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Cloud cover estimation optical package: New facility, algorithms and techniques
Author(s) -
Mikhail Krinitskiy
Publication year - 2017
Publication title -
aip conference proceedings
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.177
H-Index - 75
eISSN - 1551-7616
pISSN - 0094-243X
DOI - 10.1063/1.4975540
Subject(s) - cloud computing , computer science , cloud cover , algorithm , cover (algebra) , feature (linguistics) , artificial neural network , data mining , remote sensing , reliability (semiconductor) , machine learning , artificial intelligence , engineering , mechanical engineering , physics , geology , operating system , linguistics , philosophy , power (physics) , quantum mechanics
Short- and long-wave radiation is an important component of surface heat budget over sea and land. For estimating them accurate observations of the cloud cover are needed. While massively observed visually, for building accurate parameterizations cloud cover needs also to be quantified using precise instrumental measurements. Major disadvantages of the most of existing cloud-cameras are associated with their complicated design and inaccuracy of post-processing algorithms which typically result in the uncertainties of 20% to 30% in the camera-based estimates of cloud cover. The accuracy of these types of algorithm in terms of true scoring compared to human-observed values is typically less than 10%.We developed new generation package for cloud cover estimating, which provides much more accurate results and also allows for measuring additional characteristics. New algorithm, namely SAIL GrIx, based on routine approach, also developed for this package. It uses the synthetic controlling index (“grayness rate ...

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