Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data

• URL: http://arxiv.org/abs/2210.12853v1
• Date: Thu, 20 Oct 2022 14:59:58 GMT
• Title: Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data
• Authors: Jihoon Ko, Kyuhan Lee, Hyunjin Hwang and Kijung Shin
• Abstract summary: We propose ASOC, a novel attentive method for effectively exploiting ground-based meteorological observations from multiple weather stations. ASOC is designed to capture temporal dynamics of the observations and also contextual relationships between them. We show that such a combination improves the average critical success index (CSI) of predicting heavy (at least 10 mm/hr) and light (at least 1 mm/hr) rainfall events at 1-6 hr lead times by 5.7%.
• Score: 14.672132394870445
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i.e., predicting precipitation levels and locations in the near future). Most existing deep-learning-based approaches for precipitation nowcasting, however, consider only radar and/or satellite images as inputs, and meteorological observations collected from ground weather stations, which are sparsely located, are relatively unexplored. In this paper, we propose ASOC, a novel attentive method for effectively exploiting ground-based meteorological observations from multiple weather stations. ASOC is designed to capture temporal dynamics of the observations and also contextual relationships between them. ASOC is easily combined with existing image-based precipitation nowcasting models without changing their architectures. We show that such a combination improves the average critical success index (CSI) of predicting heavy (at least 10 mm/hr) and light (at least 1 mm/hr) rainfall events at 1-6 hr lead times by 5.7%, compared to the original image-based model, using the radar images and ground-based observations around South Korea collected from 2014 to 2020.

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