Improving Solar Cycle Prediction Using Variational Data Assimilation in a Mean-Field Dynamo Model

Thursday, 18 December 2014
Ching Pui Hung, CEA Saclay DSM / LSCE, Gif sur Yvette, CDX, France, Laurene Jouve, IRAP - Observatoire Midi Pyrenees, Toulouse, France, Sacha Brun, CEA Commissariat à l'Energie Atomique Saclay, Gif-Sur-Yvette Cedex, France; AIM, CEA, SACLAY, France and Alexandre Fournier, Institut de Physique du Globe de Paris, Paris, France
We present our recent effort to implement modern variational data assimilation techniques into a mean field solar dynamo code. This work extends the work of (Jouve et al. 2011, ApJ) to take into account the correct spherical geometry and meridional circulation into so-called Babcock-Leigthon flux transport dynamo models. Based on twin-experiments, in which we observe our dynamo simulations, and on a well defined cost function using toroidal and poloidal field observations we are able to recover the main attributes of the dynamo solution used to test our data assimilation algorithm. By assimilating solar data (such as Wolf number or butterfly diagram) we are starting to deduce the profile and temporal variations of key ingredients of the solar dynamo. We find that the data sampling and the temporal window are key to get reliable results. We show how such a powerful technique can be used to improve our ability to predict the solar magnetic activity.