Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data

Abstract

The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing. Machine learning methods, that can capture non-linearities and complex relations, have only been scarcely tested for this application. We propose a neural network model fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We use a moving frame of reference that follows the storm center for the 24h tracking forecast. The network is trained to estimate the longitude and latitude displacement of tropical cyclones and depressions from a large database from both hemispheres (more than 3000 storms since 1979, sampled at a 6 hour frequency). The advantage of the fused network is demonstrated and a comparison with current forecast models shows that deep learning methods could provide a valuable and complementary prediction. Moreover, our method can give a forecast for a new storm in a few seconds, which is an important asset for real-time forecasts compared to traditional forecasts.

Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning

“…e average angular error of typhoon track prediction was thus reduced to 27.8 degrees, indicating the great potential of CNN in typhoon path prediction. Giffard-Roisin et al [12] proposed a fusion neural network comprising a neural network using past trajectory data and a CNN involving the reanalysis of atmospheric wind-field images. GAN-based methods: Rüttgers et al [8] used GAN in conjunction with satellite images and meteorological data to forecast the central location of typhoons.…”

“…Comparison with existing works: Finally, we compare our framework with several existing works [8,10,12,27]. According to the previous introduction, Rüttgers et al [8] introduced a GAN-based model, used satellite images as the input, and predicted locations after 6 hours.…”

“…Gao et al [10] introduced an LSTM-based model. e work by Giffard-Roisin et al [12] was based on CNN and feature fusion. Lv et al [27] used the least square method and FC network to predict the locations.…”

Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

“…Other research efforts have also used CNNs for hurricane intensity estimation application. Wimmers et al [36] used CNNs to estimate cyclone intensity using passive microwave imagery (37, Giffard-Roisin et al [37] uses track data and 3D reanalysis data as input to CNN, along with other features such as location information and maximal sustained windspeed to develop storm track models. They formulate the tracking problem as estimating the displacement between current location and future location of cyclone.…”

NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning

“…Additionally, deep learning models have been introduced in TC detection as well, for example, the use of deep neural networks (DNN) for existing TC detection [30], precursor detection of TCs [31], tropical and extratropical cyclone detection [32], TC track forecasting [33], and TC precursor detection by a cloud-resolving global nonhydrostatic atmospheric model [34]. However, deep learning models usually require a large number of training samples, because it is difficult to achieve high accuracy in case of finite training samples in computer vision and other fields [35][36][37].…”