Predicting Hurricane Trajectories using a Recurrent Neural Network

Alemany, Sheila; Beltran, Jonathan; Pérez, Adrián Santamaría; Ganzfried, Sam

doi:10.48550/arxiv.1802.02548

Cited by 6 publications

(5 citation statements)

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“…Given the complexity of the dependencies between and other atmospheric variables, as well as the importance of the interaction with the site-specific topography, the potential of sophisticated machine learning techniques could be tested to improve the model parametrizations of , as already successfully done with other atmospheric phenomena (Sharma et al, 2011;Xingjian et al, 2015;Alemany et al, 2018;Gentine et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

Bodini¹,

Lundquist²,

Kirincich³

2020

J. Phys.: Conf. Ser.

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Key Points:• strength and persistence of wind plant wakes depends on turbulence dissipation rate • turbulence dissipation rate offshore is small with a weak diurnal cycle • dissipation rate is larger when flow is from the land, which tends to be in wintertime at this site AbstractThe rapid growth of offshore wind energy requires accurate modeling of the wind resource, which can be depleted by wind farm wakes. Turbulence dissipation rate ( ) governs the accuracy of model predictions of hub-height wind speed and the development and erosion of wakes. Here we assess the variability of turbulence kinetic energy and using 13 months of observations from a profiling lidar deployed on a platform off the Massachusetts coast. Offshore, is 2 orders of magnitude smaller than onshore, with a subtle diurnal cycle. Wind direction largely influences the annual cycle of turbulence, with larger values in winter when the wind flows from the land, and smaller values in summer, when the wind is mainly from open ocean. Because of the weak turbulence, wind plant wakes will be stronger and persist farther downwind in summer.

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Section: Discussionmentioning

confidence: 99%

Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

Bodini¹,

Lundquist²,

Kirincich³

2020

J. Phys.: Conf. Ser.

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“…They have used dynamic time warping (DTW), which lets the neural network learn information equally from each hurricane. Alemany et al 6 have also used a RNN to predict hurricane trajectories, but instead of assuming monotonic behavior of all hurricanes they focus on temporal and unique features of each cyclone. By learning extracted parameters, like the angle of travel or wind speed, their forecast results could achieve a better accuracy than the results of Moradi Kordmahalleh et al.…”

Section: Introductionmentioning

confidence: 99%

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

Rüttgers

Lee

Jeon

et al. 2019

Sci Rep

114

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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 predicted and real typhoon centers are measured quantitatively in kilometers. An averaged error of 95.6 km is achieved for tested 10 typhoons. Predicting sudden changes of the track in westward or northward directions is identified as a challenging task, while the prediction is significantly improved, when velocity fields are employed along with satellite images.

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“…They have used Dynamic Time Warping (DTW), which lets the neural network learn information equally from each hurricane. Alemany et al (2018) 10 have also used a RNN to predict hurricane trajectories, but instead of assuming monotonic behavior of all hurricanes they focus on temporal and unique features of each cyclone. By learning extracted parameters, like the angle of travel or wind speed, their forecast results could achieve better accuracies than the results of Moradi Kordmahalleh et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Typhoon track prediction using satellite images in a Generative Adversarial Network

Rüttgers¹,

Lee²,

You³

2018

Preprint

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Tracks of typhoons are predicted using satellite images as input for a Generative Adversarial Network (GAN). The satellite images have time gaps of 6 hours and are marked with a red square at the location of the typhoon center. The GAN uses images from the past to generate an image one time step ahead. The generated image shows the future location of the typhoon center, as well as the future cloud structures. The errors between predicted and real typhoon centers are measured quantitatively in kilometers. 42.4% of all typhoon center predictions have absolute errors of less than 80 km, 32.1% lie within a range of 80 -120 km and the remaining 25.5% have accuracies above 120 km. The relative error sets the above mentioned absolute error in relation to the distance that has been traveled by a typhoon over the past 6 hours. High relative errors are found in three types of situations, when a typhoon moves on the open sea far away from land, when a typhoon changes its course suddenly and when a typhoon is about to hit the mainland. The cloud structure prediction is evaluated qualitatively. It is shown that the GAN is able to predict trends in cloud motion. In order to improve both, the typhoon center and cloud motion prediction, the present study suggests to add information about the sea surface temperature, surface pressure and velocity fields to the input data.

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Predicting Hurricane Trajectories using a Recurrent Neural Network

Cited by 6 publications

References 0 publications

Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

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

Typhoon track prediction using satellite images in a Generative Adversarial Network

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