| 晴空与有云大气辐射分布的数值模拟及其对全天空图像云识别的应用 |
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| 引用本文： | 霍娟,吕达仁.晴空与有云大气辐射分布的数值模拟及其对全天空图像云识别的应用[J].气象学报,2006,64(1):31-38. |
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| 作者姓名： | 霍娟 吕达仁 |
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| 作者单位： | 中国科学院大气物理研究所LAGEO,北京,100029 |
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| 基金项目： | 中国科学院资助项目;国家重点基础研究发展计划(973计划) |
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| 摘 要： | 全天空图像自动云识别的研究相对来说是一项较新的研究领域。当前国际上较为通用的全天空图像云识别方法主要依靠图像蓝红灰度值对比的阈值方法进行判断。然而非洁净大气中气溶胶的增多给云识别增加了难度,同时太阳高度角不同天空色度分布情况也不同。文中利用Libradtran辐射传输模式,计算了不同能见度不同太阳高度角情况下3个典型波长(450,550,650nm;蓝/绿/红)无云及有云大气的天空辐亮度分布情况,并进行了比较分析。结果表明,相同太阳高度角情况下,无云及有云大气中蓝红比值随能见度的下降呈单调下降趋势。在特定的云光学厚度和能见度情况下,天空色度彼此呈现出类似的分布状况。全天空图像阈值判断云识别自适应算法的建立需要与太阳高度角、地面能见度联系起来。当前尚无法建立一个判断阈值随太阳高度角以及能见度变化的函数关系式。较为可行的办法是建立典型能见度、典型太阳高度角情况下的辐射信息库,在具体云识别时,首先确定太阳高度角,而后根据天空辐射比情况确定天空能见度,并利用辐射信息标准库做云或非云判别。该工作为全天空云识别算法提供判别依据,同时建立云识别随能见度和太阳高度角变化的判别信息库。
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| 关 键 词： | Libradtran 全天空图像 色度 云识别 |
| 收稿时间： | 4/5/2005 12:00:00 AM |
| 修稿时间： | 2005年4月5日 |
CHARACTERISTICS AND DISTRIBUTION OF ALL SKY RADIANCE BY LIBRADTRAN
MODELING: FOR CLOUD DETERMINATION ALGORITHM IN ALL-SKY IMAGES |
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| Huo Juan and Lu Daren.CHARACTERISTICS AND DISTRIBUTION OF ALL SKY RADIANCE BY LIBRADTRAN
MODELING: FOR CLOUD DETERMINATION ALGORITHM IN ALL-SKY IMAGES[J].Acta Meteorologica Sinica,2006,64(1):31-38. |
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| Authors: | Huo Juan and Lu Daren |
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| Institution: | LAGEO,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;LAGEO,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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| Abstract: | Study of cloud's automatic detection from the all-sky images is a relative new research field. The current cloud decision algorithm is based on the blue/red ratios for all-sky visible images. However, the aerosol in the atmosphere has different scattering characteristics at different visibilities. So, this static algorithm is not suitable for all kinds of sky conditions. In order to improve the cloud algorithms, we calculated the all-sky radiance at three different wavelengths (450/550/650 nm) under different visibilities and solar angles by using the LIBRADTRAN model. The purpose of this work is to get the distribution character of radiance and radiative ratio (450/650 nm) under different sky conditions. Results show that blue/red radiative ratio will decrease with the decrease of visibility. And thin clouds often demonstrate the same radiative distribution as the aerosol. The radiative distributions of sky also are different at different solar angles. So, an automatic cloud detection algorithm must include a time-varying factor that is used to adapt to the variation of visibility and solar angle. At present, it is difficult to set up a general function of the visibility and the solar angle because of the complex sky conditions and relationship between them. The efficient and possible method for cloud detection is to set up a radiative dataset of different visibilities and solar angles. A standard database for cloud determination algorithm will be set up. |
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| Keywords: | Libradtran All-sky image Radiance Cloud determination |
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