Improving AIFS Forecast Skill through Fine-Tuning across Spatial Resolutions and Datasets

Over the past two years, ECMWF has rapidly developed and operationalised two machine-learned forecasting systems: AIFS Single, a deterministic model, and AIFS-ENS, a fully probabilistic forecasting system. Both systems are trained on ERA5 reanalysis data and further fine-tuned using operational IFS analyses. In this talk, we briefly introduce the AIFS framework and present ongoing research aimed at further improving its forecast skill.

Current efforts are driven by several research directions, including increasing spatial resolution and incorporating observational data. The current AIFS models operate at the native ERA5 resolution of approximately 30km. While higher resolutions could significantly improve forecast skill in surface variables, available datasets, such as the operational IFS analysis at 9km, are only available for a limited number of years. To address this, we explore a cross-resolution fine-tuning strategy in which AIFS is first pretrained on ERA5 at coarse resolution and subsequently fine-tuned on six years of recent operational IFS analyses at 9 km. We present promising early results showing that this approach enables stable fine-tuning down to 9 km and leads to significant gains in surface forecast skill.

A second research direction investigates the use of alternative datasets to improve total precipitation forecasts. ERA5 is known to exhibit deficiencies in the representation of precipitation, particularly in the tropics. We therefore fine-tune AIFS using the Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, which has been shown to better capture precipitation characteristics in this region. Early results indicate that incorporating IMERG data can significantly improve total precipitation forecast skill in AIFS, with the largest benefits observed, as expected, in tropical regions.