Advancements in the ADAPT Photospheric Flux Transport Model

TL;DR

The paper discusses the implementation of the local ensemble transform Kalman filter (LETKF) in the ADAPT model to improve solar photospheric magnetic flux maps, enhancing predictions of solar and space weather.

Contribution

It introduces the integration of LETKF into ADAPT, demonstrating advantages over previous data assimilation methods for solar flux transport modeling.

Findings

Enhanced flux map accuracy through LETKF implementation

Improved propagation of observational data to unobserved regions

Reduction of spurious correlations in ensemble data assimilation

Abstract

Global maps of the solar photospheric magnetic flux are fundamental drivers for simulations of the corona and solar wind and therefore are important predictors of geoeffective events. However, observations of the solar photosphere are only made intermittently over approximately half of the solar surface. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model uses localized ensemble Kalman filtering techniques to adjust a set of photospheric simulations to agree with the available observations. At the same time, this information is propagated to areas of the simulation that have not been observed. ADAPT implements a local ensemble transform Kalman filter (LETKF) to accomplish data assimilation, allowing the covariance structure of the flux transport model to influence assimilation of photosphere observations while eliminating spurious correlations between ensemble members arising from a limited ensemble size. We give a detailed account of the implementation of the LETKF into ADAPT. Advantages of the LETKF scheme over previously implemented assimilation methods are highlighted.