ORCAst用多阶段网络提升一周内海流实时预测精度
ORCAst: Operational High-Resolution Current Forecasts
- 分阶段融合卫星与浮标数据，先预测海面高度再推算海流
- 在多个区域训练后，预测性能优于现有最先进方法
- 适合海洋气象、航运和气候研究者使用
我们提出ORCAst，一种用于一周内高分辨率海流实时预报的多阶段、多分支网络。由于卫星遥感数据信息间接或不完整，实时预测海面海流极具挑战。模型完全基于真实卫星数据与漂流器现场观测进行训练，通过多阶段学习，利用多种地面真值数据预测全球海面海流。其多分支编码器-解码器结构首先从大量星载及SWOT高度计数据中预测海面高度和地转海流，再基于更稀疏的漂流器观测数据学习海流预测。在特定区域训练可提升性能。相比多种先进方法，该模型在海流现在预报与预测上表现更优。
原文摘要 · Abstract (English)
We present ORCAst, a multi-stage, multi-arm network for Operational high-Resolution Current forecAsts over one week. Producing real-time nowcasts and forecasts of ocean surface currents is a challenging problem due to indirect or incomplete information from satellite remote sensing data. Entirely trained on real satellite data and in situ measurements from drifters, our model learns to forecast global ocean surface currents using various sources of ground truth observations in a multi-stage learning procedure. Our multi-arm encoder-decoder model architecture allows us to first predict sea surface height and geostrophic currents from larger quantities of nadir and SWOT altimetry data, before learning to predict ocean surface currents from much more sparse in situ measurements from drifters. Training our model on specific regions improves performance. Our model achieves stronger nowcast and forecast performance in predicting ocean surface currents than various state-of-the-art methods.
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