A method of radar echo extrapolation based on dilated convolution and attention convolution

Shen, Xiajiong; Meng, Kunying; Zhang, Lei; Zuo, Xianyu

doi:10.1038/s41598-022-13969-6

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They integrated temporal and spatial information specific to radar data and proposed a versatile radar precipitation forecasting model called RainNet [22], which demonstrated universality for radar precipitation forecasting tasks. Furthermore, Shen et al proposed a new radar echo extrapolation model named ADC_Net based on dilated convolutions and attention mechanisms to enhance the utilization and accuracy of radar extrapolation information, effectively improving the accuracy of radar echo extrapolation [23].…”

Section: Introductionmentioning

confidence: 99%

Residual Spatiotemporal Convolutional Neural Network Based on Multisource Fusion Data for Approaching Precipitation Forecasting

Zhang,

Wang,

Huang

et al. 2024

Atmosphere

View full text Add to dashboard Cite

Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model’s predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area.

show abstract

Section: Introductionmentioning

confidence: 99%

Residual Spatiotemporal Convolutional Neural Network Based on Multisource Fusion Data for Approaching Precipitation Forecasting

Zhang,

Wang,

Huang

et al. 2024

Atmosphere

View full text Add to dashboard Cite

show abstract

A Generative Adversarial Model for Weather Radar Echo Extrapolation based on Cycle Generation Structure

Ma,

Liu,

Zhang

et al. 2023

2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

View full text Add to dashboard Cite

Short-Term Precipitation Radar Echo Extrapolation Method Based on the MS-DD3D-RSTN Network and STLoss Function

Yang,

Zhou

et al. 2024

Sensors

View full text Add to dashboard Cite

Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficient capture of spatiotemporal dependencies, and low accuracy in radar echo extrapolation, we propose a new model: MS-DD3D-RSTN. This model employs spatiotemporal convolutional blocks (STCBs) as spatiotemporal feature extractors and uses the spatial-temporal loss (STLoss) function to learn intra-frame and inter-frame changes for end-to-end training, thereby capturing the spatiotemporal dependencies in radar echo signals. Experiments on the Sichuan dataset and the HKO-7 dataset show that the proposed model outperforms advanced models in terms of CSI and POD evaluation metrics. For 2 h forecasts with 20 dBZ and 30 dBZ reflectivity thresholds, the CSI metrics reached 0.538, 0.386, 0.485, and 0.198, respectively, representing the best levels among existing methods. The experiments demonstrate that the MS-DD3D-RSTN model enhances the ability to capture spatiotemporal dependencies, mitigates forecast degradation, and further improves radar echo prediction performance.

show abstract

A method of radar echo extrapolation based on dilated convolution and attention convolution

Cited by 3 publications

References 16 publications

Residual Spatiotemporal Convolutional Neural Network Based on Multisource Fusion Data for Approaching Precipitation Forecasting

Residual Spatiotemporal Convolutional Neural Network Based on Multisource Fusion Data for Approaching Precipitation Forecasting

A Generative Adversarial Model for Weather Radar Echo Extrapolation based on Cycle Generation Structure

Short-Term Precipitation Radar Echo Extrapolation Method Based on the MS-DD3D-RSTN Network and STLoss Function

Contact Info

Product

Resources

About