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SUMMARY:Machine learning seminar series - Exploring Machine Learning for D
 ata Assimilation
DTSTART:20200507T100000Z
DTEND:20200507T153000Z
DTSTAMP:20260720T161300Z
UID:indico-event-197@events.ecmwf.int
CONTACT:events@ecmwf.int
DESCRIPTION:\n\n\nHost\n\nMassimo Bonavita\n\nSpeaker\n\nAlban Farchi\n\nA
 lban Farchi is a recently hired permanent researcher at CEREA. He works in
  the field of data assimilation for the geosciences with application to at
 mospheric chemistry. Currently\, he is a visitor of the ECMWF where he wor
 ks on machine learning applications to numerical weather forecasts. \n\nA
 bstract\n\nRecent developments in machine learning (ML) have demonstrated 
 impressive skills in reproducing complex spatiotemporal processes by effic
 iently using a huge amount of data. ML methods rely on flexible and parall
 elisable tools to enable optimisation in high dimension. However\, contrar
 y to data assimilation (DA)\, the underlying assumption behind ML methods 
 is that the system is fully observed and without noise\, which is rarely t
 he case in numerical weather prediction. In order to circumvent this issue
 \, it is possible to embed the ML problem into a DA formalism characterise
 d by a cost function similar to that of the weak-constraint 4D-Var (Bocque
 t et al.\, 2019\; Bocquet et al.\, 2020). In practice ML and DA are combin
 ed to solve the problem: DA is used to estimate the state of the system wh
 ile ML is used to estimate the full model. This approach has been implemen
 ted and successfully tested with low-order one-dimensional models. Using a
  sufficiently long trajectory of the model\, they are able to reconstruct 
 the model dynamics.\n\nIn realistic systems\, the model dynamics can be ve
 ry complex and it may not be possible to reconstruct it from scratch. An a
 lternative could be to learn the model error of an already existent model 
 using the same approach combining DA and ML. The feasibility of the method
  is first tested using the QG model developed in OOPS. In this presentatio
 n\, we briefly describe the QG model and the kind of model error that will
  be learnt. We then show the results of preliminary ML experiments\, and w
 e present what will be the next steps of the study. \n\nBocquet\, M.\, Br
 ajard\, J.\, Carrassi\, A.\, and Bertino\, L.: Data assimilation as a lear
 ning tool to infer ordinary differential equation representations of dynam
 ical models\, Nonlin. Processes Geophys.\, 26\, 143–162\, 2019\n\nBocque
 t\, M.\, Brajard\, J.\, Carrassi\, A.\, and Bertino\, L.: Bayesian inferen
 ce of chaotic dynamics by merging data assimilation\, machine learning and
  expectation-maximization\, Foundations of Data Science\, 2 (1)\, 55-80\, 
 2020\n\n\nhttps://events.ecmwf.int/event/197/
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LOCATION:11:00 BST
URL:https://events.ecmwf.int/event/197/
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