Generative Modeling of Atmospheric Convection
Generative Modeling of Atmospheric Convection
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DOI：
10.1145/3429309.3429324
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
G. Mooers;Jens Tuyls;S. Mandt;M. Pritchard;T. Beucler
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文献类型：
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作者：
G. Mooers;Jens Tuyls;S. Mandt;M. Pritchard;T. Beucler
虽然云解析模式可以明确地模拟小尺度风暴形成和形态的细节，但由于缺乏计算资源，这些细节往往被气候模式忽略。在这里，我们探索生成建模的潜力，以廉价地重新创建小规模的风暴，通过设计和实施变分自动编码器（VAE），执行结构复制，降维，高分辨率的垂直速度场的聚类。VAE在跨越地球仪的106个样本上进行了训练，成功地重建了对流的空间结构，对对流组织状态进行了无监督聚类，并识别了异常风暴活动，证实了生成建模在气候模型中为对流随机参数化提供动力的潜力。
While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of computational resources. Here, we explore the potential of generative modeling to cheaply recreate small-scale storms by designing and implementing a Variational Autoencoder (VAE) that performs structural replication, dimensionality reduction, and clustering of high-resolution vertical velocity fields. Trained on ∼ 6 · 106 samples spanning the globe, the VAE successfully reconstructs the spatial structure of convection, performs unsupervised clustering of convective organization regimes, and identifies anomalous storm activity, confirming the potential of generative modeling to power stochastic parameterizations of convection in climate models.