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

Abstract

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.

Impact of Assimilating the FORMOSAT‐3/COSMIC and FORMOSAT‐7/COSMIC‐2 RO Data on the Midlatitude and Low‐Latitude Ionospheric Specification

“…Warm colors represent that the ensemble mean is larger than NR, which indicates a positive bias, and cold colors represent that the ensemble mean is smaller than NR, which indicates a negative bias. From 02:00 UT to 04:00 UT, negative biases disappear and positive biases start appearing in the low latitudes, which is similar to the result presented by Hsu et al ().…”

EnKF Ionosphere and Thermosphere Data Assimilation Algorithm Through a Sparse Matrix Method

“…The OSSE study has been proven an efficient method to demonstrate the capability of a DA system (Hsu et al, 2018;Yue et al, 2014). To evaluate our newly developed ionosphere/thermosphere EnKF DA system based on the sparse matrix method and TIEGCM, seven different OSSEs have been done in this work.…”

“…In the previous studies, the synthetic observations are sampled from NR based on the current or future observing system (Hsu et al, 2018;Matsuo et al, 2013). Observational errors (ε) are added to such synthetic observations, which are based on a centered Gaussian distribution with the standard deviation of σ of the magnitude of unit of observation data, namely, ε~Ν(0, σ 2 ).…”

“…Chartier et al (2016) further applied EnKF DA technique to storm time ionosphere forecast. Hsu et al (2018) did several observing system simulation experiments (OSSEs) based on the Ensemble Square Root Filter (EnSRF) for COSMIC-2 observations and concluded that the assimilation of slant TEC data from COSMIC-2 can considerably improve the global ionospheric specification and prediction. Hsu et al (2018) did several observing system simulation experiments (OSSEs) based on the Ensemble Square Root Filter (EnSRF) for COSMIC-2 observations and concluded that the assimilation of slant TEC data from COSMIC-2 can considerably improve the global ionospheric specification and prediction.…”

“…Hsu et al (2018) did several observing system simulation experiments (OSSEs) based on the Ensemble Square Root Filter (EnSRF) for COSMIC-2 observations and concluded that the assimilation of slant TEC data from COSMIC-2 can considerably improve the global ionospheric specification and prediction. However, in these above studies, the authors usually applied the commonly used software packages to implement EnKF/EnSRF, including the Gridpoint Statistical Interpolation (Hsu et al, 2018;Wu et al, 2002) and the Data Assimilation Research Testbed (Anderson et al, 2009;Chartier et al, 2016;Chen et al, 2017;Lee et al, 2012;Matsuo et al, 2013;Pedatella et al, 2018), which rely on large-scale parallel high-performance computing systems. However, in these above studies, the authors usually applied the commonly used software packages to implement EnKF/EnSRF, including the Gridpoint Statistical Interpolation (Hsu et al, 2018;Wu et al, 2002) and the Data Assimilation Research Testbed (Anderson et al, 2009;Chartier et al, 2016;Chen et al, 2017;Lee et al, 2012;Matsuo et al, 2013;Pedatella et al, 2018), which rely on large-scale parallel high-performance computing systems.…”