| 15 Apr 2026
Global Sub-national Impact-based Forecasting for Tropical Cyclones Using Open Data: Combining Machine Learning and Exposure-based Approaches
Abstract. Tropical cyclones (TCs) cause substantial and uneven impacts across regions, driven by differences in exposure and vulnerability. While anticipatory action (AA) systems aim to mitigate these impacts, they are typically based on hazard thresholds rather than predicted consequences, limiting their effectiveness and consistency. Impact-based forecasting offers a promising alternative, but existing approaches are often region-specific or rely on non-transferable data. In this study, we develop a global, sub-national impact-based forecasting framework that predicts affected-population fractions using only openly available data. The model integrates hazard, exposure, and contextual features within a two-stage XGBoost architecture and is evaluated across 780 historical TC events using decision-relevant metrics aligned with operational thresholds. Our results show that machine learning improves the detection and spatial localization of impacts, but does not outperform simpler exposure-based approaches in identifying severe events. This reveals a fundamental trade-off between coverage and conservative severity detection, suggesting that hybrid strategies combining both approaches are better suited for operational use. We position this system as a first-generation global benchmark for impact-based forecasting: it demonstrates the feasibility of transferable, sub-national predictions using open data, while clarifying the limitations that must be addressed for reliable deployment in anticipatory action systems.
This manuscript presents a timely and well-executed study on global, sub-national impact-based forecasting for tropical cyclones using a two-stage XGBoost framework and openly available data. The work is particularly valuable in its integration of hazard, exposure, and contextual predictors at global scale, as well as its comparison against operationally relevant exposure-based baselines. The inclusion of decision-oriented evaluation (e.g., 0% vs. 15% thresholds), sensitivity analyses (temporal and geographic), and exploratory forecast-based experiments strengthens the contribution and demonstrates careful methodological consideration. Overall, the paper addresses an important problem and provides meaningful insights into the trade-offs between machine learning and simpler rule-based approaches.
At the same time, several aspects of the manuscript would benefit from clarification and further refinement. In particular, some methodological elements would be clearer with additional explanation, including the definition and use of key concepts such as the ‘impact fraction’, the selection of decision thresholds (0% and 15%), and the role of hyperparameters in the XGBoost models. Similarly, certain components—such as the description of the TIGGE forecasts, the operational interpretation of the two-stage framework, and the aggregation to ADM1 units—would benefit from more explicit documentation or justification, especially given the global and heterogeneous nature of the dataset.
Additionally, there are opportunities to strengthen the interpretation and presentation of results. For example, reporting class distributions more explicitly would improve understanding of model performance under strong class imbalance, and the conclusions and future work sections could be expanded to more clearly articulate the methodological contribution, novelty, and implications of the findings (e.g., the complementarity of machine learning and threshold-based approaches, and the role of rainfall-driven impacts).Â
I have also attached an annotated version of the manuscript with specific comments and suggestions provided directly in the text.
Overall, these are relatively minor revisions focused on clarity, transparency, and positioning, and I recommend the manuscript for publication pending minor revisions.