Physics-Assisted and Topology-Informed Deep Learning for Weather Prediction
Authors: Jiaqi Zheng, Qing Ling, Yerong Feng
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the 5.625degree resolution ERA5 data set, demonstrating the competitive performance of PASSAT compared to the state-of-the-art deep learning models and the NWP model IFS T42. We conduct ablation studies to evaluate the effectiveness of the physics and topology information used in PASSAT. |
| Researcher Affiliation | Academia | Jiaqi Zheng1 , Qing Ling1 and Yerong Feng2 1Sun Yat-Sen University 2Shenzhen Institute of Meteorological Innovation EMAIL, EMAIL, yerong EMAIL. Sun Yat-Sen University is an academic institution. Shenzhen Institute of Meteorological Innovation is a public research institution. All authors are from academic or public research institutions. |
| Pseudocode | Yes | Algorithm 1: PASSAT: Predicting any weather variable u for ̄ = t + 0.2, t + 0.4, , t + t at time t |
| Open Source Code | Yes | We release an open-source Pytorch implementation of PASSAT online1. 1https://github.com/Yumenomae/PASSAT5p625 |
| Open Datasets | Yes | We conduct the experiments on the European Centre for Medium-Range Weather Forecasts Reanalysis V5 (ERA5) 5.625 -resolution data set, spanning from 1979 to 2018 and provided by Weather Bench [Hersbach et al., 2020; Rasp et al., 2020]. |
| Dataset Splits | Yes | The data samples from 1979 to 2015 are used in the training set, 2016 in the validation set, as well as 2017 and 2018 in the test set. |
| Hardware Specification | No | The paper mentions "NVIDIA s Modulus" in relation to training baseline models but does not specify the hardware (e.g., GPU models, CPU types) used by the authors for their own experiments. |
| Software Dependencies | No | The paper mentions a "Pytorch implementation" and "NVIDIA s Modulus" but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In PASSAT, we set the integration step size as 0.2 hours. We use PASSAT and the baseline models to predict these weather variables, at a temporal resolution of 6 hours (6am, 12am, 6pm, and 12pm of each day) and lasting for 24 steps (144 hours). |