English

Efficient Baseline for Quantitative Precipitation Forecasting in Weather4cast 2023

Machine Learning 2023-12-01 v1 Atmospheric and Oceanic Physics

Abstract

Accurate precipitation forecasting is indispensable for informed decision-making across various industries. However, the computational demands of current models raise environmental concerns. We address the critical need for accurate precipitation forecasting while considering the environmental impact of computational resources and propose a minimalist U-Net architecture to be used as a baseline for future weather forecasting initiatives.

Keywords

Cite

@article{arxiv.2311.18806,
  title  = {Efficient Baseline for Quantitative Precipitation Forecasting in Weather4cast 2023},
  author = {Akshay Punjabi and Pablo Izquierdo Ayala},
  journal= {arXiv preprint arXiv:2311.18806},
  year   = {2023}
}

Comments

5 pages, 1 figure, Weather4Cast 2023 challenge

R2 v1 2026-06-28T13:37:24.929Z