Articles | Volume 9, issue 4
https://doi.org/10.5194/amt-9-1859-2016
https://doi.org/10.5194/amt-9-1859-2016
Research article
 | Highlight paper
 | 
28 Apr 2016
Research article | Highlight paper |  | 28 Apr 2016

Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements

Johannes Norberg, Ilkka I. Virtanen, Lassi Roininen, Juha Vierinen, Mikko Orispää, Kirsti Kauristie, and Markku S. Lehtinen

Related authors

Sodankylä ionospheric tomography data set 2003–2014
Johannes Norberg, Lassi Roininen, Antti Kero, Tero Raita, Thomas Ulich, Markku Markkanen, Liisa Juusola, and Kirsti Kauristie
Geosci. Instrum. Method. Data Syst., 5, 263–270, https://doi.org/10.5194/gi-5-263-2016,https://doi.org/10.5194/gi-5-263-2016, 2016
Short summary
Statistical framework for estimating GNSS bias
Juha Vierinen, Anthea J. Coster, William C. Rideout, Philip J. Erickson, and Johannes Norberg
Atmos. Meas. Tech., 9, 1303–1312, https://doi.org/10.5194/amt-9-1303-2016,https://doi.org/10.5194/amt-9-1303-2016, 2016
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Closing the gap in the tropics: the added value of radio-occultation data for wind field monitoring across the Equator
Julia Danzer, Magdalena Pieler, and Gottfried Kirchengast
Atmos. Meas. Tech., 17, 4979–4995, https://doi.org/10.5194/amt-17-4979-2024,https://doi.org/10.5194/amt-17-4979-2024, 2024
Short summary
Verification of weather-radar-based hail metrics with crowdsourced observations from Switzerland
Jérôme Kopp, Alessandro Hering, Urs Germann, and Olivia Martius
Atmos. Meas. Tech., 17, 4529–4552, https://doi.org/10.5194/amt-17-4529-2024,https://doi.org/10.5194/amt-17-4529-2024, 2024
Short summary
Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs
Hai Nguyen, Derek Posselt, Igor Yanovsky, Longtao Wu, and Svetla Hristova-Veleva
Atmos. Meas. Tech., 17, 3103–3119, https://doi.org/10.5194/amt-17-3103-2024,https://doi.org/10.5194/amt-17-3103-2024, 2024
Short summary
Rotary-wing drone-induced flow – comparison of simulations with lidar measurements
Liqin Jin, Mauro Ghirardelli, Jakob Mann, Mikael Sjöholm, Stephan Thomas Kral, and Joachim Reuder
Atmos. Meas. Tech., 17, 2721–2737, https://doi.org/10.5194/amt-17-2721-2024,https://doi.org/10.5194/amt-17-2721-2024, 2024
Short summary
Improving the Estimate of Higher Order Moments from Lidar Observations Near the Top of the Convective Boundary Layer
Tessa Rosenberger, David D. Turner, Thijs Heus, Girish N. Raghunathan, Timothy J. Wagner, and Julia Simonson
EGUsphere, https://doi.org/10.5194/egusphere-2024-868,https://doi.org/10.5194/egusphere-2024-868, 2024
Short summary

Cited articles

Andreeva, E. S.: Radio tomographic reconstruction of ionization dip in the plasma near the Earth, J. Exp. Theor. Phys. Lett., 52, 142–148, 1990.
Austen, J. R., Franke, S. J., and Liu, C. H.: Ionospheric imaging using computerized tomography, Radio Sci., 3, 299–307, 1988.
Bilitza, D. and Reinisch, B. W.: International Reference Ionosphere 2007: improvements and a new parameters, Adv. Space Res., 42, 599–609, 2008.
Breit, G. and Tuve, M. A.: A Test of the Existence of the Conducting Layer, Phys. Rev., 28, 554–575, 1926.
Bust, G. S. and Mitchell, C. N.: History, current state, and future directions of ionospheric imaging, Rev. Geophys., 46, RG1003, https://doi.org/10.1029/2006RG000212, 2008.
Download
Short summary
We validate 2-D ionospheric tomography reconstructions against EISCAT incoherent scatter radar measurements. The method is based on Bayesian statistical inversion. We employ ionosonde measurements for the choice of the prior distribution parameters and use a sparse matrix approximation for the computations. This results in a computationally efficient tomography algorithm with clear probabilistic interpretation. We find that ionosonde measurements improve the reconstruction significantly.