5 accepted papers
Precipitation nowcasting involves using current radar observation sequences to predict future radar sequences and determine future precipitation distribution, which is crucial for disaster warning, traffic planning, and agricultural production. Despite numerous advancements, challenges persist in ac…
Long-sequence precipitation forecasting is critical for both meteorological science and smart city applications. The primary objective of this task is to predict future radar echo sequences, which provide high resolution and timely references for atmospheric precipitation distribution based on curre…
Most current precipitation nowcasting methods aim to capture the underlying spatiotemporal dynamics of precipitation systems by minimizing the mean square error (MSE). However, these methods often neglect effective constraints on the data distribution, leading to unsatisfactory prediction accuracy a…
Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar echoes sequences based on current observations which can serve both meteorological science and smart city applications. Due to the chaotic evolution nature of the precipitation systems it is a very challeng…
Equipping a deep model the ability of few-shot learning (FSL) is a core challenge for artificial intelligence. Gradient-based meta-learning effectively addresses the challenge by learning how to learn novel tasks. Its key idea is learning a deep model in a bi-level optimization manner, where the out…