Cloud tomography applied to sky images: a virtual testbed
Two tomographic techniques are applied to two simulated sets of sky images with different cloud fraction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients without considering multiple scattering effects. Re...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/35270 |
| Acceso en línea: | https://hdl.handle.net/2454/35270 |
| Access Level: | acceso abierto |
| Palabra clave: | 3D cloud reconstruction Cloud optical depth Sky imager Solar forecasting Tomography |
| Sumario: | Two tomographic techniques are applied to two simulated sets of sky images with different cloud fraction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients without considering multiple scattering effects. Reconstruction accuracy is explored for different products, including surface irradiance and extinction coefficients, and as a function of the number of available sky imagers and setup distance. Increasing the number of imagers improves the accuracy of the 3-D reconstruction: for surface irradiance, the error decreases significantly up to four imagers at which point the improvements become marginal. But using nine imagers gives more robust results in practical situations in which the circumsolar region of images has to be excluded due to poor cloud detection. The ideal distance between imagers was also explored: for a cloud height of 1 km, increasing distance up to 3 km (the domain length) improved the 3-D reconstruction. An iterative reconstruction technique that iteratively updated the source function improved the results of the ART by minimizing the error between input red radiance images and reconstructed red radiance simulations. For the best case of a nine-imager deployment, the ART and iterative method resulted in 53.4% and 33.6% relative mean absolute error for the extinction coefficients, respectively. |
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