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TRUVACE RECORD VERSION
record: TRV-2026-2622
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T08:05:11.325324Z
status: published
lens: g_space
sector: climate
headline: AIFS Single 1.1.0: an update to ECMWF's machine-learned weather forecast model AIFS
dek: We present version 1.1.0 of ECMWF's Artificial Intelligence Forecasting System (AIFS Single), operational since 25 February 2025. The revised system introduces a bounding-layer framework that enforces physical constraints, such as non-negativity and internal consistency within precipitation and cloud cover variables, alongside expanded training data, revised loss weighting, and an extended set of surface and atmospheric variables. Overall skill improves by 4 %–6 % in the upper air and near-surface variables with…
gain_title: In a model evaluation, ECMWF's updated AI forecast system AIFS Single 1.1.0 improved upper-air and near-surface skill by 4%–6% and precipitation skill by up to 12%, gaining about 1 day on a categorical measure.
problem_title: (none)
trace_subject: (none)
gain_reading: In a model evaluation, ECMWF's updated AI forecast system AIFS Single 1.1.0 improved upper-air and near-surface skill by 4%–6% and precipitation skill by up to 12%, gaining about 1 day on a categorical measure.
gain_evidence: Overall skill improves by 4 %–6 % in the upper air and near-surface variables | up to 12 % and an approximately 1 d advantage using a categorical measure of skill
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers at ECMWF describe version 1.1.0 of AIFS Single, its machine-learned weather forecast model that has been operational since 25 February 2025. The update raises overall skill by 4%–6% in the upper air and near-surface variables, with no loss of spatial variability.

The biggest jump is in precipitation, where forecasts improved by up to 12% and gained roughly one day of lead time on a categorical skill measure. The authors trace this to a bounding-layer framework that enforces physical limits such as non-negative precipitation.

A controlled comparison found that adding more training data was the main source of upper-air gains, which the authors say shows why frequent model updates matter.
limitation: 
tag: Evidence-backed gain
key_points: AIFS Single has been operational at ECMWF since 25 February 2025; this paper describes version 1.1.0. | The update adds a bounding-layer framework that enforces physical constraints, including non-negativity and internal consistency within precipitation and cloud cover variables. | Other changes include expanded training data, revised loss weighting and an extended set of surface and atmospheric variables. | Skill improves 4%–6% in upper-air and near-surface variables without degrading spatial variability. | Precipitation improves by up to 12%, with an approximately 1 day advantage on a categorical skill measure. | A controlled comparison identifies training data expansion as the dominant source of upper-air skill gains. | Enforcing precipitation non-negativity resolves a gradient ambiguity at the zero-precipitation boundary under MSE training, which explains the reduced drizzle bias.
rundown: The paper documents an upgrade to a forecasting system already in operational use. Version 1.1.0 changes both the training setup and the model's output handling, adding a bounding-layer framework that keeps predicted fields physically plausible.

The authors separated the effects of the changes with a controlled comparison. Expanded training data drove most of the upper-air gains, while the bounding framework produced the largest precipitation improvements, up to 12%.

They also offer a technical explanation for the better rain forecasts. Under MSE training, the zero-precipitation boundary creates a gradient ambiguity; enforcing non-negativity resolves it, reducing drizzle bias.
sources:
- peer_reviewed | Geoscientific Model Development | https://doi.org/10.5194/gmd-19-4703-2026 | 2026-06-01
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