神经网络方法在复杂气候模式下模拟大气长波辐射的鲁棒性

V. Krasnopolsky, M. Fox-Rabinovitz, M. Chou
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引用次数: 6

摘要

在本文中,我们比较了为两种不同的复杂气候模式:NCAR CAM模式和NASA NSIPP模式开发的神经网络模拟。这些模型具有不同的动力学、不同的水平和垂直分辨率以及不同的物理特性(包括不同的长波辐射格式)。两种(NCAR和NASA)神经网络仿真的比较表明,它们在仿真的准确性与仿真神经网络的原始参数化和复杂性方面具有深刻的相似性,即我们的神经网络仿真方法的方法鲁棒性和可移植性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robustness of the NN Approach to emulating atmospheric long wave radiation in complex climate models
In this paper we present comparisons of NN emulations developed for two different complex climate models: NCAR CAM model and NASA NSIPP. These models have different dynamics, different horizontal and vertical resolutions and different physics (including different long wave radiation schemes). Comparison of two (NCAR and NASA) NN emulations shows their profound similarity in terms of the accuracy of emulation vs. the original parameterizations and complexity of emulating NNs, i.e. the methodological robustness and portability of our NN emulation approach.
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