Machine learning emulation of a local-scale UK climate model
Machine learning emulation of a local-scale UK climate model
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DOI：
10.48550/arxiv.2211.16116
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Henry Addison;E. Kendon;Suman V. Ravuri;L. Aitchison;Peter A. G. Watson
中科院分区：
文献类型：
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作者：
Henry Addison;E. Kendon;Suman V. Ravuri;L. Aitchison;Peter A. G. Watson
气候变化正在导致极端降雨加剧。高空间分辨率的降水预测对于社会为这些变化做好准备非常重要，例如模拟洪水影响。用于创建这种投影的基于物理的模拟在计算上非常昂贵。这项工作证明了扩散模型的有效性，一种形式的深生成模型，用于生成更便宜的现实的高分辨率降雨样本，为英国条件下的数据从低分辨率模拟。我们首次展示了一种机器学习模型，该模型能够基于解决大气对流的物理模型生成高分辨率降雨的真实样本，这是极端降雨背后的关键过程。通过向低分辨率相对涡度添加自学的特定位置信息，样本的分位数和时间均值与高分辨率模拟的对应物匹配良好。
Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is able to produce realistic samples of high-resolution rainfall based on a physical model that resolves atmospheric convection, a key process behind extreme rainfall. By adding self-learnt, location-specific information to low resolution relative vorticity, quantiles and time-mean of the samples match well their counterparts from the high-resolution simulation.