  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.
