Open AccessAssessing models for ionospheric weather specifications over Australia during the 2004 Climate and Weather of the Sun‐Earth‐System (CAWSES) campaign
Author(s) -
Sojka J. J.,
Thompson D. C.,
Scherliess L.,
Schunk R. W.,
Harris T. J.
Publication year - 2007
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2006ja012048
Subject(s) - environmental science , international reference ionosphere , data assimilation , meteorology , ionosonde , ionosphere , tec , numerical weather prediction , weather forecasting , climatology , global forecast system , total electron content , geography , geology , electron density , quantum mechanics , electron , physics , geophysics
The Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) program is developing assimilation models to specify ionospheric weather. In this study the Gauss Markov Kalman Filter (GMKF) GAIM model was used. The period 20 March through 19 April 2004, which spanned the Climate and Weather of the Sun‐Earth‐System (CAWSES) first study period, has been extensively studied to validate the performance of the GAIM model. Although the USU‐GAIM model has both regional and global capabilities and can assimilate data from a wide variety of ionospheric observations, for this study the GMKF model was run in a global mode using data only from 162 ground‐based GPS slant total electron content (TEC) stations and in situ measurements from three satellites. Using measurements from the 11 ionosonde stations of the Australian Department of Defence sounder network as an independent bottomside ground‐truth, the International Reference Ionosphere (IRI), Ionospheric Forecast Model (IFM), and GMKF were compared for (1) monthly mean climatology and (2) the day‐to‐day weather during the 31 day period. A skill score was developed for the day‐to‐day weather by defining the IRI as the reference model. IFM is found to be a 10% improvement, while the GMKF is 39% more capable to capture weather variability. However, the study also identifies that this global version of GMKF has difficulty around sunrise, during which time the GMKF performance can be poorer than IRI. Excluding this interval from the skill score analysis increases the GMKF ability to track weather to 48%. The use of more data and different data types should further increase the GMKF's ability to capture weather variations.