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amt-2022-33-AC1-supplement.pdf
Preview of https://amt.copernic us.org/preprints/amt -2022-33/amt-2022-33 -AC1-supplement.pdf
🔗 Source: amt.copernicus.org
📊 Size: 143 KB
👤 Author: Aichinger-Rosenberger Matthias
⬇️ Downloads: 192

Summary

The authors respond to referee comments on their manuscript, addressing issues with data handling, reproducibility, choice of machine learning algorithms, performance optimization, and lack of physical understanding. They acknowledge a bug in their code that led to incorrect data handling, which they have since fixed, and will provide detailed statistics on available data. They will also make their code and data available online and provide more details on their algorithms. The authors justify their choice of algorithms, but will exclude decision trees and provide more information on their settings. They clarify that their goal is to demonstrate the possibility of using machine learning for foehn diagnosis, rather than finding the best-performing algorithm, and will adjust their formulations to reflect this. Finally, they agree to explore the physical understanding of their model, including the relationship between integral water vapor fields and foehn events.

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The authors respond to referee comments on their manuscript, addressing issues with data handling, reproducibility, choice of machine learning algorithms,...

Technical Information

  • File Format: PDF
  • File Size: 143 KB
  • Pages: 5
  • Language: EN
  • Author: Aichinger-Rosenberger Matthias
  • Total Downloads: 192
  • Last Updated: 4 weeks ago

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