论文标题
实时热带气旋强度通过处理时间异质卫星数据估计
Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data
论文作者
论文摘要
分析由多个高级传感器在各种卫星平台上收集的大地地球物理观察数据，从而促进了我们对地球物理系统的理解。例如，卷积神经网络（CNN）在基于具有固定时间频率的卫星数据（例如3小时）的卫星数据估算热带旋风（TC）强度方面取得了巨大成功。但是，为了实现更及时的（30分钟）和准确的TC强度估计，需要深入学习模型来处理时间均质的卫星观测。具体而言，每15分钟以下每15分钟就可以使用红外线（IR1）和水蒸气（WV）图像，而被动微波降雨率（PMW）约每3小时约每3小时。同时，可见的（VIS）通道受到噪音和阳光强度的严重影响，因此难以利用它。因此，我们提出了一个新型框架，将生成性对抗网络（GAN）与CNN结合在一起。该模型在训练阶段利用所有数据，包括VIS和PMW信息，最终仅使用高频IR1和WV数据来提供预测阶段的强度估计。实验结果表明，混合GAN-CNN框架的精度与最先进的模型相当，同时具有将最大估计频率从3小时提高到小于15分钟的能力。
Analyzing big geophysical observational data collected by multiple advanced sensors on various satellite platforms promotes our understanding of the geophysical system. For instance, convolutional neural networks (CNN) have achieved great success in estimating tropical cyclone (TC) intensity based on satellite data with fixed temporal frequency (e.g., 3 h). However, to achieve more timely (under 30 min) and accurate TC intensity estimates, a deep learning model is demanded to handle temporally-heterogeneous satellite observations. Specifically, infrared (IR1) and water vapor (WV) images are available under every 15 minutes, while passive microwave rain rate (PMW) is available for about every 3 hours. Meanwhile, the visible (VIS) channel is severely affected by noise and sunlight intensity, making it difficult to be utilized. Therefore, we propose a novel framework that combines generative adversarial network (GAN) with CNN. The model utilizes all data, including VIS and PMW information, during the training phase and eventually uses only the high-frequent IR1 and WV data for providing intensity estimates during the predicting phase. Experimental results demonstrate that the hybrid GAN-CNN framework achieves comparable precision to the state-of-the-art models, while possessing the capability of increasing the maximum estimation frequency from 3 hours to less than 15 minutes.
