the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Abstract. Detection of atmospheric features in gridded datasets from numerical simulation models is typically done by means of rule-based algorithms. Recently, also the feasibility of learning feature detection tasks using supervised learning with convolutional neural networks (CNNs) has been demonstrated. This approach corresponds to semantic segmentation tasks widely investigated in computer vision. However, while in recent studies the performance of CNNs was shown to be comparable to human experts, CNNs are largely treated as a “black box”, and it remains unclear whether they learn the features for the correct reasons. Here we build on the recently published “ClimateNet” dataset that contains features of tropical cyclones and atmospheric rivers as detected by human experts. We adapt the explainable artificial intelligence technique “Layer-wise Relevance Propagation” (LRP) to the feature detection task and investigate which input information CNNs with the Context-Guided Network (CG-Net) and U-Net architectures use for feature detection. We find that both CNNs indeed consider plausible patterns in the input fields of atmospheric variables, which helps to build trust in the approach. We also demonstrate application of the approach for finding the most relevant input variables and evaluating detection robustness when changing the input domain. However, LRP in its current form cannot explain shape information used by the CNNs, and care needs to be taken regarding the normalization of input values, as LRP cannot explain the contribution of bias neurons, accounting for inputs close to zero. These shortcomings need to be addressed by future work to obtain a more complete explanation of CNNs for geoscientific feature detection.
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CEC1: 'Comment on gmd-2024-60', Juan Antonio Añel, 14 Jun 2024
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Dear authors,
After checking your manuscript, we have detected a problem in the compliance of your submission with the Code and Data policy of the journal.
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
Your work heavily relies on the ClimateNet dataset; however, this dataset is made available through a link to a webpage that does not comply with the minimum requirements to be considered a trustable long-term repository. In this way, we have to request you that you store and make available the ClimateNet data that you use for your work in one of the acceptable repositories according to our policy, with a DOI. Therefore, please, publish it, and reply to this comment with the relevant information (link and DOI) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.
Also, you must include in a potentially reviewed version of your manuscript the modified 'Code and Data Availability' section, including this new information.
Regards,
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/gmd-2024-60-CEC1 -
AC1: 'Reply on CEC1', Tim Radke, 16 Jul 2024
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Dear Juan A. Añel,
Thank you for your comment. We have been trying to contact the authors of the original ClimateNet GMD paper, as we think they should have the opportunity to publish their own data under a DOI themselves. Unfortunately, we have not been able to reach the authors. If they do not reply until the end of this discussion period we will make the data available under a DOI ourselves.
Best Regards,
Tim Radke, on behalf of the authorsCitation: https://doi.org/10.5194/gmd-2024-60-AC1
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AC1: 'Reply on CEC1', Tim Radke, 16 Jul 2024
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