WF-UNet: Weather Fusion UNet for Precipitation Nowcasting

Kaparakis, Christos; Mehrkanoon, Siamak

doi:10.48550/arxiv.2302.04102

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…FureNet [18] adds two additional encoders to UNet for multimodal learning. WF-UNet [19] uses a 3D Unet model to integrate precipitation and wind speed variables as inputs to the learning process and analyzes the impact on the precipitation target task. Broad-UNet [20] is equipped with asymmetric parallel convolution as well as the Atrous Spatial Pyramid Pooling (ASPP) [21] module, which learns more complex patterns by combining multi-scale features while using fewer parameters than the core UNet model.…”

Section: Related Workmentioning

confidence: 99%

MSLKNet: A Multi-Scale Large Kernel Convolutional Network for Radar Extrapolation

Tian,

Wang,

Shen

et al. 2023

Atmosphere

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Radar echo extrapolation provides important information for precipitation nowcasting. Existing mainstream radar echo extrapolation methods are based on the Single-Input-Single-Output (SISO) architecture. These approaches of recursively predicting the predictive echo image with the current echo image as input often results in error accumulation, leading to severe performance degradation. In addition, the echo motion variations are extremely complex. Different regions of strong or weak echoes should receive different degrees of attention. Previous methods have not been specifically designed for this aspect. This paper proposes a new radar echo extrapolation network based entirely on a convolutional neural network (CNN). The network uses a Multi-Input-Multi-Output (MIMO) architecture to mitigate cumulative errors. It incorporates a multi-scale, large kernel convolutional attention module that enhances the extraction of both local and global information. This design results in improved performance while significantly reducing training costs. Experiments on dual-polarization radar echo datasets from Shijiazhuang and Nanjing show that the proposed fully CNN-based model can achieve better performance while reducing computational cost.

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Section: Related Workmentioning

confidence: 99%

MSLKNet: A Multi-Scale Large Kernel Convolutional Network for Radar Extrapolation

Tian,

Wang,

Shen

et al. 2023

Atmosphere

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“…Among the DL models used in precipitation forecasting, the U-Net model has achieved significant success [23][24][25]. U-Net, a CNN-based network, can simulate nonlinear functions flexibly and efficiently [26,27].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Tropical Cyclone Precipitation Prediction in the Northwest Pacific Using Deep Learning Models and Ensemble Techniques

He,

Li,

Zhang

et al. 2024

Water

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This study focuses on optimizing precipitation forecast induced by tropical cyclones (TCs) in the Northwest Pacific region, with lead times ranging from 6 to 72 h. The research employs deep learning models, such as U-Net, UNet3+, SE-Net, and SE-UNet3+, which utilize precipitation forecast data from the Global Forecast System (GFS) and real-time GFS environmental background data using a U-Net structure. To comprehensively make use of the precipitation forecasts from these models, we additionally use probabilistic matching (PM) and simple averaging (AVR) in rainfall prediction. The precipitation data from the Global Precipitation Measurement (GPM) Mission serves as the rainfall observation. The results demonstrate that the root mean squared errors (RMSEs) of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are lowered by 8.7%, 10.1%, 9.7%, 10.0%, 11.4%, and 11.5%, respectively, when compared with the RMSE of the GFS TC precipitation forecasts, while the mean absolute errors are reduced by 9.6%, 11.3%, 9.0%, 12.0%, 12.8%, and 13.0%, respectively. Furthermore, the neural network model improves the precipitation threat scores (TSs). On average, the TSs of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are raised by 12.8%, 21.3%, 19.3%, 20.7%, 22.5%, and 22.9%, respectively, compared with the GFS model. Notably, AVR and PM outperform all other individual models, with PM’s performance slightly better than AVR’s. The most important feature variables in optimizing TC precipitation forecast in the Northwest Pacific region based on the UNet-based neural network include GFS precipitation forecast data, land and sea masks, latitudinal winds at 500 hPa, and vertical winds at 500 hPa.

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“…SmaAt-UNet can effectively reduce the model parameter size while maintaining a comparable performance in predicting precipitation in 30 min. Kaparakis et al [27] integrated precipitation and wind speed variables into a 3D UNet core-model for 3 h precipitation nowcasting over western Europe. Chen et al [28] proposed a 3D CNN to predict daily total precipitation for next several days.…”

Section: Introductionmentioning

confidence: 99%

STPF-Net: Short-Term Precipitation Forecast Based on a Recurrent Neural Network

Wang,

Guan

et al. 2023

Remote Sensing

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Accurate and timely precipitation forecasts are critical in modern society, influencing both economic activity and daily life. While deep learning methods leveraging remotely sensed radar data have become prevalent for precipitation nowcasting, longer-term forecasting remains challenging. This is due to accumulated errors in deep learning models and insufficient information about precipitation systems over longer time horizons. To address these challenges, we introduce the Short-Term Precipitation Forecast Network (STPF-Net), a recurrent neural network designed for longer-term precipitation prediction. STPF-Net uses a multi-tier structure with varying temporal resolutions to mitigate the accumulated errors during longer forecasts. Additionally, its transformer-based module incorporates larger spatial contexts, providing more complete information about precipitation systems. We evaluated STPF-Net on radar data from southeastern China, training separate models for 6 and 12 h forecasts. Quantitative results demonstrate STPF-Net achieved superior accuracy and lower errors compared to benchmark deep learning and numerical weather prediction models. Visualized case studies indicate reasonably coherent 6 h predictions from STPF-Net versus other methods. For 12 h forecasts, while STPF-Net outperformed other models, it still struggled with storm initiation over longer forecasting time.

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WF-UNet: Weather Fusion UNet for Precipitation Nowcasting

Cited by 3 publications

References 39 publications

MSLKNet: A Multi-Scale Large Kernel Convolutional Network for Radar Extrapolation

MSLKNet: A Multi-Scale Large Kernel Convolutional Network for Radar Extrapolation

Enhanced Tropical Cyclone Precipitation Prediction in the Northwest Pacific Using Deep Learning Models and Ensemble Techniques

STPF-Net: Short-Term Precipitation Forecast Based on a Recurrent Neural Network

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