Chris Roberts

Senior scientist @ECMWF working on sub-seasonal (s2s) prediction.

[Chris Roberts @ClimatePrimate]

🚨 New preprint 🚨 - "Ensemble reliability and the signal-to-noise paradox in large-ensemble subseasonal forecasts"

https://t.co/jMg9jvkcou

[Chris Roberts @ClimatePrimate]

Final published version of “Unbiased calculation, evaluation, and calibration of ensemble forecast anomalies” now available online in the Quarterly Journal of the Royal Meteorological Society:

https://t.co/uw9xnyoHlN

[Chris Roberts @ClimatePrimate]

Do you calculate anomalies from ensemble forecasts?
If yes, then maybe you should be doing it differently!

🚨 New paper!🚨

"Unbiased evaluation and calibration of ensemble forecast anomalies"

https://t.co/kX7NyFOkLc

[Chris Roberts @ClimatePrimate]

More details on fair ensemble scores:

https://t.co/VJPgnZMX7O

https://t.co/o1g2T8CzdE

[Chris Roberts @ClimatePrimate]

And here is an example 10 day forecast of 850 hPa Meridional Wind from AIFS-CRPS provided by Simon Lang. This animation shows that CRPS-based training does not suffer from smoothing and/or reduced variance for longer rollouts that is inherent to deterministic MSE-based training.

[Chris Roberts @ClimatePrimate]

🚨New preprint🚨from @ECMWF introducing AIFS-CRPS, a new data-driven ensemble system for medium-range and subseasonal forecasting. https://t.co/ZGAPTKF8Dh

[Chris Roberts @ClimatePrimate]

This is because optimising a fair ensemble score means there is no trade-off between minimising error and maintaining realistic levels of variability, which is unavoidable for deterministic training. fCRPS loss is minimised when the fc is drawn from the same distribution as obs.

[Chris Roberts @ClimatePrimate]

Thread continued:

[Chris Roberts @ClimatePrimate]

AIFS-CRPS is an ensemble variant of the Artificial Intelligence Forecasting System (AIFS) developed at ECMWF. The training protocol utilises a probabilistic loss function based on the Continuous Ranked Probability Score (CRPS).

[Chris Roberts @ClimatePrimate]

🚨New preprint🚨from @ECMWF introducing AIFS-CRPS, a new data-driven ensemble system for medium-range and subseasonal forecasting. https://t.co/ZGAPTKF8Dh

[Chris Roberts @ClimatePrimate]

This paper presents a novel approach to obtain a weather forecast model for ensemble forecasting with machine-learning.

[Chris Roberts @ClimatePrimate]

@ECMWF … Peter Deuben , Sara Hahner, Pedro Maciel, Ana Prieto-Nemesio, Cathal O'Brien, Florian Pinault, Jan Polster, Baudouin Raoult, Steffen Tietsche, Martin Leutbecher!

[Chris Roberts @ClimatePrimate]

AIFS-CRPS is an ensemble variant of the Artificial Intelligence Forecasting System (AIFS) developed at ECMWF. The training protocol utilises a probabilistic loss function based on the Continuous Ranked Probability Score (CRPS).

[Chris Roberts @ClimatePrimate]

This work represents a huge team effort from @ECMWF colleagues Simon Lang, Mihai Alexe, Mariana Clare, Rilwan Adewoyin, Zied Ben Bouallègue, Matthew Chantry, Jesper Dramsch…

[Sol Osman @marisol_osman]

🚨 Don’t miss out! 🚨 Submit your abstract before the Christmas break and join us at #EGU25 to discuss recent progress in #S2S prediction
@Dr_Chris_White @Domeisen_D @ClimatePrimate

[Chris Roberts @ClimatePrimate]

And if you made it this far, come follow me on Bluesky! I am not sure how much longer I will be active on Twitter...

https://t.co/mP0b4E729F

[Chris Roberts @ClimatePrimate]

🚨 New preprint 🚨 - "Ensemble reliability and the signal-to-noise paradox in large-ensemble subseasonal forecasts"

https://t.co/jMg9jvkcou

[Chris Roberts @ClimatePrimate]

Crucially, there is no inconsistency between the objectives of eliminating an apparent signal-to-noise paradox and traditional approaches to ensemble forecast development guided by unbiased evaluation of forecast reliability and optimization of fair ensemble scores