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首页> 外文期刊>Nonlinear processes in geophysics >An artificial neural network technique for downscaling GCM outputs to RCM spatial scale
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An artificial neural network technique for downscaling GCM outputs to RCM spatial scale

机译：一种将GCM输出缩减为RCM空间尺度的人工神经网络技术

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摘要

An Artificial Neural Network (ANN) approach is used to downscale ECHAM5 GCM temperature (iT/i) and rainfall (iR/i) fields to RegCM3 regional model scale over Europe. The main inputs to the neural network were the ECHAM5 fields and topography, and RegCM3 topography. An ANN trained for the period 1960–1980 was able to recreate the RegCM3 1981–2000 mean iT/i and iR/i fields with reasonable accuracy. The ANN showed an improvement over a simple lapse-rate correction method for iT/i, although the ANN iR/i field did not capture all the fine-scale detail of the RCM field. An ANN trained over a smaller area of Southern Europe was able to capture this detail with more precision. The ANN was unable to accurately recreate the RCM climate change (CC) signal between 1981–2000 and 2081–2100, and it is suggested that this is because the relationship between the GCM fields, RCM fields and topography is not constant with time and changing climate. An ANN trained with three ten-year "time-slices" was able to better reproduce the RCM CC signal, particularly for the full European domain. This approach shows encouraging results but will need further refinement before becoming a viable supplement to dynamical regional climate modelling of temperature and rainfall.
机译：使用人工神经网络（ANN）方法将ECHAM5 GCM温度（ T ）和降雨（ R ）字段缩减到欧洲的RegCM3区域模型规模。神经网络的主要输入是ECHAM5场和地形，以及RegCM3地形。经过1960–1980年训练的ANN能够以合理的精度重新创建RegCM3 1981–2000平均 T 和 R 字段。尽管ANN R 字段未捕获RCM字段的所有精细细节，但ANN相对于 T 的简单失误率校正方法显示出改进。在南欧较小地区接受培训的ANN能够更精确地捕获此细节。人工神经网络无法准确地再现1981–2000年和2081–2100年之间的RCM气候变化（CC）信号，这表明这是因为GCM场，RCM场和地形之间的关系不是随时间和变化而恒定的气候。经过三个十年的“时间片”训练的ANN能够更好地重现RCM CC信号，尤其是在整个欧洲范围内。这种方法显示出令人鼓舞的结果，但在成为动态的区域温度和降雨动态气候模型的可行补充之前，需要进一步完善。

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