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Improved algorithms to generate 3-dimensional cloud fields for use in radiative transfer modelling

改进的算法生成用于辐射传输建模的 3 维云场

基本信息

批准号：
34100140
负责人：
Dr. Victor Venema
金额：
--
依托单位：
Institut für Geowissenschaften
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2007
资助国家：
德国
起止时间：
2006-12-31 至 2010-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/34100140?language=en
关键词：
Improved algorithms generate dimensional cloud

项目摘要

For solving multidimensional radiative transfer problems in the cloudy atmosphere, one needs not only a good Radiative Transfer (RT) model, but also realistic 2D or 3D cloud fields. The most used 3D cloud fields for research into cloud structure are fields from dynamical models or fractal cloud fields. We have developed algorithms to generate surrogate fields where the power spectrum and the shape of the distribu-tion can be chosen freely and independently. This facilitates sensitivity studies and allows generating fields that have a close fit with statistics from measurements. The latter is important since clouds have such a complicated structure that they cannot be measured fully. Therefore, methods to generate surrogate fields based on meas-urements are needed for empirical studies, such as closure studies or studies that need to bring measurements made at different scales together. In this project we would like to develop more versatile and better algorithms by 1) including multivariate statistics (to e.g. consider the structure in water content and effective radius simulta-neously), and 2) other structure measures (to generate better cumulus clouds) and 3) by including spatial information (making the algorithm an interpolation method that preserves structure, to be able to make closure studies with radiation point-measurements). We will generate surrogate fields from experimental data. These will be downloadable on the internet together with the algorithms.

要解决多云大气中的多维辐射传输问题，不仅需要一个好的辐射传输(RT)模型，而且需要逼真的二维或三维云场。研究云结构最常用的三维云场是动力学模型场或分形云场。我们开发了生成代理场的算法，其中功率谱和分布的形状可以自由和独立地选择。这有助于敏感性研究，并允许生成与测量统计数据非常吻合的场。后者很重要，因为云的结构非常复杂，无法完全测量。因此，经验研究需要基于测量生成替代场的方法，例如封闭研究或需要将不同尺度上的测量集合在一起的研究。在这个项目中，我们想要开发更通用和更好的算法，1)包括多元统计(例如，同时考虑含水率和有效半径中的结构)，2)其他结构措施(以生成更好的积云)，以及3)通过包括空间信息(使算法成为一种保留结构的内插方法，以便能够用辐射点测量进行闭合研究)。我们将从实验数据中生成代理域。这些算法将与算法一起在互联网上下载。