1 / 11

Estimation of fractional cloud cover from all-sky digital images at sea

Estimation of fractional cloud cover from all-sky digital images at sea. Alexey Sinitsyn Sergey Gulev Krinitsky Mikhail. Motivation and background MORE cruises: all-sky digital images of cloud Testing existing algorithm for estimation Comprising existing algorithm with visual:

mandar
Télécharger la présentation

Estimation of fractional cloud cover from all-sky digital images at sea

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Estimation of fractional cloud cover from all-sky digital images at sea Alexey Sinitsyn Sergey Gulev Krinitsky Mikhail • 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

  2. Motivation: • Accurate estimation of cloud fraction is critical for the quantitative computation of surface short wave and long • wave 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

  3. MORE cruises: 2007-2010 (for all-sky image) Days • October 2007 R/V “Academician Ioffe”Santa Cruz (Spain) –Montevideo (Uruguay) • October – November 2009 R/V “Academician Ioffe” Kaliningrad (Russia) – CapeTown (RSA) • September 2010 R/V “Academician Ioffe” cape Farewell (Greenland) – Szczecin (Poland) • 18 • 31 • 15

  4. Sigma 8mm F3,5 EX DG Circular-Fisheye 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 Example of all-sky digital image • 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)

  5. Existing algorithm for cloud recognizing (i) Sky Index for Yamashita M.and etc, 2004: Sky Index (SI) = (DNBlue – DNRed) / (DNBlue + DNRed), DNBlue = digital number of blue channel DNRed = digital number of red channel Empirical threshold 0,1. If SI <= 0,1 then consider pixel as cloudy (ii) Sky Index forKalisch J., Macke A. (IFM-GEOMAR), 2008: Sky Index (IFM SI) = (DNRed) / (DNBlue ), DNBlue = digital number of blue channel DNRed = digital number of red channel Empirical threshold 0,8. If SI > 0,8 then consider pixel as cloudy

  6. Comparisons of the algorithm cloud cover estimation for batch processing Applied to batch processing both algorithm for cloud estimation shown same result. Corr = 0.99 Stddev = 0.19

  7. Comparisons of the SI algorithm cloud cover estimation with visual data 2009 Corr = 0.84 Stddev = 1.89 2007 Corr = 0.79 Stddev = 2.11 2010 Corr = 0.78 Stddev = 1.80

  8. Comparison of calculations with observations in the case of low clouds 2009 Corr = 0.92 Stddev = 1.97 2007 Corr = 0.92 Stddev = 1.97 2010 Corr = 0.77 Stddev = 1.87

  9. Comparison of calculations with observations in the case of high and mid clouds 2007 Corr = 0.69 Stddev = 2.81 2007 Corr = 0.84 Stddev = 2.10

  10. Potential causes of errors 20% of FOV = 2 cloud amount Rigid threshold

  11. Conclusions: • 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.

More Related