A Study of Upper Tropospheric Circulations over the Northern Hemisphere Prediction Using Multivariate Features by ConvLSTM

Phermphoonphiphat, Ekasit; Tomita, Tomohiko; Numao, Masayuki; Fukui, Kenichi

doi:10.1007/978-3-030-37442-6_12

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Li et al 31 proposed a prediction model of typhoon cloud system based on generative adversarial networks, 32 which effectively predicted the overall spatiotemporal evolution of typhoons. Phermphoonphiphat et al 33 proposed a prediction model based on ConvLSTM and introduced an up-down sampling operation to predict the upper tropospheric circulation in the Northern Hemisphere in the long and short term, with better accuracy than other existing models.…”

Section: Typhoon Predictionmentioning

confidence: 99%

SASTA-Net: self-attention spatiotemporal adversarial network for typhoon prediction

et al. 2022

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To solve the problems of poor authenticity and lack of clarity for short-time typhoon prediction, we propose a self-attentional spatiotemporal adversarial network (SASTA-Net). First, we introduce a multispatiotemporal feature fusion method to fully extract and fuse the multichannel spatiotemporal feature information to effectively enhance feature expression. Second, we propose an SATA-LSTM prediction model that incorporates spatial memory cell and attention mechanisms in order to capture spatial features and important details in sequences. Finally, a spatiotemporal 3D discriminator is designed to correctly distinguish the generated predicted cloud image from the real cloud image and generate a more accurate and real typhoon cloud image by adversarial training. The evaluation results on the typhoon cloud image data set show that the proposed SASTA-Net achieves 67.3, 0.878, 31.27, and 56.48 in mean square error, structural similarity, peak signal to noise ratio, and sharpness, respectively, which is superior to the most advanced prediction algorithm.

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Section: Typhoon Predictionmentioning

confidence: 99%

SASTA-Net: self-attention spatiotemporal adversarial network for typhoon prediction

et al. 2022

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“…ConvLSTM was tested on precipitation nowcasting (up to 6 hours) in Hongkong from radar echo dataset. Our previous work [16] also showed that ConvLSTM outperformed Convolutional Neural Networks (CNNs) and linear regression for predicting geopotential height at a pressure of 300 hPa.…”

Section: Introductionmentioning

confidence: 96%

Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast

et al. 2021

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Many machine-learning applications and methods are emerging to solve problems associated with spatiotemporal climate forecasting; however, a prediction algorithm that considers only short-range sequential information may not be adequate to deal with periodic patterns such as seasonality. In this paper, we adopt a Periodic Convolutional Recurrent Network (Periodic-CRN) model to employ the periodicity component in our proposals of the periodic representation dictionary (PRD). Phase shifts and non-stationarity of periodicity are the key components in the model to support. Specifically, we propose a Soft Periodic-CRN (SP-CRN) with three proposals of utilizing periodicity components: nearby-time (PRD-1), periodic-depth (PRD-2), and periodic-depth differencing (PRD-3) representation to improve climate forecasting accuracy. We experimented on geopotential height at 300 hPa (ZH300) and sea surface temperature (SST) datasets of ERA-Interim. The results showed the superiority of PRD-1 plus or minus one month of a prior cycle to capture the phase shift. In addition, PRD-3 considered only the depth of one differencing periodic cycle (i.e., the previous year) can significantly improve the prediction accuracy of ZH300 and SST. The mixed method of PRD-1, and PRD-3 (SP-CRN-1+3) showed a competitive or slight improvement over their base models. By adding the metadata component to indicate the month with one-hot encoding to SP-CRN-1+3, the prediction result was a drastic improvement. The results showed that the proposed method could learn four years of periodicity from the data, which may relate to the El Niño–Southern Oscillation (ENSO) cycle.

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A Study of Upper Tropospheric Circulations over the Northern Hemisphere Prediction Using Multivariate Features by ConvLSTM

Cited by 2 publications

References 10 publications

SASTA-Net: self-attention spatiotemporal adversarial network for typhoon prediction

SASTA-Net: self-attention spatiotemporal adversarial network for typhoon prediction

Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast

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