2019
DOI: 10.1038/s41598-019-42339-y
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Prediction of a typhoon track using a generative adversarial network and satellite images

Abstract: Tracks of typhoons are predicted using a generative adversarial network (GAN) with satellite images as inputs. Time series of satellite images of typhoons which occurred in the Korea Peninsula in the past are used to train the neural network. The trained GAN is employed to produce a 6-hour-advance track of a typhoon for which the GAN was not trained. The predicted track image of a typhoon favorably identifies the future location of the typhoon center as well as the deformed cloud structures. Errors between pre… Show more

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Cited by 96 publications
(49 citation statements)
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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%
See 1 more Smart Citation
“…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%
“…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%
“…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%