Value of including AI weather models within a multi-model probabilistic blend

Nowadays weather forecasts are available from a wide range of sources. Combining these weather forecasts into a form that is digestible by operational meteorologists and other users is key for decision-making. IMPROVER takes the approach of converting the individual forecast sources into exceedance probabilities and combining these exceedance probabilities to create a multi-model blended probabilistic forecast.

At the Met Office, the operational IMPROVER implementation blends a deterministic nowcast, a deterministic UK domain model (UKV), an ensemble UK domain model (MOGREPS-UK), an ensemble global domain model (MOGREPS-G) with ensemble forecasts from ECMWF’s IFS to create a seamless probabilistic forecast out to 14 days. However, recent advances in AI weather models present new opportunities to further enhance performance. This study evaluates the added value of incorporating ECMWF’s AIFS‑CRPS ensemble AI model into the IMPROVER blending framework. We assess the impact on forecast skill across spatial scales, lead times, and exceedance thresholds, and analyse patterns of improvement to understand the mechanisms underpinning any added benefit from AI weather forecasts.

We also investigate how multi-model blending interacts with forecast calibration. Calibration methods can improve both mean bias and ensemble spread, however the net effect of these calibration approaches when combined with multi-model blending needs assessment. By comparing calibration applied before and after blending, we explore how best to maximise future forecast quality.

This work provides an early assessment of how AI-based ensemble models can be integrated into an operational probabilistic forecasting system, informing the Met Office’s strategy for augmenting traditional NWP with emerging AI weather prediction capabilities.