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High Turbidity Solis Clear Sky Model: Development and Validation

Department F.-A. Forel for Environmental and Aquatic Sciences, Institute for Environmental Sciences, University of Geneva, 1205 Genève, Switzerland
Remote Sens. 2018, 10(3), 435; https://doi.org/10.3390/rs10030435
Received: 8 January 2018 / Revised: 15 February 2018 / Accepted: 8 March 2018 / Published: 10 March 2018
(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing)
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Abstract

The Solis clear sky model is a spectral scheme based on radiative transfer calculations and the Lambert–Beer relation. Its broadband version is a simplified fast analytical version; it is limited to broadband aerosol optical depths lower than 0.45, which is a weakness when applied in countries with very high turbidity such as China or India. In order to extend the use of the original simplified version of the model for high turbidity values, we developed a new version of the broadband Solis model based on radiative transfer calculations, valid for turbidity values up to 7, for the three components, global, beam, and diffuse, and for the four aerosol types defined by Shettle and Fenn. A validation of low turbidity data acquired in Geneva shows slightly better results than the previous version. On data acquired at sites presenting higher turbidity data, the bias stays within ±4% for the beam and the global irradiances, and the standard deviation around 5% for clean and stable condition data and around 12% for questionable data and variable sky conditions. View Full-Text
Keywords: Solis scheme; clear sky; radiation model; radiative transfer; high turbidity; water vapor Solis scheme; clear sky; radiation model; radiative transfer; high turbidity; water vapor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ineichen, P. High Turbidity Solis Clear Sky Model: Development and Validation. Remote Sens. 2018, 10, 435.

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