On Imaging South African Regional Ionosphere Using 4D‐var Technique

Ssessanga, Nicholas; Kim, Yong Ha; Habarulema, John Bosco; Kwak, Young‐Sil

doi:10.1029/2019sw002321

Cited by 16 publications

(39 citation statements)

References 88 publications

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“…In particular, the relatively slower and long-lasting variation of the updated thermospheric composition after assimilation can contribute to a longer timescale of ionosphere electron density forecasting using the advantage of a self-consistent thermosphere-ionosphere coupling implementation, even though the computation cost can currently be very high (e.g., Chartier et al, 2016;Hsu et al, 2014;Lee et al, 2012;Matsuo et al, 2013). (c) Data assimilation products based on empirical ionospheric models, such as the Ionospheric Data Assimilation Three/Four-Dimensional (Bust et al, 2004(Bust et al, , 2007, the United States TEC (Fuller-Rowell et al, 2006;Spencer et al, 2004), the Ionosphere Real-time Assimilative Model (Galkin et al, 2012(Galkin et al, , 2015, as well as other global/ regional assimilation products based on the widely used International Reference Ionosphere model (e.g., Aa et al, 2015Aa et al, , 2016Lin et al, 2015Lin et al, , 2017Ssessanga et al, 2019;Yue et al, 2012;Yue, Schreiner, Kuo, et al, 2014) or NeQuick model (e.g., Aa et al, 2018;Brunini et al, 2011;Nava et al, 2005Nava et al, , 2011. These empirical models have the advantages of low computational cost and can be easily incorporated into operational nowcasting services to yield ionospheric specification.…”

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confidence: 99%

3‐D Regional Ionosphere Imaging and SED Reconstruction With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS)

Zhang

Erickson

et al. 2022

Space Weather

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A new TEC‐based ionospheric data assimilation system (TIDAS) over the continental US and adjacent area (20°–60°N, 60°–130°W, and 100–600 km) has been developed through assimilating heterogeneous ionospheric data, including dense ground‐based Global Navigation Satellite System (GNSS) Total Electron Content (TEC) from 2,000+ receivers, Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation data, JASON satellite altimeter TEC, and Millstone Hill incoherent scatter radar measurements. A hybrid Ensemble‐Variational scheme is utilized to reconstruct the regional 3‐D electron density distribution: a more realistic and location‐dependent background error covariance matrix is calculated from an ensemble of corrected NeQuick outputs, and a three‐dimensional variational (3DVAR) method is adopted for measurement updates to obtain an optimal state estimation. The spatial‐temporal resolution of the reanalyzed 3‐D electron density product is as high as 1° × 1° in latitude and longitude, 20 km in altitude, and 5 min in universal time, which is sufficient to reproduce ionospheric fine structure and storm‐time disturbances. The accuracy and reliability of data assimilation results are validated using ionosonde and other measurements. TIDAS reanalyzed electron density is able to successfully reconstruct the 3‐D morphology and dynamic evolution of the storm‐enhanced density (SED) plume observed during the St. Patrick's day geomagnetic storm on 17 March 2013 with high fidelity. Using TIDAS, we found that the 3‐D SED plume manifests as a ridge‐like high‐density channel that predominantly occurred between 300 and 500 km during 19:00–21:00 UT for this event, with the F2 region peak height being raised by 40–60 km and peak density enhancement of 30%–50%.

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confidence: 99%

3‐D Regional Ionosphere Imaging and SED Reconstruction With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS)

Zhang

Erickson

et al. 2022

Space Weather

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“…The points mimic the expected case of either a datum lying inside (orange) or outside (blue) the grid. Solid black lines of different lengths d i , connect the ionosonde data point (reference point) to different grid points ( i ) that lie within a specified radius of influence r. Here, r was assumed to be 10°, a value based on studies that previously utilized mid-latitude horizontal correlation lengths (Bust et al 2004;Ssessanga et al 2019).…”

Section: Inclusion Of Ionosonde Datamentioning

confidence: 99%

“…The initialization of the iterative procedure in (11) requires the use of background estimates ⃗ X b and the initial best estimate to ⃗ X j−1 a (at j = 1). In most cases, ⃗ X b could have been obtained from a physics-based or empirical model (Thompson et al 2006;Ssessanga et al 2019). However, such models are generally empirically biased or with inherent errors that sometimes grow rapidly, thus reducing the fidelity of the final image.…”

