Statistical-Dynamical Observation Operator for SST Data Assimilation
PI and organization: Andrea Storto (CMCC)
Co-Is: Gerasimos Korres (HCMR), Sam Pimentel (TWU), Nadia Pinardi (CMCC), Isabelle Mirouze (CMCC), Eric Jansen (CMCC), Francesca Macchia (CMCC).
Abstract: Advancing capability to effectively assimilate satellite observations of SST with proper account of diurnal cycle together with thermo-dynamical coupling with near-surface ocean state is highly relevant to increasing accuracy of ocean analysis and prediction.
To ensure benefits of advanced SST assimilation in the future for diverse operational systems, as well as to enhance capacity to make optimal assessments of impact of current and future satellite SST observations in step with progress in the ocean satellite observing, it is desirable to investigate more efficient approaches. The objective of this project is to develop and test a new technique for producing dynamically-based but highly efficient statistical observation operators for assimilation of daily satellite SST observations that could be easily implemented with any data assimilation technique. The technique consists of application of statistical method of canonical correlation analysis to joint data from a process model of near surface ocean thermo-dynamics and the corresponding satellite retrievals of SST from different sensors. This approach may streamline the progress toward improving the SST-DA in different operational DA systems in CMEMS, enriching overall DA technology available in CMEMS in support of improving the capacity to perform optimal assessments of SST observation impacts.