Assimilation of DMSP/SSUSI UV data into IDA4D

Monday, 15 December 2014
Lynette J Gelinas1, Gary S Bust2, Douglas G Brinkman1, Paul R Straus1 and Raymond L Swartz1, (1)The Aerospace Corporation, Los Angeles, CA, United States, (2)JHU Applied Physics Lab, Laurel, MD, United States
Ionospheric Data Assimilation Four-Dimensional (IDA4D) is a continuous-time, three-dimensional imaging algorithm that can produce 4D electron density specifications for various science investigations [e.g.,  Bust et al., 2007]. IDA4D is based on three-dimensional variational (3DVAR) data assimilation [Daley and Barker, 2001]. The algorithm combines various data sources and their associated error covariances with a background model (in this case the IRI) and its covariances to produce an ionospheric specification with formal uncertainties. IDA4D employs a Gauss- Markov Kalman filter technique similar to that used by operational assimilation models. The model can ingest a broad spectrum of data types that are either linearly or non-linearly related to electron density, including ground-based TEC, space-based TEC as measured by GPS occultation sensors and UV emissions associated with nightside recombination of O+.

IDA4D has been undergoing testing at The Aerospace Corporation to determine its performance with respect to combinations of input data sets under different conditions (solar minimum, solar maximum, geomagnetic activity). The results presented here summarize the performance of IDA4D when UV data is ingested, both with and without additional TEC measurements. The UV data used in the study summarized here are 135.6 nm emissions measured the SSUSI instruments on F16 and F18 DMSP. We discuss the process by which UV data is ingested into IDA4D, including data binning, error estimation and correction of 135.6 nm contamination from mutual neutralization of O+ and O-. Model performance is then assessed using comparisons to various ground truth data, including ISR data, Jason VTEC, CNOF/S in-situ plasma density and ionosonde-derived NmF2 values. The results of this study show that UV data improves model performance, particularly when TEC data coverage is sparse.

Bust, G. S., G. Crowley, T. W. Garner, T. L. Gaussiran II, R. W. Meggs, C. N. Mitchell, P. S. J. Spencer, P. Yin, and B. Zapfe (2007) ,Four Dimensional GPS Imaging of Space-Weather Storms, Space Weather, 5, S02003, doi:10.1029/2006SW000237.

Daley, R. & Barker, E., NAVDAS: Formulation and Diagnostics. Monthly Weather Review 129, 869 (2001).