The ACCORD consortium and its scientific strategy

ACCORD is a consortium made up of 26 Meteorological Services (https://www.accord-nwp.org/ ). The primary objective is to provide the consortium's member services with state-of-the-art numerical weather prediction (NWP) limited-area model codes. A substantial part of the ACCORD codes are shared with IFS-ARPEGE.

During phase 1 of ACCORD (2021-2025), collaborative working methods have been drastically modernized. Code management has greatly benefited from the implementation of the ACCORD software forge (Github) as well as from the expanded use of a testing tool which enables component-wise testing of new code versions. The adaptation of the codes to new HPC architectures (CPU-GPU accelerators) has largely progressed in close collaboration with MF (ARPEGE) and ECMWF (IFS).

Research on the current (semi-implicit semi-Lagrangian spectral) dynamical kernel of ACCORD models continues, notably through an extensive reformulation of the semi-implicit operator. In addition, the alternative FVM (Finite Volume Model) code, initially developed at ECMWF, is being studied. SURFEX (https://www.umr-cnrm.fr/surfex/ ) has become the main code infrastructure for modeling surface processes and the surface-atmosphere interface. Efforts devoted to developing new options for very high-resolution modeling are increasing: dynamical kernel, 3D aspects of turbulence and radiation, refined surface characteristics, all for models at the hectometer scale. This trend is largely driven by user needs.

In data assimilation, a major advance is the near-operational status of flow-dependent algorithms (EnVar-type algorithms coded in the OOPS software framework). ACCORD has maintained first-rate expertise in preprocessing observations for assimilation. This applies both to satellite data (infrared or microwave, in polar orbit or geostationary orbit) and to ground-based networks of various types (radar, surface networks, citizen observations) or aircraft (Mode-S). In probabilistic forecasting and ensemble methods (EPS), scientific collaboration focuses (among other aspects) on ensemble perturbation methods. Several approaches for model perturbations have been studied, such as tendency perturbations (SPPT), model parameter perturbations (SPP or RP), and surface field perturbations.

A new scientific strategy was approved by the Assembly of Directors in December 2024 for the next Programme phase (2026-2030). The main objectives include:

• Continue modernizing the collaborative working methods.

• Continue adapting the codes for CPU-GPU architectures.

• Continue research on the various model components with an increased focus on very high resolution and high-impact weather forecasting.

• Develop a research infrastructure enabling process-oriented meteorological evaluation of models using specialized observations.

• Continue to develop DA algorithms with flow dependence, leveraging observations from the coming years (MTG etc.).

• Pursue and strengthen scientific collaboration on ensemble forecasting systems.

In connection with the rapid evolution of data driven forecast tools, ACCORD members want to be proactive in AI initiatives in Europe (within EUMETNET and with ECMWF). The scientific strategy foresees exploring hybrid "AI-physical NWP" solutions.

In the presentation, a few keynote features of the ACCORD scientific strategy will be further addressed.