# Efficient Baseline for Quantitative Precipitation Forecasting in Weather4cast 2023

arXiv: https://arxiv.org/abs/2311.18806 · HTML: https://mlsanity.com/paper/2311.18806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021)
- [SimVP: Simpler yet Better Video Prediction](https://mlsanity.com/paper/2206.05099/) (2022)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts](https://mlsanity.com/paper/2212.04005/) (2022)
- [Efficient spatio-temporal weather forecasting using U-Net](https://mlsanity.com/paper/2112.06543/) (2021)

## Abstract (arXiv)

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.