RainPredRNN: A New Approach for Precipitation Nowcasting with Weather Radar Echo Images Based on Deep Learning

Tuyen, Do Ngoc; Tuấn, Trần Mạnh; Le, Xuan-Hien; Tùng, Nguyễn Thanh; Chau, Tran Kim; Hai, Pham Van; Gerogiannis, Vassilis C.; Sơn, Lê Hoàng

doi:10.3390/axioms11030107

Cited by 24 publications

(9 citation statements)

References 32 publications

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“…Wu et al [22] presented ISA-PredRNN, integrating a self-attention mechanism and long-term memory to enhance capability in handling global and long-term dependencies. Tuyen et al [23] proposed the RainPredRNN model, which combines PredRNN-V2 and U-Net [24], which not only reduces the amount of computation for model prediction but also improves performance.…”

Section: Related Workmentioning

confidence: 99%

DSADNet: A Dual-Source Attention Dynamic Neural Network for Precipitation Nowcasting

Yao,

Ji,

Wang

et al. 2024

Sustainability

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Accurate precipitation nowcasting is of great significance for flood prevention, agricultural production, and public safety. In recent years, spatiotemporal sequence models based on deep learning have been widely used for precipitation nowcasting and have achieved better prediction results than traditional methods. These models commonly use radar echo extrapolation and utilize the Z-R relationship between radar and rainfall to predict rainfall. However, radar echo data can be affected by various noises, and the Z-R correlation linking radar and rainfall encompasses several variables influenced by factors like terrain, climate, and seasonal variations. To solve this problem, we propose a dual-source attention dynamic neural network (DSADNet) for precipitation nowcasting, which is a network model that utilizes a fusion module to extract valid information from radar maps and rainfall maps, together with dynamic convolution and the attention mechanism, to directly predict future rainfall through encoding and decoding structure. We conducted experiments on a real dataset in Jiangsu, China, and the experimental results show that our model had better performance than the other examined models.

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Section: Related Workmentioning

confidence: 99%

DSADNet: A Dual-Source Attention Dynamic Neural Network for Precipitation Nowcasting

Yao,

Ji,

Wang

et al. 2024

Sustainability

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“…Theoretically, the information encoding and processing in CNN is implemented through mathematical transformations of the convolutional, pooling, and fully connected layers (in certain cases). For complex tasks that require a substantial computational operation, the architecture of CNN can be a flexible stacking of various layers [28] or can also be flexibly combined with other types of architecture such as encoder-decoder [29] or LSTM [30,31].…”

Section: Cae Modelmentioning

confidence: 99%

A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin

Lee

Nguyen

2023

Remote Sensing

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Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of precipitation data is on the distinctive geographical features of each location, they remain at a considerable distance from station-based data. This paper examines the effectiveness of a convolutional autoencoder (CAE) architecture in pixel-by-pixel bias correction of SP products for the Mekong River Basin (MRB). Two satellite-based products (TRMM and PERSIANN-CDR) and a gauge-based product (APHRODITE) are gridded rainfall products mined in this experiment. According to the estimated statistical criteria, the CAE model was effective in reducing the gap between SP products and benchmark data both in terms of spatial and temporal correlations. The two corrected SP products (CAE_TRMM and CAE_CDR) performed competitively, with CAE TRMM appearing to have a slight advantage over CAE CDR, however, the difference was minor. This study’s findings proved the effectiveness of deep learning-based models (here CAE) for bias correction of SP products. We believe that this technique will be a feasible alternative for delivering an up-to-current and reliable dataset for MRB studies, given that the sole available gauge-based dataset for this area has been out of date for a long time.

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“…LSTM has recently displayed its superiority in addressing tasks involving sequential data, particularly in short-term forecasting [50], [51], [52]. However, the bias-correction domain for SPPs has not yet witnessed the prevalence of LSTM-based models, even though these precipitation data are likewise gathered over time.…”

Section: Convlstmmentioning

confidence: 99%

Performance Comparison of Bias-Corrected Satellite Precipitation Products by Various Deep Learning Schemes

Nguyen

Lee

2023

IEEE Trans. Geosci. Remote Sensing

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Precipitation observations from a ground-based gauge provide a reliable data source for hydrological and climatological studies. However, these data are sparse in many regions of the world, particularly the Mekong River Basin (MRB). Satellite-based precipitation products (SPPs) are the sole data source available with worldwide coverage. Despite this, there is a mismatch between SPPs and gauge-based observations, and the correct procedures should be utilized to minimize systematic bias in SPPs. This study aimed to benchmark the efficacy of four state-of-the-art bias-correcting deep learning models (DLMs) for the tropical rainfall measuring mission-based precipitation product named TRMM_3B42 (hereafter TRMM) over the entire MRB. These models were designed mainly based on convolutional neural network (CNN) and encoder-decoder (ENDE) architectures, including ConvENDE, ConvUNET, ConvINCE, and ConvLSTM. The bias-corrected dataset by DLMs was then confirmed against the gauge-based dataset (Asian precipitationhighly resolved observational data integration toward evaluation of water resources, APHRODITE). From the results obtained, all four DLMs effectively minimized the bias of the TRMM product. Among them, ConvENDE and ConvUNET had a higher consistency and performance level compared to ConvINCE and ConvLSTM. Additionally, the complexity of DLMs did not enhance their efficiency, as is the case with ConvINCE and ConvLSTM, despite using many computing resources. Given the observed data shortage for the MRB since 2016, the application of DLMs, such as ConvENDE and ConvUNET, can serve to improve the reliability of existing rainfall datasets and provide valuable input for various research purposes in the MRB.

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RainPredRNN: A New Approach for Precipitation Nowcasting with Weather Radar Echo Images Based on Deep Learning

Cited by 24 publications

References 32 publications

DSADNet: A Dual-Source Attention Dynamic Neural Network for Precipitation Nowcasting

DSADNet: A Dual-Source Attention Dynamic Neural Network for Precipitation Nowcasting

A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin

Performance Comparison of Bias-Corrected Satellite Precipitation Products by Various Deep Learning Schemes

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