8 research outputs found
CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis
Convolutional neural networks (CNN) have achieved great success in analyzing
tropical cyclones (TC) with satellite images in several tasks, such as TC
intensity estimation. In contrast, TC structure, which is conventionally
described by a few parameters estimated subjectively by meteorology
specialists, is still hard to be profiled objectively and routinely. This study
applies CNN on satellite images to create the entire TC structure profiles,
covering all the structural parameters. By utilizing the meteorological domain
knowledge to construct TC wind profiles based on historical structure
parameters, we provide valuable labels for training in our newly released
benchmark dataset. With such a dataset, we hope to attract more attention to
this crucial issue among data scientists. Meanwhile, a baseline is established
with a specialized convolutional model operating on polar-coordinates. We
discovered that it is more feasible and physically reasonable to extract
structural information on polar-coordinates, instead of Cartesian coordinates,
according to a TC's rotational and spiral natures. Experimental results on the
released benchmark dataset verified the robustness of the proposed model and
demonstrated the potential for applying deep learning techniques for this
barely developed yet important topic.Comment: Submitted to AAAI202
Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data
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 within every 15 minute period, while passive microwave rain rate (PMW) is available 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 during the training phase including VIS and PMW information 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.
Please visit https://github.com/BoyoChen/CNN-GAN-TC for codes and implementation details