Four knowledgeable reviewers all appreciated the contributions of this paper and rated it as above the bar for publication at NeurIPS. Reviewers acknowledged that the primary contribution was the curation of a dataset and benchmark tasks on the data set, and not novel methods, but felt that the curation of a large, high-quality data set for real tasks in atmospheric/earth sciences is important and could spur AI work in this area. The authors deserve credit for this. Additionally, the reviewers appreciated that the baseline methods developed for the benchmark tasks were themselves thoughtful and significant, if not highly novel from a methods perspective. The reviewers asked a number of questions about justification and details of the data set construction, evaluation metrics, and baselines. The authors answered several of these in the rebuttal, including adding a new baseline method (optical flow); they are encouraged to use these comments to improve the final version of the paper. The meta-reviewer recommends accept.