cloud occurrence at nighttime. Continuous collection of nighttime pictures is currently implemented. In regions
where there is a dearth of scientific data, the measured nighttime cloud occurrence will serve as a baseline for future
cloud studies in this part of the world.
Methodology:
Location
Capture images of the sky
above Manila Observatory at 5
minute interval.
Algorithm converts one night
worth of pictures from its native
RGB to greyscale format.
Pixel
value > 17
Count as
Cloud
The Manila Observatory (14.6367° N,
121.0767° E) is a private non-stock, non-
profit, scientific research institution estab-
lished in 1865 by the Jesuit mission in the
Philippines. The Observatory finds itself at
the nexus of global concerns for environment
and development. With its tradition of excel-
lence and service in scientific research, the
Observatory envisions itself as a vital partner
in these critically global yet locally relevant
concerns.
Image without Clouds:
RGB format
Cloud Occurrence:
This work has shown that nighttime clouds can be detected using a ground based digital camera. Continuous gathering
of images can lead to measurements of nighttime cloud occurrence. Clouds are discerned from pixel values by apply-
ing the threshold value of 17 from grayscale images. Further work is still needed to analyze nighttime images with rain
droplets and moon signals. The occurrence of these variables destroy the pixel information of cloud signals. In the fu-
ture, with continuous gathering of images, the cloud occurrence data at nighttime can be added and related to cloud oc-
currence data at daytime to have a comprehensive idea of daily and seasonal cloud occurrence over an urban area in
this part of the world.
The authors would like to thank the Lloyd Berkner Fellowship Awards for the travel grant support.
(Source: Google Maps)
Count as Non-
cloud
YES
NO
Matrix of Cloud
Occurrence
Cloud
Percentage
Image without Clouds:
grayscale format
Image with Clouds:
RGB format
Image with Clouds:
grayscale format
Set-up
Figure 2. Camera set-up without the cover
Figure 3. Camera set-up with the cover
Model Canon Powershot A2300
Picture size 640 x 480 pixels
Exposure time 1 sec
Focal Length 5 mm
F-stop f/2.8
Color representation sRGB
Table 1. Camera specifications
Cloud Occurrence:
Figure 1. Sample unprocessed pictures of a) cloudless
night (May 22-23, 2014) b) and cloudy skies (May 23-24,
2014). Percentage of cloud occurrence of nighttime clouds
on c) May 22-23 and d) May 23-24, 2014.
OSPA No. A31H-3113
Abstract ID: 12245
Figure 5. Cloud percent-
ages for a)06/01/2014, b)
06/02/2014, c)06/03/2014,
d) 06/05/2014, e)
06/07/2014, f) 06/30/2014.
Fig. 5d is an example of an
almost clear sky
(maximum cloud percent-
age is roughly 15%). Figs.
5b and 5e are nights with
high cloud occurrence.
Figure 4. Mean values of cloud occurrence in percentage for selected dates of June 2014. Standard deviation of
cloud occurrence range from 1% to 12%.
Figure 6. Mean values of cloud occurrence in percentage for selected dates of October 2014. Standard devia-
tion of cloud occurrence range from 0.1% to 7.4%. Compared to June results, nights of October are relatively
less cloudy.
Figure 7. Selected dates in the month of October showing cloud percentage for the nights of a)10/18/2014, b)
10/19/2014, c)10/202014, d)10/232014, e)10/25/2014, f)10/262014, g)10/30/2014, h)10/31/2014.
Abstract
October Results
Conclusions
Acknowledgements
AMERICAN GEOPHYSICAL UNION (AGU) FALL MEETING
15-19 DECEMBER 2014
MOSCONE CENTER, SAN FRANCISCO, USA.
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Histogram:
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Histogram:
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