Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting
Authors: Haitao Lin, Zhangyang Gao, Yongjie Xu, Lirong Wu, Ling Li, Stan Z. Li7470-7478
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our model is evaluated on realworld weather benchmark datasets, achieving state-of-the-art performance with obvious improvements. We conduct further analysis on local pattern visualization, model s framework choice, advantages of horizon maps and etc. |
| Researcher Affiliation | Academia | Haitao Lin,*1 3 Zhangyang Gao,*1 3 Yongjie Xu, 1 3 Lirong Wu, 1 3 Ling Li, 2 Stan Z. Li 1 1 Center of Arti๏ฌcial Intelligence for Research and Innovation, Westlake University 2 Eco-Environmental Research Laboratory, Westlake University 3 Zhejiang University linhaitao, gaozhangyang, xuyongjie, wulirong, liling, EMAIL |
| Pseudocode | No | The paper describes the method and architecture in text and figures (e.g., Figure 5) but does not contain a dedicated pseudocode or algorithm block. |
| Open Source Code | Yes | The source code is available at https://github.com/BIRD-TAO/CLCRN. |
| Open Datasets | Yes | The datasets used for performance evaluation are provided in Weather Bench (Rasp et al. 2020), with 2048 nodes on the earth sphere. |
| Dataset Splits | Yes | The hyper-parameters are chosen through a carefully tuning on the validation set (See Appendix D1 for more details). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with versions) were explicitly mentioned in the paper. |
| Experiment Setup | Yes | All the models are trained with target function of MAE and optimized by Adam optimizer for a maximum of 100 epoches. The hyper-parameters are chosen through a carefully tuning on the validation set (See Appendix D1 for more details). The learning rate is set to 0.001 with decayed by 0.7 for every 5 epoches. The batch size is set to 64. The hidden size is 32. |