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Derivation of 3D cloud animation from geostationary satellite images

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Abstract

Large-scale cloud animation is crucial to TV weather presentation, weather observer training and video products. In this paper, a physically based system is presented for the derivation of time-varying 3D clouds from geostationary satellite images. Cloud properties are derived from a set of meteorological models while the clouds are rendered by graphics models, the proposed method thus presents a new modeling methodology, which integrates the reality of the data with the realistic visual feeling. In particular, image pixels are first classified into cloud-free, water cloud, ice cloud, thin cirrus cloud in terms of their spectral signature. Then, cloud top surface, cloud bottom surface and cloud extinction are generated by applying different combinations of images. Finally, clouds are rendered under various light directions or view directions. The results have indicated that the proposed method can yield a realistic and approximately valid clouds with similar appearance to those in the input satellite images.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (No. 61170186) and the National High Technology Research and Development Program of China (No. 2013AA013701). We would like to thank the anonymous reviewers for helpful comments, and Professor Jiming Sun for the useful discussions on cloud physics.

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Authors and Affiliations

  1. The State Key Laboratory Of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Xiaohui Liang & Chunqiang Yuan

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  1. Xiaohui Liang
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  2. Chunqiang Yuan
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Correspondence to Xiaohui Liang.

Appendix: The reflectance in MWIR image

Appendix: The reflectance in MWIR image

We use satellite images in longitude/latitude projection and the longitude and latitude for all pixels are sampled with equal spacing(0.05∘). For each pixel, the azimuth and zenith angles of the satellite and those of the sun are obtained by using the method of [26]. The measured MWIR radiance \(B_{\lambda _{mwir}}(T_{mwir})\) contains both thermal emission and solar reflection R at daytime [32]:

$$ B_{\lambda_{mwir}}(T_{mwir})=(1-\Re_{mwir})B_{\lambda_{mwir}}(T_{ir1})+\Re_{mwir}R $$
(A.1)

where \(R=\frac {S_{0}}{\pi }{d^{2}_{SE}}\cos (v_{0})\), S 0 is the constant solar spectral irradiance at 3.75μ m , d S E is the relative Sun-Earth distance, v 0 is the solar zenith angle, and λ m w i r = 3.75μ m. From (A.1), the reflection function is

$$ \Re_{mwir}=\frac{B_{\lambda_{mwir}}(T_{mwir})-B_{\lambda_{mwir}}(T_{ir1})}{R-B_{\lambda_{mwir}}(T_{ir1})} $$
(A.2)

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Liang, X., Yuan, C. Derivation of 3D cloud animation from geostationary satellite images. Multimed Tools Appl 75, 8217–8237 (2016). https://doi.org/10.1007/s11042-015-2738-7

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