Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting

• URL: http://arxiv.org/abs/2412.15917v1
• Date: Fri, 20 Dec 2024 14:09:36 GMT
• Title: Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting
• Authors: Haotian Li, Arno Siebes, Siamak Mehrkanoon,
• Abstract summary: Short-term prediction of weather is essential for making timely and weather-dependent decisions. In this work, we leverage the benefits of self-supervised learning and integrate it with spatial-temporal learning to develop a novel model, SpaT-SparK.
• Score: 5.365086662531667
• License:
• Abstract: Nowcasting, the short-term prediction of weather, is essential for making timely and weather-dependent decisions. Specifically, precipitation nowcasting aims to predict precipitation at a local level within a 6-hour time frame. This task can be framed as a spatial-temporal sequence forecasting problem, where deep learning methods have been particularly effective. However, despite advancements in self-supervised learning, most successful methods for nowcasting remain fully supervised. Self-supervised learning is advantageous for pretraining models to learn representations without requiring extensive labeled data. In this work, we leverage the benefits of self-supervised learning and integrate it with spatial-temporal learning to develop a novel model, SpaT-SparK. SpaT-SparK comprises a CNN-based encoder-decoder structure pretrained with a masked image modeling (MIM) task and a translation network that captures temporal relationships among past and future precipitation maps in downstream tasks. We conducted experiments on the NL-50 dataset to evaluate the performance of SpaT-SparK. The results demonstrate that SpaT-SparK outperforms existing baseline supervised models, such as SmaAt-UNet, providing more accurate nowcasting predictions.

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