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Visual analysis of meteorological satellite data via model-agnostic meta-learning
Journal of Visualization ( IF 1.7 ) Pub Date : 2021-01-02 , DOI: 10.1007/s12650-020-00704-4
Shiyu Cheng , Hanwei Shen , Guihua Shan , Beifang Niu , Weihua Bai

Abstract Satellites detect the distribution of meteorological data worldwide. However, due to the orbital constraints, the satellite can only reach the same area again after one orbiting cycle. The interval between two detections in the same area is long, and the variation of meteorological data between the two detections is unknown. Moreover, meteorological satellite data are only located near the orbit in one cycle, while the global distribution of meteorological data is unknown. Our method allows to train a regression model with only few meteorological satellite data by taking advantage of the recent advances in deep learning. In detail, we train a model-agnostic meta-learning (MAML) model with data from ground stations instead of meteorological satellites and get the initial network parameters. Based on the initial network parameters trained by MAML, we train the regression models again for different areas. We sample the regression curves of all areas by time and get a time series of global meteorological data distribution. Through case studies conducted together with domain experts, we validate the effectiveness of our method. Graphic abstract

中文翻译:

通过与模型无关的元学习对气象卫星数据进行可视化分析

摘要 卫星检测全球气象数据的分布。但由于轨道限制,卫星只能在一个轨道周期后才能再次到达同一区域。同一地区两次探测的间隔时间长,两次探测之间的气象数据变化未知。而且,气象卫星数据仅在一个周期内位于轨道附近,而气象数据的全球分布是未知的。我们的方法允许通过利用深度学习的最新进展来训练只有少量气象卫星数据的回归模型。具体来说,我们使用来自地面站而不是气象卫星的数据训练模型无关元学习 (MAML) 模型,并获得初始网络参数。基于 MAML 训练的初始网络参数,我们针对不同区域再次训练回归模型。我们按时间对所有区域的回归曲线进行采样,得到全球气象数据分布的时间序列。通过与领域专家一起进行的案例研究,我们验证了我们方法的有效性。图形摘要
更新日期:2021-01-02
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