用神经网络提升气象模型边界数据精度，改善区域天气预报。
Improving regional weather forecasts with neural interpolation
- 结合图像超分与残差网络设计神经插值算子
- 在多尺度网格间实现动态数据精准映射
- 适合气象建模与高精度预报研究者
本文设计了一种神经插值算子，用于改进区域气象模型的边界数据，该问题具有挑战性，因需在不同网格分辨率间映射多尺度动力学。我们通过简化模型研究提出方法，并旨在将结果推广至区域气象模型的动力核心。方法融合卷积神经网络（CNN）与残差网络技术，同时将大气动力学过程嵌入神经网络结构中，以提升插值精度与物理一致性。
原文摘要 · Abstract (English)
In this paper we design a neural interpolation operator to improve the boundary data for regional weather models, which is a challenging problem as we are required to map multi-scale dynamics between grid resolutions. In particular, we expose a methodology for approaching the problem through the study of a simplified model, with a view to generalise the results in this work to the dynamical core of regional weather models. Our approach will exploit a combination of techniques from image super-resolution with convolutional neural networks (CNNs) and residual networks, in addition to building the flow of atmospheric dynamics into the neural network
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