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<p>Ensemble weather forecasts provide a probabilistic description of the future state of the atmosphere and give users flow-dependent estimates of forecast uncertainty. Here, we introduce AIFS-CRPS, an ensemble variant of the machine-learned Artificial Intelligence Forecasting System (AIFS) developed at ECMWF. Its loss function is the almost fair Continuous Ranked Probability Score (afCRPS). It is based on a proper score, the CRPS, but approximately removes the bias in the score due to finite ensemble size yet avoids a degeneracy of the fair CRPS. The trained model is stochastic and can generate as many exchangeable members as desired. For medium-range forecasts AIFS-CRPS outperforms the physics-based Integrated Forecasting System (IFS) ensemble for the majority of variables and lead times. For subseasonal forecasts, AIFS-CRPS outperforms the IFS ensemble before calibration and is competitive with the IFS ensemble when forecasts are evaluated as anomalies to remove the influence of model biases.</p>

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AIFS-CRPS: ensemble forecasting using a model trained with a loss function based on the continuous ranked probability score

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Simon Lang,
  • Mihai Alexe,
  • Mariana C. A. Clare,
  • Christopher Roberts,
  • Rilwan Adewoyin,
  • Zied Ben Bouallègue,
  • Matthew Chantry,
  • Jesper Dramsch,
  • Peter D. Dueben,
  • Sara Hahner,
  • Pedro Maciel,
  • Ana Prieto-Nemesio,
  • Cathal O’Brien,
  • Florian Pinault,
  • Jan Polster,
  • Baudouin Raoult,
  • Steffen Tietsche,
  • Martin Leutbecher

摘要

Ensemble weather forecasts provide a probabilistic description of the future state of the atmosphere and give users flow-dependent estimates of forecast uncertainty. Here, we introduce AIFS-CRPS, an ensemble variant of the machine-learned Artificial Intelligence Forecasting System (AIFS) developed at ECMWF. Its loss function is the almost fair Continuous Ranked Probability Score (afCRPS). It is based on a proper score, the CRPS, but approximately removes the bias in the score due to finite ensemble size yet avoids a degeneracy of the fair CRPS. The trained model is stochastic and can generate as many exchangeable members as desired. For medium-range forecasts AIFS-CRPS outperforms the physics-based Integrated Forecasting System (IFS) ensemble for the majority of variables and lead times. For subseasonal forecasts, AIFS-CRPS outperforms the IFS ensemble before calibration and is competitive with the IFS ensemble when forecasts are evaluated as anomalies to remove the influence of model biases.