用神经网络同时压缩和重建多高度风数据，提升分辨率与存储效率。
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data
- 用隐式神经表示联合实现降维与超分辨率重建。
- 在多种气候条件下提升数据分辨率并降低存储开销。
- 支持不同高度风速数据间的跨层预测，适合气候建模者使用。
全球正转向风能等可再生能源以减少温室气体排放。为提升风数据的分析与存储效率，我们提出一种深度学习框架，可同时实现多高度风数据的高效降维与连续表示。该框架包含降维、跨模态预测与超分辨率三个核心组件，旨在：(1) 在多种气候条件下恢复高分辨率细节；(2) 降低数据维度以提升大规模气候数据集的存储效率；(3) 实现不同高度风速数据间的跨层预测。全面测试表明，该方法在超分辨率质量与压缩效率上均优于现有技术。
原文摘要 · Abstract (English)
The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming. To enhance the analysis and storage of wind data, we introduce a deep learning framework designed to simultaneously enable effective dimensionality reduction and continuous representation of multi-altitude wind data from discrete observations. The framework consists of three key components: dimensionality reduction, cross-modal prediction, and super-resolution. We aim to: (1) improve data resolution across diverse climatic conditions to recover high-resolution details; (2) reduce data dimensionality for more efficient storage of large climate datasets; and (3) enable cross-prediction between wind data measured at different heights. Comprehensive testing confirms that our approach surpasses existing methods in both super-resolution quality and compression efficiency.
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