.png){kind=icon}
返回
Convective Precipitation Nowcasting Using U-Net Model
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2021.3100847.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Radar
Radar imaging
Storms
Training
Meteorological radar
Correlation
Predictive models
Deep learning
precipitation nowcasting
U-Net
weather radar
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
Types of Parent Verbal Responsiveness That Predict Language in Young Children With Autism Spectrum Disorder预测自闭症谱系障碍幼儿语言的父母言语反应类型
OAAI
OAAI分享收藏The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm风暴细胞识别与跟踪算法: 一种增强的WSR-88D算法OAAI
OAAI分享收藏Comparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression properties.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容