Articles | Volume 9, issue 3
https://doi.org/10.5194/amt-9-1303-2016
https://doi.org/10.5194/amt-9-1303-2016
Research article
 | 
30 Mar 2016
Research article |  | 30 Mar 2016

Statistical framework for estimating GNSS bias

Juha Vierinen, Anthea J. Coster, William C. Rideout, Philip J. Erickson, and Johannes Norberg

Related authors

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
Atmos. Meas. Tech., 9, 1859–1869, https://doi.org/10.5194/amt-9-1859-2016,https://doi.org/10.5194/amt-9-1859-2016, 2016
Short summary
Coded continuous wave meteor radar
Juha Vierinen, Jorge L. Chau, Nico Pfeffer, Matthias Clahsen, and Gunter Stober
Atmos. Meas. Tech., 9, 829–839, https://doi.org/10.5194/amt-9-829-2016,https://doi.org/10.5194/amt-9-829-2016, 2016
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach
Anne-Claire Billault-Roux, Gionata Ghiggi, Louis Jaffeux, Audrey Martini, Nicolas Viltard, and Alexis Berne
Atmos. Meas. Tech., 16, 911–940, https://doi.org/10.5194/amt-16-911-2023,https://doi.org/10.5194/amt-16-911-2023, 2023
Short summary
High-resolution 3D winds derived from a modified WISSDOM synthesis scheme using multiple Doppler lidars and observations
Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, and GyuWon Lee
Atmos. Meas. Tech., 16, 845–869, https://doi.org/10.5194/amt-16-845-2023,https://doi.org/10.5194/amt-16-845-2023, 2023
Short summary
Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations
Simone Kotthaus, Juan Antonio Bravo-Aranda, Martine Collaud Coen, Juan Luis Guerrero-Rascado, Maria João Costa, Domenico Cimini, Ewan J. O'Connor, Maxime Hervo, Lucas Alados-Arboledas, María Jiménez-Portaz, Lucia Mona, Dominique Ruffieux, Anthony Illingworth, and Martial Haeffelin
Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023,https://doi.org/10.5194/amt-16-433-2023, 2023
Short summary
Assessing and mitigating the radar–radar interference in the German C-band weather radar network
Michael Frech, Cornelius Hald, Maximilian Schaper, Bertram Lange, and Benjamin Rohrdantz
Atmos. Meas. Tech., 16, 295–309, https://doi.org/10.5194/amt-16-295-2023,https://doi.org/10.5194/amt-16-295-2023, 2023
Short summary
Spectral replacement using machine learning methods for continuous mapping of the Geostationary Environment Monitoring Spectrometer (GEMS)
Yeeun Lee, Myoung-Hwan Ahn, Mina Kang, and Mijin Eo
Atmos. Meas. Tech., 16, 153–168, https://doi.org/10.5194/amt-16-153-2023,https://doi.org/10.5194/amt-16-153-2023, 2023
Short summary

Cited articles

Bust, G. S. and Mitchell, C. N.: History, current state, and future directions of ionospheric imaging, Rev. Geophys., 46, 1–23, 2008.
Carrano, C. S. and Groves, K.: The GPS Segment of the AFRL-SCINDA Global Network and the Challenges of Real-Time TEC Estimation in the Equatorial Ionosphere, Proceedings of the 2006 National Technical Meeting of The Institute of Navigation, Monterey, CA, 2006.
Coster, A., Williams, J., Weatherwax, A., Rideout, W., and Herne, D.: Accuracy of GPS total electron content: GPS receiver bias temperature dependence, Radio Sci., 48, 190–196, https://doi.org/10.1002/rds.20011, 2013.
Coster, A. J., Gaposchkin, E. M., and Thornton, L. E.: Real-time ionospheric monitoring system using the GPS, MIT Lincoln Laboratory, Technical Report, 954, 1992.
Davies, K.: Ionospheric Radio Propagation, National Bureau of Standards, 278–279, 1965.
Download
Short summary
We present a statistical framework for estimating GNSS receiver bias by using a weighted linear least squares of independent differences (WLLSID) model to examine differences of a large number of TEC measurements. This allows a consistent way for treating elevation-dependent model errors and spatiotemporal distance-dependent geophysical differences arising in ionospheric GNSS measurements. The method is also applicable to other GNSS system than GPS, supporting, e.g., GLONASS.