论文标题
热带气旋强度预测的结构预测：通过深度学习提供见解
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning
论文作者
论文摘要
热带气旋（TC）强度预测最终由人类预测者发行。人体内管道要求，如果要在国家飓风中心（National Hurricane Center）等运营中心采用，则必须轻松地被TC专家挖掘任何预测指南。我们提出的框架利用深度学习为预测者提供端到端预测模型和传统强度指导的事物：一种强大的工具，用于监视关键与物理相关的高度相关预测指标的高维度序列，以及了解预测因子如何相互关系并与短期强度变化相关的手段。
Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestible by TC experts if it is to be adopted at operational centers like the National Hurricane Center. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does: a powerful tool for monitoring high-dimensional time series of key physically relevant predictors and the means to understand how the predictors relate to one another and to short-term intensity changes.
