Artificial Intelligence Forecasting System (AIFS)
Unlocking Enhanced Weather Prediction with AIFS-CRPS
The new AIFS-CRPS model from ECMWF significantly improves medium-range and subseasonal ensemble weather forecasts by using a novel probabilistic training approach. This leads to more accurate and reliable predictions, particularly for critical atmospheric variables and extreme events.
Quantifiable Impact on Forecast Accuracy
AIFS-CRPS demonstrates superior performance across key meteorological metrics, enhancing predictability and reducing uncertainty.
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Improvement in CRPS for 2m Temperature
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Increased Skill for 850hPa Temperature
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Extended MJO Predictability Horizon
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Lower RMSE in Tropical 200hPa Temperature
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Probabilistic Training with afCRPS
AIFS-CRPS uses an almost fair Continuous Ranked Probability Score (afCRPS) as its loss function, enabling the model to generate stochastic forecasts with realistic atmospheric variability. This approach avoids the degeneracy issues of the fair CRPS while addressing finite ensemble size bias.
Superior Model Performance
For medium-range forecasts, AIFS-CRPS outperforms the physics-based IFS ensemble across most variables. It maintains small-scale detail and realistic variability throughout forecast ranges, a significant improvement over MSE-trained models.
Enhanced Subseasonal Forecasts
AIFS-CRPS demonstrates high skill in subseasonal forecasts (up to 46 days), matching or exceeding the IFS ensemble, particularly for Madden-Julian Oscillation (MJO) predictions, even though it's trained on shorter ranges.
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Forecast improvements for upper-air variables (e.g., 500 hPa geopotential, 250 hPa wind speed) in AIFS-CRPS over IFS.
Enterprise Process Flow
Initial Conditions
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Noise Injection
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Encoder Processing
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Stochastic Forecast Generation
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Ensemble Member Output
| Feature | AIFS-CRPS (Probabilistic) | MSE-trained AIFS (Deterministic) |
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| Uncertainty Representation |
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| Computational Efficiency (Inference) |
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Madden-Julian Oscillation (MJO) Forecast Breakthrough
AIFS-CRPS shows consistently higher MJO skill than IFS reforecasts, as evidenced by better correlations and lower RMSE of the ensemble mean (Fig. 8). It accurately represents the magnitude and eastward propagation of zonal wind anomalies across the Maritime Continent, a crucial aspect for subseasonal predictability. This is a significant improvement given that MJO forecasts from IFS generally compare very favorably to other systems.
Calculate Your Potential AI Forecast ROI
Estimate the financial and operational benefits of integrating AIFS-CRPS into your decision-making processes.
Your AIFS-CRPS Implementation Roadmap
A phased approach to integrate advanced AI weather forecasting into your enterprise operations.
Phase 1: Initial Assessment & Data Integration
Evaluate current forecasting needs, assess data infrastructure, and begin integrating AIFS-CRPS data streams. Focus on aligning with existing workflows.
Phase 2: Model Customization & Localized Training
Tailor AIFS-CRPS parameters for specific regional requirements and critical variables. Conduct localized training with historical data to optimize performance.
Phase 3: Pilot Deployment & Validation
Implement AIFS-CRPS in a controlled pilot environment. Validate forecast accuracy and reliability against internal benchmarks and operational requirements.
Phase 4: Full-Scale Operational Integration
Deploy AIFS-CRPS across all relevant operational units. Establish continuous monitoring, feedback loops, and ongoing model refinement for sustained performance.
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