Learnable Optical Flow Network for Radar Echo Extrapolation

Zhang, Chengwei; Xudong, Zhou; Zhuge, Xiaoyong; Meng, Xu

doi:10.1109/jstars.2020.3031244

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…Some scholars have applied deep learning technology to weather forecasts and achieved satisfactory results. Traditional algorithms such as the optical flow method have the advantages of a good real-time performance and support from physical theory in radar echo extrapolation tasks [18,19].…”

Section: Related Workmentioning

confidence: 99%

A Generative Adversarial and Spatiotemporal Differential Fusion Method in Radar Echo Extrapolation

Niu,

Zhang,

Wang

et al. 2023

Remote Sensing

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As an important part of remote sensing data, weather radar plays an important role in convective weather forecasts to reduce extreme precipitation disasters. The existing radar echo extrapolation methods do not utilize the local natural characteristics of the radar echo effectively but only roughly extract the whole characteristics of the radar echo. To address these challenges, we design a spatiotemporal difference and generative adversarial fusion model (STDGAN). Specifically, a spatiotemporal difference module (STD) is designed to extract local weather patterns and model them in detail. In our model, spatiotemporal difference information and spatiotemporal features captured by the model itself are fused together. In addition, our model is trained in a generative adversarial network (GAN) framework; it helps to generate a clearer map of future radar echoes at the image level. The discriminator consists of multi-scale feature extractors, which can simulate weather models of various scales more completely. Finally, extrapolation experiments were conducted using actual radar echo data from Shijiazhuang and Nanjing. The experiments have shown that our model has a more accurate prediction performance for predicting local weather patterns and overall echo change trajectories compared with previous research models. Our model achieved MSE, PSNE, and SSIM values of 132.22, 37.87, and 0.796, respectively, on the Shijiazhuang radar echo dataset. In addition, our model also showed better performance results on the Nanjing radar echo dataset. The results show that the MSE was 49.570, the PSNR was 0.714, and the SSIM was 30.633. The CC value was 0.855.

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Section: Related Workmentioning

confidence: 99%

A Generative Adversarial and Spatiotemporal Differential Fusion Method in Radar Echo Extrapolation

Niu,

Zhang,

Wang

et al. 2023

Remote Sensing

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show abstract

“…This method uses the changes of pixels in the image sequence in the time domain to find the corresponding relationship between frames to predict future weather 5 – 8 However, this method does not make full use of the data of radar echo sequences, which also produces more cumulative errors when estimating the optical flow vector before extrapolation in the precipitation forecast.…”

Section: Introductionmentioning

confidence: 99%

Research on short-term and impending precipitation forecasting based on deep spatiotemporal network

Xie,

Miao,

et al. 2024

J. Electron. Imag.

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Precipitation forecasting has always been a challenging problem. Currently, meteorological radar echo data are used widely in the field of precipitation forecasting. From the radar echo images, the current precipitation situation can be obtained. Compared with the actual image, however, the radar echo extrapolation image has the disadvantages of echo loss and inaccurate echo tracks, which leads to low accuracy of the precipitation forecast. A more effective network based on the New-RainNet module for precipitation forecasting is proposed. The network is constructed by stacking New-RainNet modules that are established by adding a convolutional block attention module and a flexible switch from Adam to SGD optimization algorithm to the original convolutional long short-term memory network. The short-term and impending precipitation forecast in the next 0 to 60 min was successfully realized on the radar echo dataset of the Hong Kong Observatory and achieves satisfactory results. Experiments show that the network surpasses other methods in both network convergence and prediction accuracy. In addition, the radar echo image generated by the network prediction is also better than other methods in visual quality as it retains the image details and is closer to the real radar echo image than other methods.

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“…The cross-correlation methods calculate the correlation of each sub-region in two consecutive radar echo images to get the motion vectors [15]. These methods have low prediction accuracy regarding the condition that echoes evolve rapidly [16]. The centroid tracking methods regard the single radar echo as a 3-Dimensional entity, and then perform extrapolation by tracking its centroid; however, these methods are not applicable when echo splitting, or the convective weather is severe and complex [17].…”

Section: Introductionmentioning

confidence: 99%

CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Yang¹,

Liu²,

Wu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation, where radar echo maps were used to predict their consequent moment, so as to recognize potential severe convective weather events. However, these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation, due to the size limitation of convolution filter, lack of global feature, and less attention to features from previous states. To address the problems, this paper proposes a CEMA-LSTM recurrent unit, which is embedded with a Contextual Feature Correlation Enhancement Block (CEB) and a Multi-Attention Mechanism Block (MAB). The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction; the MAB uses a position and channel attention mechanism to capture global features of radar echoes. Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets. Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTM over recent models, e.g., PhyDNet, MIM and PredRNN++, etc. In particular, compared with the second-ranked model, its average POD, FAR and CSI have been improved by 3.87%, 1.65% and 1.79%, respectively on the FREM, and by 1.42%, 5.60% and 3.16%, respectively on the CIKM 2017.

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Learnable Optical Flow Network for Radar Echo Extrapolation

Cited by 11 publications

References 16 publications

A Generative Adversarial and Spatiotemporal Differential Fusion Method in Radar Echo Extrapolation

A Generative Adversarial and Spatiotemporal Differential Fusion Method in Radar Echo Extrapolation

Research on short-term and impending precipitation forecasting based on deep spatiotemporal network

CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

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