Enhancing rainfall prediction accuracy through image fusion of radar and numerical weather prediction models

초록

Precipitation is one of the most challenging atmospheric phenomena to predict due to the complexity involved in solving dynamic and thermodynamic atmospheric equations. To address this challenge, extensive research has been conducted to enhance the precision of numerical weather prediction models and radar-based extrapolation data, in conjunction with the development of various blending techniques. However, traditional methods have proven insufficient in capturing the diversity and nonlinearity of weather phenomena. In response to these limitations, this study introduces a novel methodology that leverages machine-learning based image fusion models to merge radar-based extrapolation and numerical weather prediction rainfall datasets, thereby enhancing prediction accuracy. An image fusion model was developed using radar-based extrapolation data and numerical weather prediction data as input datasets, with radar observation data utilized as target dataset. To identify the most suitable image fusion model for capturing the complex patterns of rainfall data, two experiments were conducted: 1) Impact of model topology, and 2) Effect of model size. A systematic analysis of the model outputs was performed using eight evaluation metrics categorized under pixel-based metrics, featurebased metrics, structural similarity metrics, and categorical verification metrics. Experimental results indicated that image fusion model based on a Residual Network (ResNet) outperformed other models in terms of model topology. Regarding model size, it was observed that the performance did not increase proportionally with the number of residual blocks; the most suitable performance was achieved with a specific number of residual blocks (Case 5: 8 blocks). Additionally, the metrics compared with radar observation data indicated that the proposed model delivered superior performance, thus offering a high-accuracy rainfall prediction methodology.

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