Che-Wei Hsu

Ionospheric data assimilation with thermosphere-ionosphere-electrodynamics general circulation model and GPS-TEC during geomagnetic storm conditions

The main purpose of this paper is to investigate the effects of rapid assimilation-forecast cycling on the performance of ionospheric data assimilation during geomagnetic storm conditions. An ensemble Kalman filter software developed by the National Center for Atmospheric Research (NCAR), called Data Assimilation Research Testbed, is applied to assimilate ground-based GPS total electron content (TEC) observations into a theoretical numerical model of the thermosphere and ionosphere (NCAR thermosphere-ionosphere-electrodynamics general circulation model) during the 26 September 2011 geomagnetic storm period. Effects of various assimilation-forecast cycle lengths: 60, 30, and 10 min on the ionospheric forecast are examined by using the global root-mean-squared observation-minus-forecast (OmF) TEC residuals. Substantial reduction in the global OmF for the 10 min assimilation-forecast cycling suggests that a rapid cycling ionospheric data assimilation system can greatly improve the quality of the model forecast during geomagnetic storm conditions. Furthermore, updating the thermospheric state variables in the coupled thermosphere-ionosphere forecast model in the assimilation step is an important factor in improving the trajectory of model forecasting. The shorter assimilation-forecast cycling (10 min in this paper) helps to restrain unrealistic model error growth during the forecast step due to the imbalance among model state variables resulting from an inadequate state update, which in turn leads to a greater forecast accuracy.

Assessment of the Impact of FORMOSAT‐7/COSMIC‐2 GNSS RO Observations on Midlatitude and Low‐Latitude Ionosphere Specification: Observing System Simulation Experiments Using Ensemble Square Root Filter

The Formosa Satellite-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (FORMOSAT-7/COSMIC-2) Global Navigation Satellite System radio occultation (RO) payload can provide global observations of slant total electron content (sTEC) with an unprecedentedly high spatial temporal resolution. Recently, a new ionospheric data assimilation system, the Community Gridpoint Statistical Interpolation (GSI) Ionosphere, is constructed with the National Oceanic and Atmospheric Administration GSI Ensemble Square Root Filter and the Global Ionosphere Plasmasphere and the Thermosphere Ionosphere Electrodynamic General Circulation Model. The paper demonstrates the capability of the GSI Ionosphere to improve the ionospheric specification and make a quantitative assessment of the impact of FORMOSAT-7/COSMIC-2 RO data on the ionospheric observing system simulation experiments conducted to calibrate key Ensemble Square Root Filter parameters that control detrimental effects of the sampling errors, particularly on the ensemble-based estimation of the correlation between observations and model states, in order to yield high-quality assimilation analysis. Results from the observing system simulation experiments show that (1) an ensemble size larger than 70 is recommended for assimilation of RO sTEC data with the GSI Ionosphere and (2) localizing the impact of observations around the tangent points in the horizontal direction with a length scale of 5,000 km is effective in improving assimilation analysis quality. Assimilation of sTEC data from FORMOSAT-7/COSMIC-2 can considerably improve the global ionospheric specification through the application of the GSI Ionosphere. The GSI Ionosphere can provide instantaneous global pictures of the ionosphere variability and help characterize day-to-day variability of the ionosphere and deepen our understanding of the observed day-to-day variability.