Convective Precipitation Nowcasting Using U-Net Model
| العنوان: | Convective Precipitation Nowcasting Using U-Net Model |
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| المؤلفون: | He Liang, Lei Han, Wei Zhang, Haonan Chen, Yurong Ge |
| المصدر: | IGARSS |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Nowcasting, business.industry, Computer science, Deep learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, computer.software_genre, Translation (geometry), Convolutional neural network, Convolution, law.invention, Upsampling, Recurrent neural network, law, Radar imaging, General Earth and Planetary Sciences, Precipitation, Data mining, Artificial intelligence, Electrical and Electronic Engineering, Radar, business, computer, Remote sensing |
| الوصف: | Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications. |
| تدمد: | 1558-0644 0196-2892 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b81be9ff65fd0acd14b3dc15505f93a https://doi.org/10.1109/tgrs.2021.3100847 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....1b81be9ff65fd0acd14b3dc15505f93a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580644 01962892 |
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