Radiative transfer simulations (RTS) still face significant challenges in accurately representing the highly complex gas absorption spectra of the Earth’s atmosphere. Line-by-line RTS achieves high accuracy by solving radiative transfer equations for narrow spectral intervals, but at a considerable computational cost. Especially in remote sensing and climate modeling, a trade-off between efficiency and accuracy must be done. k-distribution methods are widespread in the scientific community and offer a way to make this trade-off. k-distribution methods reorder the absorption spectra k for a given spectral interval and find appropriate so-called k-bins. In the k-space much less integration points can be used, while maintaining high accuracy. The way to find optimal k-bins differs from method to method and depends on the application. In this paper, we present the flexible and fast k-bin tool. The python based lightweight k-bin tool provides a variety of different k-distribution methods and configuration options. One k-distribution method is the in-house developed k-bin approach. The different setups of the tool can be easily compared, helping to decide which method and configuration is best suited for a given application. We encourage the user of the tool to continue to optimize the k-bin tool and to extend it with new approaches and functionalities.

中文翻译：

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k-bin 工具：用 Python 编写的快速灵活的 k 分布算法


辐射传输模拟 （RTS） 在准确表示地球大气高度复杂的气体吸收光谱方面仍然面临重大挑战。逐行 RTS 通过求解窄光谱间隔的辐射传输方程来实现高精度，但计算成本相当高。特别是在遥感和气候建模中，必须在效率和精度之间进行权衡。k 分布方法在科学界很普遍，并提供了一种进行这种权衡的方法。k 分布方法为给定的光谱间隔对吸收光谱 k 重新排序，并找到合适的所谓的 k bin。在 k 空间中，可以使用的积分点要少得多，同时保持高精度。找到最佳 k bin的方法因方法而异，并且取决于应用程序。在本文中，我们介绍了灵活快速的 k-bin 工具。基于 python 的轻量级 k-bin 工具提供了多种不同的 k 分布方法和配置选项。一种 k 分布方法是内部开发的 k-bin 方法。可以轻松比较该工具的不同设置，从而有助于确定哪种方法和配置最适合给定的应用。我们鼓励该工具的用户继续优化 k-bin 工具，并使用新的方法和功能对其进行扩展。