Qian Li;Jinrui Jing;Leiming Ma;Lei Chen;Shiqing Guo;Hanxing Chen;Tianying Wang;Yechao Xu
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摘要

天气雷达回波推断是天气预报的重要手段之一。在过去的十年中,深度学习给了它很大的启发。然而,回波演变过程的内部相似性却很少被利用。为了研究这一优点,本文提出了一种具有编码器-投影器结构的深度对比模型,该模型通过对比学习将从相同演化过程中采样的子序列投影到邻近的潜空间。因此,输入回声序列本身的内部演化相似性可以被发现和利用,从而促进预测。为了使训练更加平滑,我们还采用了从简单到困难的累积采样策略。在两个真实雷达数据集上的实验结果表明,我们的模型优于最先进的模型。此外,还分析并验证了采样策略的有效性以及在有限输入条件下的外推能力。训练代码和预训练模型见 https://github.com/tolearnmuch/ESCL。
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A Deep Contrastive Model for Radar Echo Extrapolation
Weather radar echo extrapolation is one of the essential means for weather nowcasting. It has been considerably inspired over the last decade by deep learning. However, the internal similarity of the echo evolution process has little been exploited. To investigate this merit, a deep contrastive model with an encoder–projector structure is proposed in this letter, which projects the subsequences sampled from the same evolution process into the neighborhood of latent space by contrastive learning. Thus, the internal evolution similarity of the input echo sequence itself can be discovered and exploited for promoting prediction. To make the training smoother, we also adopt a cumulative sampling strategy that follows a simple-to-hard manner. Experimental results on two real-world radar datasets demonstrate the superiority of our model in comparison to state-of-the-art. The effectiveness of the sampling strategy and extrapolation ability on limited input is also analyzed and verified. Training code and pretrained models are available at https://github.com/tolearnmuch/ESCL .
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