Few shot learning for Korean winter temperature forecasts

IF 8.5 1区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES npj Climate and Atmospheric Science Pub Date : 2024-11-12 DOI:10.1038/s41612-024-00813-z
Seol-Hee Oh, Yoo-Geun Ham
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Abstract

To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation has been achieved by using global climate model simulations, the proposed augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the Korean winter temperatures. The MAML-applied convolutional neural network (CNN) (referred to as the MAML model) demonstrates superior correlation skills for Korean temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Sensitivity experiments show that the domain-knowledge-based data augmentation enhances the forecast skill of the MAML model. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence Korean winter temperatures, resulting in more accurate predictions.

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学习韩国冬季气温预报的几个镜头
为了应对训练样本有限的挑战,本研究采用了模型无关元学习(MAML)算法和基于领域知识的数据扩增来预测朝鲜半岛的冬季气温。虽然数据扩增是通过全球气候模型模拟实现的,但建议的扩增则纯粹基于观测数据,利用与韩国冬季气温相关的大尺度气候变异来定义标签。与参考模型(即不含 MAML 的 CNN)和最先进的动态预报模型相比,应用了 MAML 的卷积神经网络(CNN)(简称 MAML 模型)在北方冬季所有目标前导月的韩国气温异常方面表现出卓越的相关性技能。灵敏度实验表明,基于领域知识的数据增强增强了 MAML 模型的预报技能。此外,闭塞敏感性结果表明,MAML 模型能更好地捕捉影响韩国冬季气温的物理前兆,从而获得更准确的预测结果。
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来源期刊
npj Climate and Atmospheric Science
npj Climate and Atmospheric Science Earth and Planetary Sciences-Atmospheric Science
CiteScore
8.80
自引率
3.30%
发文量
87
审稿时长
21 weeks
期刊介绍: npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols. The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.
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