Remote Sensing of deep convective clouds - A new satellite view

深对流云遥感——新的卫星视图

• 批准号: 19002093
• 负责人: Dr. Tobias Zinner
• 金额: --
• 依托单位: Meteorologisches Institut
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2006
• 资助国家: 德国
• 起止时间: 2005-12-31 至 2007-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/19002093?language=en
• 关键词: Remote; Sensing; deep; convective; clouds

Clouds and there evolution in a changing climate are a factor of major importance in the prediction of future climate due to their impact on the radiation budget of the earth. At the same time their variability in time and space is a source of uncertainties. Their global distribution and evolution can only be observed by satellite remote sensing. Thus, the uncertainties of standard remote sensing methods as well as the development of novel methods will be an important research topic for the next decade. The effects on existing methods due to (sub sensor resolution) cloud inhomogeneity have been investigated to some extent for low boundary layer clouds. However, no studies have yet been conducted concerning remote sensing of vertically more complex cloud types, e.g. deep convection, although their distinct three-dimensional structure is likely to introduce large errors into standard remote sensing methods. In this project the potential and the uncertainties of different, standard and novel, methods of remote sensing will be examined for deep convective cloud cases. The satellite observations corresponding to known cloud structures will be simulated and remote sensing techniques will be tested for the simulated data. To achieve this objective, complex cloud structures from physical cloud resolving modeling (CRM) will be used. A state-of-the-art Monte Carlo code for the three-dimensional simulation of radiative transfer will be used to simulate the radiance field. In this context, the Monte Carlo code will be optimized to reduce the extensive computing time necessary for 3D Monte Carlo simulations, in particular in the infra-red spectral range.

云及其在气候变化中的演变是预测未来气候的一个重要因素，因为它们对地球辐射收支有影响。与此同时，它们在时间和空间上的可变性是不确定性的一个来源。它们的全球分布和演变只有通过卫星遥感才能观测到。因此，标准遥感方法的不确定性以及新方法的发展将是未来十年的重要研究课题。对低边界层云，由于（亚传感器分辨率）云的不均匀性对现有方法的影响进行了一定程度的研究。然而，还没有对垂直方向上更复杂的云类，例如深对流进行遥感研究，尽管它们独特的三维结构可能会给标准遥感方法带来很大的误差。在本项目中，将对深对流云情况下不同的、标准的和新的遥感方法的潜力和不确定性进行审查。将模拟与已知云结构相对应的卫星观测，并将对模拟数据测试遥感技术。为了实现这一目标，将使用来自物理云解析建模（CRM）的复杂云结构。一个国家的最先进的蒙特卡洛代码的三维模拟辐射传输将被用来模拟辐射场。在这方面，蒙特卡洛代码将进行优化，以减少三维蒙特卡洛模拟所需的大量计算时间，特别是在红外光谱范围内。