Understanding
the dynamo mechanism and predicting the cyclic solar activity are among
the most important key problems of the LWS program. Recent advances in
dynamo modeling and magnetographic and helioseismic observations have
provided important insights into the basic mechanism of the solar
cycle. However, the physics-based forecasting of the strength and
timing of the solar cycles is still not possible because of numerous
uncertainties in the parameter values of dynamo models, such as kinetic
and magnetic helicities, magnetic field diffusion and the magnetic flux
transport by meridional circulation. The observational data provide
only weak constraints on the surface magnetic field and on the plasma
dynamics of the solar interior where the dynamo operates. We propose to
investigate a new approach for modeling and forecasting magnetic
properties of the solar cycles by applying data assimilation methods to
solar dynamo models. This approach will allow us to determine the
importance of various model characteristics for estimating of the
physical state of the solar dynamo and for forecasting the future cycle.
The data
assimilation methods, such as the Ensemble Kalman Filter (EnKF), have
been used successfully for weather and climate modeling forecasting.
They provide the best conditional estimates of past, present, and even
future states for a given set of measurements, and can do so even when
the precise nature of the modeled system is unknown. Our research plan
is based on the implementation of data assimilation methods, in
particular, the EnKF method, using previously developed 2D and 3D
dynamo codes, synoptic magnetic field data for the past three cycles
and helioseismology data.