Section: Data Assimilation (Da) Stepmentioning

confidence: 99%

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Complementing regional ground GNSS-STEC computerized ionospheric tomography (CIT) with ionosonde data assimilation

et al. 2021

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A near-real-time computerized ionospheric tomography (CIT) technique was developed over the East Asian sector to specify the 3-D electron density field. The technique is based on a plethora of Global Navigation Satellite System observables within the region of interest which is bounded horizontally 110°–160°E and 10°–60°N and extending from 80 to 25,000 km in altitude. Prior to deployment, studies validated the CIT results using ionosonde, middle-upper atmosphere radar and occultation data and found the technique to adequately reconstruct the regional ionosphere vertical structure. However, with room for improvement in estimating the peak height and avoiding physically unrealistic negative densities in the final solution, we present preliminary results from a technique that addresses these issues by incorporating CIT results into a data assimilation (DA) technique. The DA technique adds ionosonde bottomside measurements into CIT results, thereby improving the accuracy of the reconstructed bottomside 3-D structure. More specifically, on average CIT NmF2 and hmF2 improve by more than 60%. Further, during analysis, ionospheric electron densities are assumed to be better described by probability log-normal distribution, which introduces the positivity constraint that is mandatory in ionospheric imaging.

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“…Data assimilation techniques, such as Kalman filter algorithms (e.g., Chartier et al, 2016;Lee et al, 2012;Lin et al, 2015;Scherliess et al, 2004;Yue et al, 2011Yue et al, , 2012, 3D-Var (e.g., Aa et al, 2016;Bust et al, 2001Bust et al, , 2004Bust et al, , 2007, and 4D-Var techniques (e.g., Pi et al, 2003;Ssessanga et al, 2019;Wang et al, 2004), have a considerable advantage in improving the performance of ionospheric models. In particular, since the data assimilation is based on observations, it can be a useful tool for nowcasting, which can simulate the current state of the ionosphere.…”

Section: Introductionmentioning

confidence: 99%

Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

et al. 2020

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This paper presents a study on the possibility of predicting the regional ionosphere at midlatitude by assimilating the predicted ionospheric parameters from a neural network (NN) model into the Sami2 is Another Model of the Ionosphere (SAMI2). The NN model was constructed from the data set of Jeju ionosonde (33.43°N, 126.30°E) for the period of 1 January 2011 to 31 December 2015 by using the long-short term memory (LSTM) algorithm. The NN model provides 24-hr prediction of the peak density (NmF2) and peak height (hmF2) of the F2 layer over Jeju. The predicted NmF2 and hmF2 were used to compute two ionospheric drivers (total ion density and effective neutral meridional wind), which were assimilated into the SAMI2 model. The SAMI2-LSTM model estimates the ionospheric conditions over the midlatitude region around Jeju on the same geomagnetic meridional plane. We evaluate the performance of the SAMI2-LSTM by comparing predicted NmF2 and hmF2 values with measured values during the geomagnetic quiet and storm periods. The root-mean-square error values of NmF2 (hmF2) from Jeju ionosonde measurements are lower by 45% and 45% (30% and 11%) than those of the SAMI2 and IRI-2016 models during the geomagnetic quiet periods. However, during the geomagnetic storm periods, the performance of the SAMI2-LSTM model does not predict positive geomagnetic storms well. Comparing the quiet and storm periods for the SAMI2-LSTM model, the root-mean-square error (RMSE) of the storm period was calculated to be 2.76 (3.2) times higher at Jeju (Icheon) than in the quiet period. From these results, we demonstrated that in this study, the combination of the NN-LSTM model and physics-based model could improve the ionosphere prediction of existing theoretical and empirical models for midlatitude regions, at least in geomagnetically quiet conditions. We strongly suggest that this attempt, which has not been reported before, could be used as one of the keys to advance the physics-based model further.

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On Imaging South African Regional Ionosphere Using 4D‐var Technique

Cited by 16 publications

References 88 publications

3‐D Regional Ionosphere Imaging and SED Reconstruction With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS)

3‐D Regional Ionosphere Imaging and SED Reconstruction With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS)

Complementing regional ground GNSS-STEC computerized ionospheric tomography (CIT) with ionosonde data assimilation

Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

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