Using Conditional Generative Adversarial 3-D Convolutional Neural Network for Precise Radar Extrapolation

Wang, Cong; Wang, Ping; Xue, Bing; Wang, Di

doi:10.1109/jstars.2021.3083647

Cited by 27 publications

(16 citation statements)

References 54 publications

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“…A twostage extrapolation model based on 3D Convolutional Neural Network (3D-CNN) and Conditional Generative Adversarial Network (CGAN) named ExtGAN was defined. This model can more accurately forecast convective cells that usually lead to severe hazards [24]. A new ConvRNN model of energybased GAN named EBGAN-Forecaster was built.…”

Section: B Research Progress Of Radar Echo Extrapolationmentioning

confidence: 99%

Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

Liu¹,

Yang²,

Ji³

et al. 2023

Preprint

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Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this paper, recent relevant scientific investigation and practical efforts using Deep Learning (DL) models for weather radar data analysis and pattern recognition have been reviewed; particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization. In addition to recent achievements, the latest advancements and existing challenges are also presented and discussed in this paper, trying to lead to reasonable potentials and trends in this highly-concerned field.

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Section: B Research Progress Of Radar Echo Extrapolationmentioning

confidence: 99%

Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

Liu¹,

Yang²,

Ji³

et al. 2023

Preprint

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“…Recently, DGNs, especially generative adversarial networks (GANs), have proven the ability of generating new realistic-looking samples [30][31][32][33][34][35][36]. In radar, GAN has been applied in various applications, such as meteorological radar extrapolation [37], synthetic aperture radar (SAR) image enhancement [38] and optical and SAR image matching [39]. As for radar data generation, most existing works focus on SAR image synthesis.…”

Section: Introductionmentioning

confidence: 99%

Multi-View HRRP Generation With Aspect-Directed Attention GAN

Song

Zhou

Yang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In radar automatic target recognition (RATR), highresolution range profile (HRRP) has received intensive attention due to its low computational cost. As HRRP is sensitive to the aspect of the target, a training set covering sufficient aspects is essential to the success of a RATR model, which is however intractable in complex environment with noncooperative targets. In this paper, an aspect-directed attention generative adversarial network (ADA-GAN) is proposed to generate multi-view HRRPs using real samples from few aspects. The key is that the HRRPs from the similar targets share the same aspect variation pattern. Hence, an HRRP is decomposed into its identity and aspect features via an aspect-directed disentangled representation network with self-attention modules. In the training stage, the decomposition network and the aspect variation pattern are learned from full aspect samples of cooperative targets. When generation, the desired multi-view HRRPs of the noncooperative target are synthesized by its identity features extracted from few aspect samples and the learned aspect variation pattern. Three types of experiments on the simulated and measured datasets demonstrate the generation performances of our method. First, the generated HRRPs are visually compared with the truth. Second, the similarity of the scattering center power and handcrafted feature distributions are quantitatively evaluated. Finally, recognition experiments verify the feasibility of data augmentation with the generated HRRPs. Extensive results show the superior performance of our method over other state-of-theart methods.

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“…The calculation of the relationship between lightning frequency and other thundercloud parameters shows that lightning frequency is correlated with radar reflectivity, precipitation rate, updraft velocity, cloud radius, ice crystal concentration, and shotgun particles [16]. More importantly, significant progress has been made in radar extrapolation, which can show weather conditions from one to two hours into the future [17,18]. Lightning location data combined with radar product data can provide better lightning monitoring and early warning.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data

Zhang

Chen

et al. 2022

Remote Sensing

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Lightning is an important cause of casualties, and of the interruption of power supply and distribution facilities. Monitoring lightning locations is essential in disaster prevention and mitigation. Although there are many ways to obtain lightning information, there are still substantial problems in intelligent lightning monitoring. Deep learning combined with weather radar data and land attribute data can lay the foundation for future monitoring of lightning locations. Therefore, based on the residual network, the Lightning Monitoring Residual Network (LM-ResNet) is proposed in this paper to monitor lightning location. Furthermore, comparisons with GoogLeNet and DenseNet were also conducted to evaluate the proposed model. The results show that the LM-ResNet model has significant potential in monitoring lightning locations. In this study, we converted the lightning monitoring problem into a binary classification problem and then obtained weather radar product data (including the plan position indicator (PPI), composite reflectance (CR), echo top (ET), vertical integral liquid water (VIL), and average radial velocity (V)) and land attribute data (including aspect, slope, land use, and NDVI) to establish a lightning feature dataset. During model training, the focal loss function was adopted as a loss function to address the constructed imbalanced lightning feature dataset. Moreover, we conducted stepwise sensitivity analysis and single factor sensitivity analysis. The results of stepwise sensitivity analysis show that the best performance can be achieved using all the data, followed by the combination of PPI, CR, ET, and VIL. The single factor sensitivity analysis results show that the ET radar product data are very important for the monitoring of lightning locations, and the NDVI land attribute data also make significant contributions.

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Using Conditional Generative Adversarial 3-D Convolutional Neural Network for Precise Radar Extrapolation

Cited by 27 publications

References 54 publications

Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

Multi-View HRRP Generation With Aspect-Directed Attention GAN

Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data

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