Prediction of a typhoon track using a generative adversarial network and satellite images

Rüttgers, Mario; Lee, Sang-Seung; Jeon, Seun; You, Donghyun

doi:10.1038/s41598-019-42339-y

Cited by 114 publications

(66 citation statements)

References 25 publications

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“…Moradi Kordmahalleh et al (2016) was tested on 6h-and 12h-forecast on only 4 tropical cyclones, while Gao et al (2018) was tested on only Northwest Pacific tracks. Rüttgers et al (2018) proposes to use a generative adversarial network (GAN) to generate the future atmospheric image (harder problem), but only for a 6 hour prediction. Another study uses storm tracks and reanalysis maps as input for a hybrid CNN -LSTM network in order to learn the (x,y) tracking coordinates (Mudigonda et al (2017)).…”

Section: Machine Learning and Deep Learning In Forecasting Problemsmentioning

confidence: 99%

“…When dealing with image-like data, these studies consider a fixed regional map for tracking storms, of size 160 x 80 deg (longitude/latitude) for Mudigonda et al (2017) and of the size of the Korean peninsula area (around 30 x 30 deg) for Rüttgers et al (2018). However, a fixed region for tropical cyclone forecast has three major limitations.…”

Section: Frame Of Referencementioning

confidence: 99%

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

Giffard‐Roisin¹,

Yang²,

Charpiat³

et al. 2020

Front. Big Data

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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.

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Section: Machine Learning and Deep Learning In Forecasting Problemsmentioning

confidence: 99%

Section: Frame Of Referencementioning

confidence: 99%

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

Giffard‐Roisin¹,

Yang²,

Charpiat³

et al. 2020

Front. Big Data

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“…During the typhoon outbreak, it is difficult to obtain typhoon data directly from conventional climate and ocean monitoring data, which makes it difficult to predict typhoons [ 4 ]. With the improvement of the satellite remote sensing technology, meteorological satellite cloud pictures can more accurately and stably monitor the weather changes in real-time in all weathers, becoming the main means of observing and predicting typhoons [ 5 ]. Research on using satellite cloud pictures has achieved a series of results in the process of typhoon generation and development [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Zhou

Xiang

Huang

2020

Sensors

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Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional–Long Short-Term Memory Network (GC–LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010–2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that: the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.35%, and the prediction accuracy of typhoons and super typhoons reaches 95.12%. The model can accurately identify typhoon eye and spiral cloud belt, and the prediction results are always kept in the minimum range compared with the actual results, which proves that the GC–LSTM model has stronger stability. The model can accurately identify the levels of different typhoons according to the satellite cloud pictures. In summary, the results can provide a theoretical basis for the related research of typhoon level classification.

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“…Recently, some studies have attempted to adapt data-driven methods to forecast typhoons. For example, Rüttgers et al [8] adapted a generative adversarial network (GAN) with satellite images as inputs for typhoon track predictions, reducing the errors between predicted and real typhoon centers measured quantitatively to kilometers. Precise forecasts may support decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

Historical Typhoon Search Engine Based on Track Similarity

Tsai

Chan

Hsieh

et al. 2019

IJERPH

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The potential effect of a typhoon track on the extent of damage makes the track a critical factor during the emergency response phase. Historical typhoon data may provide information for decision makers to anticipate the impact of an upcoming typhoon and develop prevention strategies to reduce the damage. In our preliminary work, we proposed a track similarity algorithm and implemented a real-time search engine for past typhoon events. However, the proposed algorithm was not discussed thoroughly in the preliminary work, and the great number of historical typhoon track records slowed down the similarity calculations. In addition, the tool did not feature the option of automatically importing upcoming typhoon track predictions. This research introduces the assumption of the recentness dominance principle (RDP), explores the details of the track similarity algorithm of the preliminary work, completes the discussion of parameter setting, and developed a method to improve the efficiency of the similarity calculation. In this research, we implemented the proposed advanced methodology by developing a new information display panel featuring the ability to auto-import forecast data. The results of this study provide decision makers and the public with a concise and handy search engine for searching similar historical typhoon records.

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Prediction of a typhoon track using a generative adversarial network and satellite images

Cited by 114 publications

References 25 publications

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

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

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Historical Typhoon Search Engine Based on Track Similarity

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