GAN-rcLSTM: A Deep Learning Model for Radar Echo Extrapolation Article Swipe
Huantong Geng
,
Tianlei Wang
,
Xiaoran Zhuang
,
Xi Du
,
Zhongyan Hu
,
Liangchao Geng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/atmos13050684
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/atmos13050684
The target of radar echo extrapolation is to predict the motion and development of radar echo in the future based on historical radar observation data. For such spatiotemporal prediction problems, a deep learning method based on Long Short-Term Memory (LSTM) networks has been widely used in recent years, although such models generally suffer from weak and blurry prediction. This paper proposes two models called Residual Convolution LSTM (rcLSTM) and Generative Adversarial Networks-rcLSTM (GAN-rcLSTM): The former introduces the residual module, and the latter introduces the discriminator. We use the historical data of 2017 and 2018 in the Jiangsu region as training and test sets. Experiments show that in long sequence forecasts, our model can provide more stable and clear images, while achieving higher CSI scores.
Related Topics
Concepts
Extrapolation
Discriminator
Residual
Radar
Computer science
Echo (communications protocol)
Deep learning
Artificial intelligence
Convolution (computer science)
Sequence (biology)
Term (time)
Generative grammar
Machine learning
Pattern recognition (psychology)
Algorithm
Artificial neural network
Telecommunications
Mathematics
Mathematical analysis
Biology
Quantum mechanics
Computer network
Physics
Genetics
Detector
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/atmos13050684
- https://www.mdpi.com/2073-4433/13/5/684/pdf?version=1650902568
- OA Status
- gold
- Cited By
- 12
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224320620
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224320620Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/atmos13050684Digital Object Identifier
- Title
-
GAN-rcLSTM: A Deep Learning Model for Radar Echo ExtrapolationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-24Full publication date if available
- Authors
-
Huantong Geng, Tianlei Wang, Xiaoran Zhuang, Xi Du, Zhongyan Hu, Liangchao GengList of authors in order
- Landing page
-
https://doi.org/10.3390/atmos13050684Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4433/13/5/684/pdf?version=1650902568Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2073-4433/13/5/684/pdf?version=1650902568Direct OA link when available
- Concepts
-
Extrapolation, Discriminator, Residual, Radar, Computer science, Echo (communications protocol), Deep learning, Artificial intelligence, Convolution (computer science), Sequence (biology), Term (time), Generative grammar, Machine learning, Pattern recognition (psychology), Algorithm, Artificial neural network, Telecommunications, Mathematics, Mathematical analysis, Biology, Quantum mechanics, Computer network, Physics, Genetics, DetectorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3, 2023: 5, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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