Reference number : GC-2017-Q1-02473, amendment number : , agreement type : G, recipient legal name : Dowd, Michael (Dalhousie University), recipient country : CA, recipient province : nan, recipient city : Nova Scotia|Nouvelle-Écosse, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Successful environmental prediction requires making effective use of simulation models and available observations. Statistical modeling provides a means to combine these two sources of information, as well as scientific knowledge, in order to learn as much as possible about the system under consideration, a procedure that is sometimes referred to as data assimilation. Key application areas are in weather, climate, and marine forecasting. Special estimation methods must be developed and applied since complex environmental models are constantly being refined as scientific and computational advances are incorporated in the simulation code. Observational technologies have become increasingly sophisticated ranging from time series from new sensor types, spatial imagery from satellites, and complex autonomous instruments that probe and adaptively transit the ocean depths. In the fusion of data and model lies the promise and possibility of skillful environmental prediction and a better scientific understanding of these systems. My proposed work deals with further development of advanced data assimilation that uses state-of-the-art statistical methods to move us towards this goal., program purpose french : Successful environmental prediction requires making effective use of simulation models and available observations. Statistical modeling provides a means to combine these two sources of information, as well as scientific knowledge, in order to learn as much as possible about the system under consideration, a procedure that is sometimes referred to as data assimilation. Key application areas are in weather, climate, and marine forecasting. Special estimation methods must be developed and applied since complex environmental models are constantly being refined as scientific and computational advances are incorporated in the simulation code. Observational technologies have become increasingly sophisticated ranging from time series from new sensor types, spatial imagery from satellites, and complex autonomous instruments that probe and adaptively transit the ocean depths. In the fusion of data and model lies the promise and possibility of skillful environmental prediction and a better scientific understanding of these systems. My proposed work deals with further development of advanced data assimilation that uses state-of-the-art statistical methods to move us towards this goal., agreement title english : Bayesian Data Assimilation, agreement title french : Bayesian Data Assimilation, agreement number : RGPIN, agreement value : 148500.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada