Structural Forecasting for Short-term Tropical Cyclone Intensity Guidance

TL;DR

This paper presents a prototype deep learning model that uses geostationary satellite imagery to predict short-term tropical cyclone intensity and structure, demonstrating potential for operational guidance despite limited inputs.

Contribution

The study introduces a novel approach combining infrared imagery, a deep autoregressive model, and a CNN for short-term TC intensity forecasting using only satellite data and prior intensity estimates.

Findings

Prototype model achieves slightly higher error than official forecasts but offers interpretable structural insights.

Radial profiles from infrared imagery can reasonably predict short-term TC convective structure.

Model demonstrates the viability of satellite-based structural forecasting without environmental data.

Abstract

Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model which is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 hours prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 hours by applying a Deep Autoregressive Generative Model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a ``nowcasting'' convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center's official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance.