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Durham Research Online
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Modeling cumulus cloud scenes from high-resolution satellite images.

Zhang, Zili and Liang, Xiaohui and Yuan, Chunqiang and Li, Frederick W.B. (2017) 'Modeling cumulus cloud scenes from high-resolution satellite images.', Computer graphics forum., 36 (7). pp. 229-238.

Abstract

We present a reconstruction framework, which fits physically-based constraints to model large-scale cloud scenes from satellite images. Applications include weather phenomena visualization, flight simulation, and weather spotter training. In our method, the cloud shape is assumed to be composed of a cloud top surface and a nearly flat cloud base surface. Based on this, an effective method of multi-spectral data processing is developed to obtain relevant information for calculating the cloud base height and the cloud top height, including ground temperature, cloud top temperature and cloud shadow. A lapse rate model is proposed to formulate cloud shape as an implicit function of temperature lapse rate and cloud base temperature. After obtaining initial cloud shapes, we enrich the shapes by a fractal method and represent reconstructed clouds by a particle system. Experiment results demonstrate the capability of our method in generating physically sound large-scale cloud scenes from high-resolution satellite images.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1111/cgf.13288
Publisher statement:This is the accepted version of the following article: Zhang, Zili, Liang, Xiaohui, Yuan, Chunqiang & Li, Frederick W.B. (2017). Modeling Cumulus Cloud Scenes from High-resolution Satellite Images. Computer Graphics Forum 36(7): 229-238, which has been published in final form at https://doi.org/10.1111/cgf.13288. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Date accepted:29 July 2017
Date deposited:18 September 2017
Date of first online publication:13 October 2017
Date first made open access:13 October 2018

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