Dr. Andrea Storto is an environmental engineer and has obtained his Ph.D. in 2009 at the University of Rome La Sapienza in close collaboration with the Norwegian Meteorological Institute (met.no) through an EUMETSAT fellowship, studying the optimal assimilation of satellite radiances for regional medium-range NWP models by using variational assimilation schemes. In 2009, he joined the Ocean Modeling and Data Applications (ODA) Division of CMCC (Euro-Mediterannean Center for Climate Change, Bologna, Italy) to develop a Global Ocean variational analysis system at eddy-permitting resolution for use in the context of ocean reanalyses, in particular within the EU-funded MyOcean project and its follow-ups MyOcean2 and CMEMS. He significantly contributed to the development of high-resolution operational forecasting systems in the global ocean, the Black Sea and the Mediterranean Sea. His expertise mainly includes variational data assimilation, satellite altimetry, techniques for improving the representation of model errors in data assimilation and evaluation of ocean reanalyses. He coordinated the group on ocean data assimilation and forecasting at CMCC/ODA. In 2018, he joined the NATO/STO Centre for Maritime Research and Experimentation (CMRE, La Spezia, Italy) as research scientist to work on regional oceanographic and acoustic data assimilation. In 2021, he became research scientist at the Institute of Marince Sciences (ISMAR) of the National Research Council of Italy (CNR). He was co-chair of the CLIVAR panel on global observations and ocean syntheses (GSOP), and member of the GODAE Task Team on data assimilation. He was P.I. of the CMEMS SOSSTA project for the optimal formulation of sea surface temperature data assimilation, and he coauthors more than 70 scientific publications.
Numerical ocean prediction models are increasingly adopted tools for both climate monitoring and operational forecasting, in response to the societal demand, respectively, for a better understanding of the recent climatic changes and to serve operational activities such as oil spill monitoring, search and rescue, fishery, etc. Oceanic data assimilation algorithms have rapidly evolved to approach the maturity of NWP systems. Here, we present some examples extracted by recently published works. First, we show how enhanced ensemble generation methods, based on stochastic physics schemes, can benefit ensemble or hybrid analysis schemes in terms of both skill scores and mesoscale eddy activity. Second, approaches inherited from machine learning can be fruitfully embedded in classical variational schemes to improve the formulation of observation operators. Finally, through simplified model experiments, we provide further motivation to develop Earth system (coupled) assimilation algorithms.
Arranged date for the seminar talk: Oct 04, 2021 at 14:15.