DATA ASSIMILATION in A SOLAR DYNAMO MODEL USING ENSEMBLE KALMAN FILTERS: SENSITIVITY and ROBUSTNESS in RECONSTRUCTION of MERIDIONAL FLOW SPEED

We implement an Ensemble Kalman Filter procedure using the Data Assimilation Research Testbed for assimilating synthetic meridional flow-speed data in a BabcockLeighton-type flux-transport solar dynamo model. By performing several observing system simulation experiments, we reconstruct time variation in meridional flow speed and analyze sensitivity and robustness of reconstruction. Using 192 ensemble members including 10 observations, each with 4% error, we find that flow speed is reconstructed best if observations of nearsurface poloidal fields from low latitudes and tachocline toroidal fields from midlatitudes are assimilated. If observations include a mixture of poloidal and toroidal fields from different latitude locations, reconstruction is reasonably good for ≤40% error in low-latitude data, even if observational error in polar region data becomes 200%, but deteriorates when observational error increases in low- and midlatitude data. Solar polar region observations are known to contain larger errors than those in low latitudes; our forward operator (a flux-transport dynamo model here) can sustain larger errors in polar region data, but is more sensitive to errors in low-latitude data. An optimal reconstruction is obtained if an assimilation interval of 15 days is used; 10- and 20-day assimilation intervals also give reasonably good results. Assimilation intervals <5 days do not produce faithful reconstructions of flow speed, because the system requires a minimum time to develop dynamics to respond to flow variations. Reconstruction also deteriorates if an assimilation interval >45 days is used, because the systems inherent memory interferes with its short-term dynamics during a substantially long run without updating.