Abstract
Quantitative precipitation prediction is essential for managing water-related disasters, including floods, landslides, tsunamis, and droughts. Recent advances in data-driven approaches using deep learning techniques provide improved precipitation nowcasting performance. Moreover, it has been known that multi-modal information from various sources could improve deep learning performance. This study introduces the RAIN-F+ dataset, which is the fusion dataset for rainfall prediction, and proposes the benchmark models for precipitation prediction using the RAIN-F+ dataset. The RAIN-F+ dataset is an integrated weather observation dataset including radar, surface station, and satellite observations covering the land area over the Korean Peninsula. The benchmark model is developed based on the U-Net architecture with residual upsampling and downsampling blocks. We examine the results depending on the number of the integrated dataset for training. Overall, the results show that the fusion dataset outperforms the radar-only dataset over time. Moreover, the results with the radar-only dataset show the limitations in predicting heavy rainfall over 10 mm/h. This suggests that the various information from multi-modality is crucial for precipitation nowcasting when applying the deep learning method.
Highlights
Weather observation provides the state of the atmosphere with various types of information from in situ and remote measurements
This study proposed the fusion dataset and the benchmark model for rainfall prediction based on a deep learning approach
We aim to address the following goals in our study: (1) to introduce the integrated realworld weather observation dataset named RAIN-F+ for rainfall prediction; (2) to propose the rainfall prediction algorithm based on the U-Net with residual blocks; (3) and to evaluate the prediction performance according to the number of modalities using RAIN-F+ dataset
Summary
Weather observation provides the state of the atmosphere with various types of information from in situ and remote measurements. Surface observations are from the in situ sensors that provide direct atmospheric state observations such as temperature, humidity, or pressure, while many remote sensing data from radar and satellites provide radiance and reflectivity measurements over distance. Weather forecasting using deep learning approaches is an interesting research topic in the weather and climate community and the computer vision community since weather data are considered a typical spatial-temporal dataset related to many applications in image prediction. There have been many studies related to weather forecasting using deep learning approaches, and the famous Conv-LSTM architecture [1] is developed to predict future precipitation using radar observations in the Hong Kong area and is applied to various image prediction applications
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