Upgrade to IFS Cycle 50r1
Polichtchouk, Inna ; Massart, Sébastien ; Kipling, Zak
An upgrade to ECMWF's Integrated Forecasting System (IFS) is scheduled for operational implementation in early 2026. IFS Cycle 50r1 introduces a new ocean and sea ice configuration based on the NEMO4-SI³ model (a state-of-the-art modelling framework used for research activities and forecasting services in ocean and climate sciences - Nucleus for European Modelling of the Ocean coupled with the Sea Ice Modelling Integrated Initiative is developed by a European consortium), alongside a new ocean data assimilation system. The level of ocean-atmosphere coupling in the four-dimensional variational (4D-Var) data assimilation system is enhanced by using outer-loop coupling, a method that increases the consistency of the ocean and atmospheric initial conditions. An additional 12-hour window ocean analysis is now running in parallel to the atmospheric analysis. Together, these changes improve the representation of ocean-sea ice-atmosphere interactions. The cycle also revises the treatment of convection with the aim of improving aspects such as inland propagation of convective precipitation. In the stratosphere, reduced vertical diffusion (small-scale mixing of air between different heights) improves representation of the quasi-biennial oscillation (QBO) of tropical zonal winds between easterly and westerly and of the humidity. The data assimilation will also be able to extract stratospheric humidity information from observations and make use of more 2-metre temperature observations. Cycle upgrades to the ensemble system include scale-selective re-centring in the Ensemble of Data Assimilations (EDA) and a revised Stochastically Perturbed Parametrizations (SPP) scheme, which represents model uncertainty, to reduce excessive 10-metre wind spread. Additional improvements include a new glacier scheme and refined wave-sea ice coupling. These developments result in more realistic coupled forecasts, better use of observations, through both assimilating more data and extracting more information from them, and reduced computational cost.</p>
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