Camella Nasr, Research Scientist
Scientists are always looking for better ways to model the ionosphere, the upper layer of Earth’s atmosphere that affects radio signals. One promising but underutilized tool is the Oblique Ionogram (OI), which measures how long high-frequency (HF) radio waves take to travel between a transmitter and a receiver that are far apart. Unlike Vertical Ionograms (VIs)—which measure signals traveling straight up and down—OIs are more complex because HF waves take different paths depending on frequency. Until now, their potential for improving ionospheric models had not been fully explored.
Converting Oblique Ionograms into Vertical Ionograms
To make OI data easier to use, Orion developed a method to convert OIs into equivalent VIs, placing them at the midpoint between the transmitter and receiver. This transformation allowed researchers to test whether incorporating OI data into ionospheric models would improve their accuracy.
The approach was tested using a simulation experiment over the continental U.S. (CONUS), which involved:
Simulating OIs – Tracing HF signals at different frequencies and recording the time delays.
Transforming the Data – Converting these delays into vertical equivalents using a midpoint formula.
Generating Electron Density Profiles (EDPs) – Using the POLAN program to calculate electron density in the lower ionosphere.
Assimilating Data – Feeding the EDPs into Orion’s Modern Modular Model for Space Data Assimilation (M3SDA) using an Extended Kalman Filter (EKF).
Comparing Model Performance
The study compared how well ionospheric models performed with and without OI data:
Baseline 1 (I) – Models using only ionosonde (ground-based radar) data.
Baseline 2 (IRG) – Models using ionosonde data plus Total Electron Content (TEC) and Radio Occultation (RO) data.
Key Findings:
In the ionosonde-only baseline, adding OI data improved accuracy by reducing foF2 errors (a key ionospheric measurement) by about 0.5 MHz on average. However, results varied by location and time. In the Midwest, accuracy actually decreased, likely due to strong horizontal ionospheric gradients (sudden changes in electron density) affecting OI data.
In the IRG baseline, adding OI data improved accuracy by about 0.25 MHz across CONUS, but certain areas—like the West Coast and Florida—saw performance drop by 0.15 MHz.
Lessons Learned and Next Steps
This research shows that while OI data can significantly improve ionospheric models, it doesn’t always help. In some regions, the transformation method struggled with complex ionospheric conditions, particularly where horizontal gradients were strong.
Moving forward, Orion will refine the transformation process to better handle these challenges. Future research will focus on identifying when and where OI data is most beneficial, ensuring it is used in ways that maximize its value for space weather forecasting.
This study marks an important step in harnessing Oblique Ionograms to enhance our understanding of the ionosphere. Stay tuned as we continue to unlock their full potential in space science!
Click the link below to view the poster presentation of this work.
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