Data Assimilation in the ADAPT Photospheric Flux Transport Model
By Kyle S. Hickmann, Humberto C. Godinez, Carl J. Henney, Charles N. Arge. Solar Physics, 2014
169 citations recorded by the source corpus.
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.
Subfields: Astronomy and Astrophysics, Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/6711a924aa2964f7492eaad6bc06167e62d996c7
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Citations in the corpus
- The Open Flux Problem (2017, methodology): “…showed that PFSS models computed daily using maps from the Air Force Data Assimilative Photospheric flux Transport (ADAPT) model (Arge et al. 2010; Hickmann et al. 2015), generated with the assimilation of NSO Synoptic Optical Long-Term Investigation of the Sun (SOLIS) Vector Spectromagnetograph…”