Challenges in Specifying and Predicting Space Weather. Issue 2 (11th February 2021)

• Record Type: Journal Article
• Authors: Schunk, R. W.; Scherliess, L.; Eccles, V.; Gardner, L. C.; Sojka, J. J.; Zhu, L.; Pi, X.; Mannucci, A. J.; Komjathy, A.; Wang, C.; Rosen, G.
• Is Part Of: Space weather. Volume 19:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-11
• Subjects: Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2019SW002404
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: Physics‐based Data Assimilation (DA) has been shown to be a powerful technique for specifying and predicting space weather. However, it is also known that different data assimilation models simulating the same geophysical event can display different space weather features even if the same data are assimilated. In this study, we used our Multimodel Ensemble Prediction System (MEPS) of DA models to elucidate the similarities and differences in the individual DA model reconstructions of the mid‐low latitude ionosphere when the same data are assimilated. Ensemble model averages were also obtained. For this ensemble modeling study, we selected the quiet/storm period of 16 and 17 March 2013 (equinox, solar medium). Five data assimilation models and one physics‐based model were used to produce an ensemble mean output for Total Electron Content (TEC), ionospheric peak density ( N m F 2 ), and ionospheric peak height ( h m F 2 ) for latitudes less than 60° and all longitudes. The data assimilated included ground‐based Global Positioning Satellite TEC and topside plasma densities near 800 km altitude derived from the COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) satellites. Both a simple average and a weighted average of the models were used in the ensemble averaging in order to determine if there was an improvement of the ensemble averages over the individual models. Key Points: A Multimodel Ensemble Prediction System of data assimilation models was used to elucidate differences in the model reconstructions A simple average of the ensemble of models was better than the individual models for TEC and N m F 2, but not for h m F 2 A weighted mean of the ensemble of the models was better than a simple mean