EMS Annual Meeting Abstracts
Vol. 21, EMS2024-573, 2024, updated on 05 Jul 2024
https://doi.org/10.5194/ems2024-573
EMS Annual Meeting 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Coupled land-atmosphere data assimilation developments in support of the next generation of Earth system reanalyses and seasonal prediction systems: The CopERnIcus Climate Change Service Evolution (CERISE) project

Patricia de Rosnay1, Filipe Aires2, Jelena Bojarova3, Anca Brookshaw1, Jean-Christophe Calvet4, Carla Cardinali5, Christoph Herbert1, Hans Hersbach1, Jeff Knight6, Núria Pérez-Zanón7, Harald Schyberg8, Tim Stockdale1, Isabel Trigo9, Frédéric Vitart1, and Peter Weston1
Patricia de Rosnay et al.
  • 1ECMWF, Reading, UK
  • 2Estellus, Paris, France
  • 3SMHI, Norrköping, Sweden
  • 4Météo-France, Toulouse, France
  • 5CMCC, Italy
  • 6UK Met Office, Exeter, UK
  • 7BSC, Barcelona, Spain
  • 8Met Norway, Oslo, Norway
  • 9IPMA, Lisbon, Portugal

The aim of CERISE is to develop new and innovative coupled land-atmosphere data assimilation approaches and land initialisation techniques to pave the way for the next generations of the Copernicus Climate Change Service (C3S) reanalysis and seasonal prediction systems. These developments are combined with innovative work on machine-learned observation operator to ensure optimal data fusion fully integrated in coupled assimilation systems. The project aims at improving the quality and consistency of the C3S reanalysis systems and of the components of the seasonal prediction multi-system, directly addressing the evolving user needs for improved and more consistent C3S Earth system products.

This presentation gives an overview of the objectives of the CERISE project with a focus on developments of coupled land-atmosphere data assimilation to improve the climate consistency of the next generation of C3S Earth system global and regional reanalyses. It describes ensemble-based unified land data assimilation and coupling infrastructure and methodology developments conducted in the first 18 months of the project. Work on machine-learning based observation operator to enhance the exploitation of passive microwave data is introduced, presenting the training databases, the machine learning approaches developed, and results comparing simulated and observed brightness temperature from AMSR2. Results from numerical experiments show the benefits of using ensemble-based land data assimilation for surface and near-surface weather representation both at regional and global scales. The first CERISE global land reanalysis prototype is presented. Its results are compared to state-of-the-art operational reanalysis using a set of newly developed diagnostic tools. Infrastructure, methodology and scientific results presented highlight the feasibility and the added value of the integration of the CERISE developments in the existing C3S core service.

How to cite: de Rosnay, P., Aires, F., Bojarova, J., Brookshaw, A., Calvet, J.-C., Cardinali, C., Herbert, C., Hersbach, H., Knight, J., Pérez-Zanón, N., Schyberg, H., Stockdale, T., Trigo, I., Vitart, F., and Weston, P.: Coupled land-atmosphere data assimilation developments in support of the next generation of Earth system reanalyses and seasonal prediction systems: The CopERnIcus Climate Change Service Evolution (CERISE) project, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-573, https://doi.org/10.5194/ems2024-573, 2024.