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)

18

31

15

Days

- 6 0 - 4 0 - 2 0 0 2 0

- 6 0

- 4 0

- 2 0

0

2 0

4 0

6 0

8 0

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

0

1

2

3

4

5

6

7

8

9

10

IFM

Sky

Ind

ex

Comparisons of the SI algorithm cloud cover estimation with visual data

0 1 2 3 4 5 6 7 8 9 10Visual

0

1

2

3

4

5

6

7

8

9

10

Sk

y In

d

0 1 2 3 4 5 6 7 8 9 10Visual

0

1

2

3

4

5

6

7

8

9

10

Sky

Ind

0 1 2 3 4 5 6 7 8 9 10Visual

0

1

2

3

4

5

6

7

8

9

10

Sky

Ind

2007Corr = 0.79Stddev = 2.11

2010Corr = 0.78Stddev = 1.80

2009Corr = 0.84Stddev = 1.89

0 1 2 3 4 5 6 7 8 9 10

Visual

0

1

2

3

4

5

6

7

8

9

10

Sky

Ind

0 1 2 3 4 5 6 7 8 9 10Visual

0

1

2

3

4

5

6

7

8

9

10

Sky

Ind

0 1 2 3 4 5 6 7 8 9 10Visual

0

1

2

3

4

5

6

7

8

9

10

Sky

Ind

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

0 1 2 3 4 5 6 7 8 9 10

Visual

0

1

2

3

4

5

6

7

8

9

10

Sky

Ind

0 1 2 3 4 5 6 7 8 9 10Visual

0

1

2

3

4

5

6

7

8

9

10

Sky

Ind

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: