A Generative Adversarial Gated Recurrent Unit Model for Precipitation Nowcasting

Tian, Lin; Li, Xutao; Ye, Yunming; Xie, Pengfei; Li, Yan

doi:10.1109/lgrs.2019.2926776

Cited by 86 publications

(58 citation statements)

References 8 publications

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“…ConvGRU layers learn the appropriate update rules from one time step to the next, enabling the GAN generator to model the evolution of the fields with time, and allowing the discriminator to evaluate the plausibility of image sequences rather than single images. These layers, along with the closely related convolutional long short-term memory (LSTM) layers, have been previously applied to modeling the time evolution of precipitation fields [30], [31].…”

Section: B Network Architecturementioning

confidence: 99%

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Leinonen

Nerini

Berne

2021

IEEE Trans. Geosci. Remote Sensing

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Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling" in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resolution atmospheric fields for an input consisting of a low-resolution sequence of images of the same field. We test the GAN using two data sets: one consisting of radar-measured precipitation from Switzerland; the other of cloud optical thickness derived from the Geostationary Earth Observing Satellite 16 (GOES-16). We find that the GAN can generate realistic, temporally consistent super-resolution sequences for both data sets. The statistical properties of the generated ensemble are analyzed using rank statistics, a method adapted from ensemble weather forecasting; these analyses indicate that the GAN produces close to the correct amount of variability in its outputs. As the GAN generator is fully convolutional, it can be applied after training to input images larger than the images used to train it. It is also able to generate time series much longer than the training sequences, as demonstrated by applying the generator to a three-month data set of the precipitation radar data. The source code to our GAN is available at https://github.com/jleinonen/downscaling-rnn-gan.

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Section: B Network Architecturementioning

confidence: 99%

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Leinonen

Nerini

Berne

2021

IEEE Trans. Geosci. Remote Sensing

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“…Convolutional GRU (ConvGRU) architecture is widely used for learning spatio-temporal features from videos [ 53 , 54 , 55 ]. Therefore, we decided to implement a ConvGRU-based neural network architecture.…”

Section: Related Workmentioning

confidence: 99%

An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques

Nasri

Orts-Escolano

Cazorla

2020

Sensors

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In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that leverages a deep learning-based architecture for real-time gesture recognition. The 3D game experience developed in the study is focused on rehabilitation exercises, allowing individuals with certain disabilities to use low-cost sEMG sensors to control the game experience. For this purpose, we acquired a novel dataset of seven gestures using the Myo armband device, which we utilized to train the proposed deep learning model. The signals captured were used as an input of a Conv-GRU architecture to classify the gestures. Further, we ran a live system with the participation of different individuals and analyzed the neural network’s classification for hand gestures. Finally, we also evaluated our system, testing it for 20 rounds with new participants and analyzed its results in a user study.

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“…In recent years, artificial intelligence has become the new engine of the global scientific and technological revolution, and some scholars have applied machine learning and deep learning to precipitation forecasting [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. After Shi et al [ 14 ] achieved precipitation intensity nowcasting by radar echo extrapolation, it has emerged as a hot research topic in the meteorological community.…”

Section: Introductionmentioning

confidence: 99%

RN-Net: A Deep Learning Approach to 0–2 Hour Rainfall Nowcasting Based on Radar and Automatic Weather Station Data

Zhang

Wang

Guan

et al. 2021

Sensors

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Precipitation has an important impact on people’s daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Furthermore, existing rainfall nowcasting methods based on machine learning and deep learning cannot provide large-area rainfall nowcasting with high spatiotemporal resolution. This paper proposes a dual-input dual-encoder recurrent neural network, namely Rainfall Nowcasting Network (RN-Net), to solve this problem. It takes the past grid rainfall data interpolated by automatic weather stations and doppler radar mosaic data as input data, and then forecasts the grid rainfall data for the next 2 h. We conduct experiments on the Southeastern China dataset. With a threshold of 0.25 mm, the RN-Net’s rainfall nowcasting threat scores have reached 0.523, 0.503, and 0.435 within 0.5 h, 1 h, and 2 h. Compared with the Weather Research and Forecasting model rainfall nowcasting, the threat scores have been increased by nearly four times, three times, and three times, respectively.

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A Generative Adversarial Gated Recurrent Unit Model for Precipitation Nowcasting

Cited by 86 publications

References 8 publications

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques

RN-Net: A Deep Learning Approach to 0–2 Hour Rainfall Nowcasting Based on Radar and Automatic Weather Station Data

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