  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.
