Day 4

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Paper title Towards the coupled assimilation of satellite radiances: Assimilating CIMR brightness temperatures in an atmosphere-ocean coupled variational analysis system
Authors
  1. Andrea Storto CNR ISMAR Speaker
  2. Gianluigi Liberti CNR ISMAR
  3. Daniele Ciani CNR-Institute of Marine Sciences
  4. Andrea Pisano National Research Council of Italy, Institute of Marine Sciences (CNR-ISMAR)
  5. Chunxue Yang National Research Council (CNR) - Institute of Marine Sciences (ISMAR)
  6. Anna Lewinschal University of Stockholm
  7. Rosalia Santoleri CNR (italian national research council)
Form of presentation Poster
Topics
  • A8. Ocean
    • A8.07 Oceanographic Change of the Arctic Ocean From Space
Abstract text It is expected that coupled air-sea data assimilation algorithms may enhance the exploitation of satellite observations whose measured brightness temperatures depend upon both the atmospheric and oceanic states, thus improving the resulting numerical forecasts. To demonstrate in practice the advantages of the fully coupled assimilation scheme, the assimilation of brightness temperatures from a forthcoming microwave sensor (the Copernicus Imaging Microwave Radiometer, CIMR) is evaluated within idealized assimilation and forecast experiments. The forecast model used here is the single-column version of a state-of-the-art Earth system model (EC-Earth), while a variational scheme, complemented with ensemble-derived background-error covariances, is adopted for the data assimilation problem.
The Copernicus Imaging Microwave Radiometer (CIMR), scheduled for the 2027+ timeframe, is a high priority mission of the Copernicus Expansion Missions Programme. Polarised (H and V) channels centered at 1.414, 6.925, 10.65, 18.7 and 36.5 GHz are included in the mission design under study. CIMR is thus designed to provide global, all-weather, mesoscale-to-submesoscale resolving observations of sea-surface temperature, sea-surface salinity and sea-ice concentration. The coupled observation operator is derived as polynomial regression from the application of the Radiative Transfer for TOVS (RTTOV) model, and we perform Observing System Simulation Experiments (OSSE) to assess the benefits of different assimilation methods and observations in the forecasts.
Results show that the strongly coupled assimilation formulation outperforms the weakly coupled one in both experiments assimilating atmospheric data and verified against oceanic observations and experiments assimilating oceanic observations verified against atmospheric observations. The sensitivity of the analysis system to the choice of the coupled background-error covariances is found significant and discussed in detail. Finally, the assimilation of microwave brightness temperature observations is compared to the assimilation of the corresponding geophysical retrievals (sea surface temperature and salinity and marine winds), in the coupled analysis system. We found that assimilating microwave brightness temperatures significantly increases the short-range forecast accuracy of the oceanic variables and near-surface wind vectors, while it is neutral for the atmospheric mass variables. This suggests that adopting radiance observation operators in oceanic and coupled applications will be beneficial for operational forecasts.