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

He, Jianhui; Yue, Xinan; Wang, Wenbin; Wan, Weixing

doi:10.1029/2019ja026554

Cited by 17 publications

(42 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that the ionosphere forecast capability can be enhanced by initializing both thermosphere and ionosphere state variables via EnKF data assimilation method. As was shown on the majority of previous studies in observation system simulation experiments (Chartier et al, 2013;He et al, 2019;Hsu et al, 2014), updating the thermosphere states (Tn, O, O 2 ) simultaneously has more impact on the ionosphere electron density prediction than the ionosphere state variables only (e.g., O + ). The reason is that…”

Section: Discussionmentioning

confidence: 80%

“…Meanwhile, given that we believe in the observations much more than the model state, the ratio is typically small. In this paper, the value of α is set to be 0.01, which is same as the value we used in the previous reasonable ensemble experiment results (He et al, ). It is clear that the observations can be given much more weights than the model state when the value is equal to be 0.01.…”

Section: Data Assimilation Prediction Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm

Yue

et al. 2020

Space Weather

Self Cite

View full text Add to dashboard Cite

In this work, we evaluated the quasi‐realistic ionosphere forecasting capability by an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as the background model in the system. The slant total electron contents (TECs) from global International Global Navigation Satellite Systems Service ground‐based receivers and from the Constellation Observing System for Meteorology, Ionosphere and Climate are assimilated into the system, and the ionosphere is then predicted in advance during the quiet interval of 23 to 27 March 2010. The predicted ionosphere vertical TEC (VTEC) and the critical frequency foF2 are validated by the Massachusetts Institute of Technology VTEC and global ionosondes network, respectively. We found that the ionosphere forecast quality could be enhanced by optimizing the thermospheric neutral components via the EnKF method. The ionosphere electron density forecast accuracy can be improved by at least 10% for 24 hr. Furthermore, the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Global Ultraviolet Imager (TIMED/GUVI) [O/N2] observations are used to validate the predicted thermosphere [O/N2]. The validation shows that the [O/N2] optimized by EnKF has better agreement with the TIMED/GUVI observation. This study further demonstrates the validity of EnKF in enhancing the ionospheric forecast capability in addition to our previous observing system simulation experiments by He et al. (2019, https://doi.org/10.1029/2019JA026554).

show abstract

Section: Discussionmentioning

confidence: 80%

Section: Data Assimilation Prediction Descriptionmentioning

confidence: 99%

Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm

Yue

et al. 2020

Space Weather

Self Cite

View full text Add to dashboard Cite

show abstract

“…The unique feature of our developed system is that we used the sparse matrix method to avoid huge storage and computation in the EnKF (Yue et al, 2014). The detail description of this EnKF data assimilation system can be found in He et al (2019, ). Table 2 summarizes the configuration of key parameters used in the OSSE studies.…”

Section: Enkf Data Assimilation Algorithm Descriptionmentioning

confidence: 99%

Observing System Impact on Ionospheric Specification Over China Using EnKF Assimilation

Yue

et al. 2020

Space Weather

Self Cite

View full text Add to dashboard Cite

Accurate ionospheric specification for the current and future is one of the key tasks in operational space weather. In this work, we have assessed the effect of a dense ground network consisting of different radio instruments either developed or under developing on ionosphere nowcasting and forecasting over China and adjacent region (0-60°N and 70-140°E) through observing system simulation experiments. The data assimilation system is an Ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as a background model. Effects of different observation types, including the slant total electron content from Beidou System geostationary satellite and Global Position System and electron density observations from ionosondes and incoherent scattering radar on the ionospheric nowcasting and forecasting, are examined by the accurate specification of ionosphere key parameters (total electron content and 3-D electron density). We found that simultaneously assimilating different observation types can greatly improve the quality of ionosphere specification. Furthermore, updating the thermospheric state variables in the coupled thermosphere-ionosphere forecast model in the assimilation step plays an important role in improving the ionosphere forecasting. The ionosphere forecasting capability can last longer (>24 hr) in each observing system simulation experiment due to the adjusted thermosphere states. This study can provide a reference for observing system design over the China region and future ionosphere forecasting operation. Plain Language Summary In this work, the different observation data types are ingested into the first principle physical model TIEGCM by Ensemble Kalman filter algorithm to evaluate the effect of every kind of data on the nowcasting and forecasting of ionosphere parameters in China and adjacent region (0-60°N and 70-140°E). Three different kinds of observations are used to evaluate the nowcasting and 24 hr predictions of ionosphere parameters in the observation system simulation experiments, which includes the slant total electron content data from Beidou geostationary satellite systems and other global navigation satellite systems (e.g., Global Position System) and electron density profile from ionosondes and incoherent scattering radar. We found that the better ionosphere ionospheric nowcasting and forecasting results can be achieved by simultaneously assimilating above three different types of observations. Meanwhile, the improvement of thermosphere states can be found in each observing system simulation experiment by the comparison of the value of [O/N 2 ]. The ionosphere forecasting capability can be enhanced to be 24 hr due to the adjusted thermosphere states.

show abstract

“…Given that more ground GNSS receivers have the capability to track multiple navigation signals, She CL et al (2020) proposed a new method to estimate the receiver's differential code bias (DCB), based on the assumption that the ionosphere has local spherical symmetry; this assumption allows calibration of the slant TEC self‐consistently without the aid of another data source or model. He JH et al (2019) constructed an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation system using the National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model (NCAR‐TIEGCM) as the background model. They use a sparse matrix method to avoid significant matrix related calculation and storage.…”

Section: Modeling and Data Assimilationmentioning

confidence: 99%

Recent ionospheric investigations in China (2018–2019)

Liu

Wan

2020

Earth and Planetary Physics

Self Cite

View full text Add to dashboard Cite

Key Points:Ionospheric observations have continuously accumulated with increasing GNSS receivers being installed q Studies of ionospheric climatology and space weather highlight new physical understanding q Study of ionospheric irregularities/scintillations reports interesting features q Abstract: Since the release of the 2018 National Report of China on ionospheric research ( Liu LB and Wan WX, 2018) to the Committee on Space Research (COSPAR), scientists from Mainland China have made many new fruitful investigations of various ionospheric-related issues. In this update report, we briefly introduce more than 130 recent reports ( [2018][2019]. The current report covers the following topics: ionospheric space weather, ionospheric structures and climatology, ionospheric dynamics and couplings, ionospheric irregularity and scintillation, modeling and data assimilation, and radio wave propagation in the ionosphere and sounding techniques. ΔV para (m/s) SYM-H (nT) V para (m/s) V para (m/s) ΔV para (m/s) Figure 2. (a) The SYM-H index on 13-17 July 2012. The blue line marks the storm onset (at 06:42 UT on 15 July); yellow and gray areas indicate the 24-hour storm-time and 24-hour quiet-time intervals. (b) The blue dots denote the storm-time equatorial field-aligned plasma drifts observed by C/NOFS with red curve representing median and red lines denoting upper and lower quartile values. (c) Same as panel (b), but for the 24-hour quiet time interval. (d) The difference between (b) and (c). (e-h) Same as panels (a-d), but for the case on 7-11 July 2012 where the 24-hour quiet and storm intervals start at 04:12 UT on 7 and 9 July, respectively, after Zhang R et al. (2018). Earth and Planetary Physics

show abstract

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

Cited by 17 publications

References 26 publications

Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm

Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm

Observing System Impact on Ionospheric Specification Over China Using EnKF Assimilation

Recent ionospheric investigations in China (2018–2019)

Contact Info

Product

Resources

About