Target Concept Tuning Improves Extreme Weather Forecasting
arXiv:2603.19325v1 Announce Type: new Abstract: Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typically face a trade-off between overlooking these extreme events and overfitting them at the expense of overall performance. We propose TaCT, an interpretable concept-gated fine-tuning framework that solves the aforementioned issue by selective model improvement: models are adapted specifically for failure cases while preserving performance in common scenarios. To this end, TaCT automatically discovers failure-related internal concepts using Sparse Autoencoders and counterfactual analysis, and updates parameters only when the corresponding concepts are activated, rather than applying uniform adaptation. Experiments show consistent improvements in typhoon forecasting across different regions without degrading other meteorological variables. The identified concepts co
arXiv:2603.19325v1 Announce Type: new Abstract: Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typically face a trade-off between overlooking these extreme events and overfitting them at the expense of overall performance. We propose TaCT, an interpretable concept-gated fine-tuning framework that solves the aforementioned issue by selective model improvement: models are adapted specifically for failure cases while preserving performance in common scenarios. To this end, TaCT automatically discovers failure-related internal concepts using Sparse Autoencoders and counterfactual analysis, and updates parameters only when the corresponding concepts are activated, rather than applying uniform adaptation. Experiments show consistent improvements in typhoon forecasting across different regions without degrading other meteorological variables. The identified concepts correspond to physically meaningful circulation patterns, revealing model biases and supporting trustworthy adaptation in scientific forecasting tasks. The code is available at https://anonymous.4open.science/r/Concept-Gated-Fine-tune-62AC.
Executive Summary
This article introduces TaCT, a novel fine-tuning framework for deep learning models in meteorological forecasting. TaCT addresses the trade-off between overlooking extreme events and overfitting them by selectively improving models for failure cases while preserving overall performance. The framework uses Sparse Autoencoders and counterfactual analysis to discover failure-related internal concepts and updates parameters only when the corresponding concepts are activated. Experiments demonstrate consistent improvements in typhoon forecasting without degrading other meteorological variables. The identified concepts correspond to physically meaningful circulation patterns, revealing model biases and supporting trustworthy adaptation. This work has significant implications for scientific forecasting tasks and suggests a promising approach for improving extreme weather forecasting models.
Key Points
- ▸ TaCT is a novel fine-tuning framework for deep learning models in meteorological forecasting
- ▸ TaCT selectively improves models for failure cases while preserving overall performance
- ▸ TaCT uses Sparse Autoencoders and counterfactual analysis to discover failure-related internal concepts
Merits
Improved Forecasting Accuracy
TaCT demonstrates consistent improvements in typhoon forecasting without degrading other meteorological variables.
Physically Meaningful Insights
The identified concepts correspond to physically meaningful circulation patterns, revealing model biases and supporting trustworthy adaptation.
Interpretable Model Updates
TaCT provides interpretable model updates by selectively updating parameters based on activated concepts.
Demerits
Limited Dataset
The article does not discuss the potential limitations of the dataset used for training and testing the TaCT framework.
Scalability
The article does not address the scalability of the TaCT framework to larger datasets or more complex forecasting tasks.
Transferability
The article does not discuss the transferability of the TaCT framework to other domains or tasks beyond meteorological forecasting.
Expert Commentary
The article presents a novel approach to fine-tuning deep learning models for meteorological forecasting. The use of Sparse Autoencoders and counterfactual analysis to discover failure-related internal concepts is a clever innovation. However, the article could benefit from a more in-depth discussion of the limitations and potential biases of the TaCT framework. Additionally, the scalability and transferability of the framework to larger datasets and more complex forecasting tasks should be addressed. Overall, the work has significant implications for scientific forecasting tasks and suggests a promising approach for improving extreme weather forecasting models.
Recommendations
- ✓ Future research should focus on addressing the scalability and transferability of the TaCT framework.
- ✓ The authors should provide a more in-depth discussion of the limitations and potential biases of the TaCT framework.
Sources
Original: arXiv - cs.LG
