Reconstructing meridional flow speed variation from synthetic magnetic observations by using EnKF data assimilation

Abstract:

Meridional circulation plays an important role ingoverning cycle period and memory of solar dynamo models.Accurate knowledge of time variation in flow speed andprofile is crucial for estimating a solar cycle's amplitude,timing, rise and fall patterns and north-south asymmetry,which are ultimately responsible for causing space climatevariation. However, no consensus has been reached yet aboutthe Sun's meridional circulation pattern from observationsand theories. Therefore, it is necessary to implement dataassimilation approaches, in addition to observations andmodels, to investigate solar interior flow properties. Wepresent here first results from a sequential dataassimilation into a Babcock-Leighton flux-transport solardynamo model to reconstruct time-varying meridionalcirculation speed. We perform several observation systemsimulation experiments (OSSE) by implementing an EnsembleKalman Filter (EnKF) in the framework of the Data AssimilationResearch Testbed (DART). We find that the best reconstructionof time-variation in meridional flow-speed can be obtainedwhen ten or more observations are used with an updatingtime of 15 days and a 10% observational error. Increasingensemble-size from 16 to 160 improves reconstruction, buteven larger ensembles do not lead to further improvement.Comparison of reconstructed flow-speed with "true-state"reveals that EnKF data assimilation is very powerful forreconstructing meridional flow-speeds and suggests that it can be implemented for reconstructing spatio-temporal patterns of meridional circulation. This work is partially supported by NASA's LWS grant NNX08AQ34G. NCAR is sponsoredby the NSF.