scholarly journals Analyses of different propagation models for the estimation of the topside ionosphere and plasmasphere with an Ensemble Kalman Filter

2020 ◽  
Author(s):  
Tatjana Gerzen ◽  
David Minkwitz ◽  
Michael Schmidt ◽  
Eren Erdogan
Keyword(s):  
Kalman Filter ◽  
Electron Density ◽  
Ensemble Kalman Filter ◽  
Propagation Model ◽  
Topside Ionosphere ◽  
Reconstruction Technique ◽  
Propagation Models ◽  
Swarm Satellites ◽  
Iterative Reconstruction Technique

Abstract. The accuracy and availability of satellite-based applications like GNSS positioning and remote sensing crucially depends on the knowledge of the ionospheric electron density distribution. The tomography of the ionosphere is one of the major tools to provide link specific ionospheric corrections as well as to study and monitor physical processes in the ionosphere and plasmasphere. In this work, we apply an Ensemble Kalman Filter (EnKF) approach for the 4D electron density reconstruction of the topside ionosphere and plasmasphere with the focus on the investigation of different propagation models and compare them with the iterative reconstruction technique SMART+. The STEC measurements of eleven LEO satellites are assimilated into the reconstructions. We conduct a case study on a global grid with altitudes between 430 and 20200 km, for two periods of the year 2015 covering quiet to perturbed ionospheric conditions. Particularly, the performance of the methods to estimate independent STEC and electron density measurements from the three Swarm satellites is analysed. The results indicate that the methods EnKF with Exponential decay as the propagation model and SMART+ perform best, providing in summary the lowest residuals.

scholarly journals Analysis of different propagation models for the estimation of the topside ionosphere and plasmasphere with an ensemble Kalman filter

2020 ◽  
Vol 38 (6) ◽  
pp. 1171-1189
Author(s):  
Tatjana Gerzen ◽  
David Minkwitz ◽  
Michael Schmidt ◽  
Eren Erdogan
Keyword(s):  
Kalman Filter ◽  
Electron Density ◽  
Ensemble Kalman Filter ◽  
Propagation Model ◽  
Topside Ionosphere ◽  
Reconstruction Technique ◽  
Electron Content ◽  
Propagation Models ◽  
Total Electron ◽  
Swarm Satellites

Abstract. The accuracy and availability of satellite-based applications, like Global Navigation Satellite System (GNSS) positioning and remote sensing, crucially depend on the knowledge of the ionospheric electron density distribution. The tomography of the ionosphere is one of the major tools for providing links to specific ionospheric corrections and studying and monitoring physical processes in the ionosphere and plasmasphere. In this work, we apply an ensemble Kalman filter (EnKF) approach for the 4D electron density reconstruction of the topside ionosphere and plasmasphere, with the focus on the investigation of different propagation models, and compare them with the iterative reconstruction technique of simultaneous multiplicative column normalized method plus (SMART+). The slant total electron content (STEC) measurements of 11 low earth orbit (LEO) satellites are assimilated into the reconstructions. We conduct a case study on a global grid with altitudes between 430 and 20 200 km, for two periods of the year 2015, covering quiet to perturbed ionospheric conditions. Particularly the performance of the methods for estimating independent STEC and electron density measurements from the three Swarm satellites is analysed. The results indicate that the methods of EnKF, with exponential decay as the propagation model, and SMART+ perform best, providing, in summary, the lowest residuals.

The Ensemble Kalman Filter for Groundwater Plume Characterization: A Case Study

Ground Water ◽  
2018 ◽  
Vol 56 (4) ◽  
pp. 571-579 ◽  
Author(s):  
James L. Ross ◽  
Peter F. Andersen
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter

Improving numerical forecast accuracy with ensemble Kalman filter and chaos theory: Case study on Ciliwung river model

2014 ◽  
Vol 512 ◽  
pp. 540-548 ◽  
Author(s):  
Yabin Sun ◽  
Chi Dung Doan ◽  
Anh Tuan Dao ◽  
Jiandong Liu ◽  
Shie-Yui Liong
Keyword(s):  
Kalman Filter ◽  
Chaos Theory ◽  
Ensemble Kalman Filter ◽  
Forecast Accuracy ◽  
Numerical Forecast

scholarly journals Comparison of Ensemble Kalman Filter–Based Forecasts to Traditional Ensemble and Deterministic Forecasts for a Case Study of Banded Snow

2012 ◽  
Vol 27 (1) ◽  
pp. 85-105 ◽  
Author(s):  
Astrid Suarez ◽  
Heather Dawn Reeves ◽  
Dustan Wheatley ◽  
Michael Coniglio
Keyword(s):  
Kalman Filter ◽  
Flow Pattern ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Closed Surface ◽  
Flow Patterns ◽  
Precipitation Forecast ◽  
Ensemble Mean ◽  
Correct Location

Abstract The ensemble Kalman filter (EnKF) technique is compared to other modeling approaches for a case study of banded snow. The forecasts include a 12- and 3-km grid-spaced deterministic forecast (D12 and D3), a 12-km 30-member ensemble (E12), and a 12-km 30-member ensemble with EnKF-based four-dimensional data assimilation (EKF12). In D12 and D3, flow patterns are not ideal for banded snow, but they have similar precipitation accumulations in the correct location. The increased resolution did not improve the quantitative precipitation forecast. The E12 ensemble mean has a flow pattern favorable for banding and precipitation in the approximate correct location, although the magnitudes and probabilities of relevant features are quite low. Six members produced good forecasts of the flow patterns and the precipitation structure. The EKF12 ensemble mean has an ideal flow pattern for banded snow and the mean produces banded precipitation, but relevant features are about 100 km too far north. The EKF12 has a much lower spread than does E12, a consequence of their different initial conditions. Comparison of the initial ensemble means shows that EKF12 has a closed surface low and a region of high low- to midlevel humidity that are not present in E12. These features act in concert to produce a stronger ensemble-mean cyclonic system with heavier precipitation at the time of banding.

scholarly journals Plasmasphere and Topside Ionosphere Reconstruction using METOP Satellite Data during Geomagnetic Storms

2020 ◽  
Author(s):  
Fabricio dos Santos Prol ◽  
Mainul Hoque ◽  
Arthur Amaral Ferreira
Keyword(s):  
Electron Density ◽  
Space Weather ◽  
Geomagnetic Storms ◽  
Tomographic Reconstruction ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Topside Ionosphere ◽  
Reconstruction Technique ◽  
Electron Content ◽  
Total Electron

As part of the space weather monitoring, the response of the ionosphere and plasmasphere to geomagnetic storms is typically under continuous supervision by operational services. Fortunately, Global Navigation Satellite System (GNSS) receivers on board low Earth orbit satellites provides a unique opportunity for developing image representations that can capture the global distribution of the electron density in the plasmasphere and topside ionosphere. Among the difficulties of plasmaspheric imaging based on GNSS measurements, the development of procedures to invert the Total Electron Content (TEC) into electron density distributions remains as a challenging task. In this study, a new tomographic reconstruction technique is presented to estimate the electron density from TEC data along the METOP (Meteorological Operational) satellites. The proposed method is evaluated during four geomagnetic storms to check the capabilities of the tomography for space weather monitoring. The investigation shows that the developed method can successfully capture and reconstruct well-known enhancement and decrease of electron density variabilities during storms. The comparison with in-situ electron densities has shown an improvement around 11% and a better description of plasma variabilities due to the storms compared to the background. Our study also reveals that the plasmasphere TEC contribution to ground-based TEC may vary 10 to 60% during geomagnetic storms, and the contribution tends to reduce during the storm-recovery phase

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