Estimation of fractional cloud cover from all-sky digital images at sea
description
Transcript of Estimation of fractional cloud cover from all-sky digital images at sea
Estimation of fractional cloud
cover from all-sky digital images at
sea
Motivation and background MORE cruises: all-sky digital images of cloud Testing existing algorithm for estimation Comprising existing algorithm with visual:
- for low clouds condition- for high and mid clouds condition
Conclusions
Alexey SinitsynSergey Gulev
Krinitsky Mikhail
Motivation: Accurate estimation of cloud fraction is critical for the quantitative computation of surface short wave and longwave solar radiation at sea.
Today science advanced to a point when we can engage information about cloud types in the analysis of solar radiation considerably extending the abilities of routine observations of cloud fraction taken visually.
This is particularly important under overcast conditions frequently associated with the mixed cloud types and, therefore, optical thickness of the cloudy atmosphere
MORE cruises: 2007-2010 (for all-sky image)
October 2007R/V “Academician Ioffe” Santa Cruz (Spain) – Montevideo (Uruguay)
October – November 2009R/V “Academician Ioffe”Kaliningrad (Russia) –
Cape Town (RSA)
September 2010R/V “Academician Ioffe”
cape Farewell (Greenland) – Szczecin (Poland)
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All-sky image package Nikon D80 with 10,2 effective pixels
Sigma 8mm F3,5 Circular-Fisheye
Angle of view for APS-C sensor 180 ° х 120 °
Projection of fisheye lens is equidistant projection All shoots were taken with hands
Data collected: 2007-2010: 62 days of all-sky digital
images with 1-hour resolution
Simulteneous hourly observations of standard meteorological variables (clouds, air temperature, humidity, SST)
Example of all-sky digital image
Sigma 8mm F3,5 EX DG Circular-Fisheye
Existing algorithm for cloud recognizing
(ii) Sky Index for Kalisch J., Macke A. (IFM-GEOMAR), 2008:
Sky Index (IFM SI) = (DNRed) / (DNBlue ),
DNBlue = digital number of blue channelDNRed = digital number of red channel
Sky Index (SI) = (DNBlue – DNRed) / (DNBlue + DNRed),
DNBlue = digital number of blue channelDNRed = digital number of red channel
Empirical threshold 0,1. If SI <= 0,1 then consider pixel as cloudy
(i) Sky Index for Yamashita M. and etc, 2004:
Empirical threshold 0,8. If SI > 0,8 then consider pixel as cloudy
Comparisons of the algorithm cloud cover estimation for batch processing
Applied to batch processing both algorithm for cloud estimation shown same result.Corr = 0.99Stddev = 0.190 1 2 3 4 5 6 7 8 9 10
Sky Index
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Comparisons of the SI algorithm cloud cover estimation with visual data
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2007Corr = 0.79Stddev = 2.11
2010Corr = 0.78Stddev = 1.80
2009Corr = 0.84Stddev = 1.89
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2007Corr = 0.92Stddev = 1.97
2010Corr = 0.77Stddev = 1.87
2009Corr = 0.92Stddev = 1.97
Comparison of calculations with observations in the case of low clouds
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Comparison of calculations with observations in the case of high and mid clouds
2007Corr = 0.69Stddev = 2.81
2007Corr = 0.84Stddev = 2.10
Potential causes of errors
20% of FOV = 2 cloud amount
Rigid threshold
Using these parameters retrieved from the sky images we improve the accuracy of computation of cloud dependent radiative fluxes at sea surface.
Use only one threshold value - incorrectly . Within each series of photos needed correction threshold.
For further work with the cloud amount and the type of cloud we need to use Circular-Fisheye for crop matrix.
Conclusions: