This work presents a novel 3D CNN architecture for climate event detection that combines an unsupervised auto-encoder reconstruction loss with YOLO like bounding box prediction. The approach is trained and evaluated on a large-scale, simulated climate dataset labeled by a costly heuristic approach called TECA.   For the most part, the paper is nicely written (minor comments below) and addresses an important and well motivated problem. The authors provide sufficient model details to allow reproduction (although public code would be preferred). I find the experiments a bit unconvincing (see below) but appreciate the attention to model capacity (via number of parameter) when comparing the 2D and 3D model variants.  I am concerned that the evaluation may be insufficient to assess the effectiveness of this method. An IoU threshold of 0.1 allows for many rather poor detections to count as true positives. If the goal of this work is to count the number of such instances this is defensible, but for localization this seems overly loose.Furthermore, the 3D CNN architecture (which is one of the the core novelties of this work compared to past efforts) does not seem capable of producing variable sized boxes (as noted in the last paragraph of page 7), which I imagine results in poorer performance at higher IoU thresholds (as many of the weather events appear small).  The experiments also feel inconclusive about the effect of temporal modeling and semi-supervision. The temporal component does not seem to matter in the supervised settings (2D 51.45 mAP - 3D 51.00 mAP) but improves somewhat in the semi-supervised case (2D 51.11 mAP - 3D 52.92 mAP). Whereas the additional unlabeled data seems to hurt in the 2D case but improve results for the 3D model. Could the authors provide confidence intervals for these numbers? I would like to see further discussion of these trends especially with respect to the effect of the loss weights (alpha, beta, and gamma).   I also note that it is not clear if both the 2D and 3D models were trained for equivalent time periods (seems like no from last paragraph of page 7). Could a plot of training and validation accuracy for each model be presented for comparison?   Finally, is there any baseline approach the authors could report or compare too? Without one, it is difficult to evaluate the performance of the approach with respect to the difficulty of the problem.  Preliminary Rating: I think this is an interesting paper that is well motivated but feel the experiments as presented do not seem adequate to support any conclusive trends. I would like to see the mAP trends across a wider range of IoU values and further discussion of training procedure, loss weight settings, and reasons for lack of bounding box variability in the 3D model (as stated above).   Clarification: In the paper you say "While climate models are run on a 3D grid, with the vertical dimension corresponding to 30 levels; we only consider surface quantities (i.e. 2D data) in this study." Could you elaborate on what the surface quantities correspond to? Is it the highest cloud level?  Minor notes: 	Please provide years for Prabhat et al. references rather than a and b. 	Footnote in 4.2 could be inline text with similar space. 	4.3 second paragraph the word table is not capitalized like elsewhere. 	4.3 4th paragraph the word section is not capitalized like elsewhere.  Edit: I appreciate the authors responding to my questions but still feel the relatively poor localization performance at stricter IoU thresholds fails to justify the complexity of the approach. I encourage the authors to continue pursuing this line of research.
Output: en