Learning Robust Precipitation Forecaster by Temporal Frame Interpolation
- URL: http://arxiv.org/abs/2311.18341v2
- Date: Fri, 1 Dec 2023 16:46:10 GMT
- Title: Learning Robust Precipitation Forecaster by Temporal Frame Interpolation
- Authors: Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan
- Abstract summary: We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies.
Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
- Score: 65.5045412005064
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in deep learning have significantly elevated weather
prediction models. However, these models often falter in real-world scenarios
due to their sensitivity to spatial-temporal shifts. This issue is particularly
acute in weather forecasting, where models are prone to overfit to local and
temporal variations, especially when tasked with fine-grained predictions. In
this paper, we address these challenges by developing a robust precipitation
forecasting model that demonstrates resilience against such spatial-temporal
discrepancies. We introduce Temporal Frame Interpolation (TFI), a novel
technique that enhances the training dataset by generating synthetic samples
through interpolating adjacent frames from satellite imagery and ground radar
data, thus improving the model's robustness against frame noise. Moreover, we
incorporate a unique Multi-Level Dice (ML-Dice) loss function, leveraging the
ordinal nature of rainfall intensities to improve the model's performance. Our
approach has led to significant improvements in forecasting precision,
culminating in our model securing \textit{1st place} in the transfer learning
leaderboard of the \textit{Weather4cast'23} competition. This achievement not
only underscores the effectiveness of our methodologies but also establishes a
new standard for deep learning applications in weather forecasting. Our code
and weights have been public on \url{https://github.com/Secilia-Cxy/UNetTFI}.
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