Sequential Data Assimilation In A Flux-transport Dynamo Model Using Ensemble Kalman Filter

Flux-transport dynamo models are able to explain many global solar cycle features. However, the causes of variations in cycle amplitude, timing and shape of a cycle from cycle to cycle are still not well understood. One of the plausible candidates for governing the rise, peak and fall patterns of a cycle is the spatio-temporal variation in meridional circulation, which is not known in the entire dynamo domain. Identifying the parameters, which govern the spatio-temporal pattern of meridional flow, as the state vectors we apply an Ensemble Kalman Filter to these state vectors and create an ensemble of time-varying magnetic fields by advancing our dynamo model. After a specified time advancement within a solar cycle, an in-built Monte Carlo step within DART framework selects the simulation closest to observation. Ensemble member for that simulation corresponds to the correct meridional flow in this case. Repeating the procedure we advance the model sequentially in an entire solar cycle and derive the rise and fall patterns of that cycle. Combining sequentially the ensemble members, we construct the spatio-temporal pattern of meridional flow for an entire cycle. We close by presenting a specific case derived from this study, namely the behavior of meridional flow in cycle 23.