Research map / Time series / Long-term forecasting
Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add GMAO-GEOS forecasts as features (ablation) | XGBoost · Western contiguous U.S. forecast period | mean spatial ACC: 0.09 (0.02) -> 0.13 (0.02) | Helped |
| Add NCEP-CFSv2 forecasts as features (ablation) | XGBoost · Western contiguous U.S. forecast period | mean spatial ACC: 0.15 (0.02) -> 0.18 (0.02) | Helped |
| Add GMAO-GEOS forecasts as features (ablation) | Multitask Lasso · Western contiguous U.S. forecast period | mean spatial ACC: 0.12 (0.03) -> 0.16 (0.03) | No clear effect |
| Add NCEP-CFSv2 forecasts as features (ablation) | Multitask Lasso · Western contiguous U.S. forecast period | mean spatial ACC: 0.19 (0.01) -> 0.23 (0.02) | Helped |
| Add GMAO-GEOS forecasts as features (ablation) | XGBoost · Western contiguous U.S. forecast period | median spatial ACC: 0.12 (0.03) -> 0.14 (0.04) | Helped |
| Add NCEP-CFSv2 forecasts as features (ablation) | XGBoost · Western contiguous U.S. forecast period | median spatial ACC: 0.21 (0.03) -> 0.23 (0.02) | Helped |
| Add GMAO-GEOS forecasts as features (ablation) | Multitask Lasso · Western contiguous U.S. forecast period | median spatial ACC: 0.16 (0.04) -> 0.18 (0.04) | No clear effect |
| Add NCEP-CFSv2 forecasts as features (ablation) | Multitask Lasso · Western contiguous U.S. forecast period | median spatial ACC: 0.21 (0.02) -> 0.25 (0.02) | Helped |
| Use XGBoost instead of SubX dynamical models | SSF forecasting · Western contiguous U.S., most forecast dates and locations | spatial relative R² | Mixed |
| Use XGBoost instead of SubX dynamical models | SSF forecasting · December 2018 to February 2019 cold wave | spatial ACC | Hurt |
| Use separate modeling for extreme weather events | ML-based SSF · Extreme weather events, including unusual cold waves | RMSE | Helped |
Closest papers by meaning
- Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances (AAAI 2021 · similarity 0.95)
- ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction (NeurIPS 2024 · similarity 0.86)
- OmniCast: A Masked Latent Diffusion Model for Weather Forecasting Across Time Scales (NeurIPS 2025 · similarity 0.86)
- DroughtSet: Understanding Drought Through Spatial-Temporal Learning (AAAI 2025 · similarity 0.86)
- SEMPO: Lightweight Foundation Models for Time Series Forecasting (NeurIPS 2025 · similarity 0.85)
- CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer (ICLR 2025 · similarity 0.85)
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