Preprints
https://doi.org/10.5194/angeo-2023-17
https://doi.org/10.5194/angeo-2023-17
12 Jun 2023
 | 12 Jun 2023
Status: a revised version of this preprint was accepted for the journal ANGEO and is expected to appear here in due course.

Estimation and evaluation of hourly MetOp satellites GPS DCBs with two different methods

Linlin Li and Shuanggen Jin

Abstract. Differential code bias (DCB) is one of the Global Positioning System (GPS) errors, which affects the calculation of total electron content (TEC) and ionospheric modeling. In the past, DCB was normally estimated as a constant in one day, while DCB of low Earth orbit (LEO) satellite GPS receiver may have large variations within one day due to complex space environments and highly dynamic orbit conditions. In this study, daily and hourly DCBs of Meteorological Operational (MetOp) satellites GPS receivers are calculated and evaluated using spherical harmonic function (SHF) and local spherical symmetry (LSS) assumption. The results demonstrated that both approaches could obtain accurate and consistent DCB values. The estimated daily DCB standard deviation (STD) is within 0.1 ns in accordance with the LSS assumption and is numerically less than the standard deviation of the reference value provided by the COSMIC Data Analysis and Archive Center (CDAAC). The average error's absolute value is within 0.2 ns with respect to the provided DCB reference value. As for the SHF method, the DCB's standard deviation is within 0.1 ns, which is also less than the standard deviation of the CDAAC reference value. The average error of the absolute value is within 0.2 ns. The estimated hourly DCB with LSS assumptions suggested that calculated results of MetOpA, MetOpB, and MetOpC are, respectively, 0.5 ns to 3.1 ns, -1.1 ns to 1.5 ns, and -1.3 ns to 0.7 ns. The root mean square error (RMSE) is less than 1.2 ns, and the STD is under 0.6 ns. According to the SHF method, the results of MetOpA, MetOpB, and MetOpC are 1 ns to 2.7 ns, - 1 ns to 1 ns, and - 1.3 ns to 0.6 ns, respectively. The RMSE is under 1.3 ns and STD is less than 0.5 ns. The STD for solar active days is less than 0.43 ns, 0.49 ns, and 0.44 ns, respectively, with the LSS assumption, and the appropriate fluctuation ranges are 2.0 ns, 2.2 ns, and 2.2 ns. The variation ranges for the SHF method are 1.5 ns, 1.2 ns, and 1.2 ns, respectively, while the STD is under 0.28 ns, 0.35 ns, and 0.29 ns.

Linlin Li and Shuanggen Jin

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on angeo-2023-17', Anonymous Referee #1, 08 Aug 2023
  • RC2: 'Comment on angeo-2023-17', Anonymous Referee #1, 08 Aug 2023
  • RC3: 'Comment on angeo-2023-17', Anonymous Referee #2, 18 Aug 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on angeo-2023-17', Anonymous Referee #1, 08 Aug 2023
  • RC2: 'Comment on angeo-2023-17', Anonymous Referee #1, 08 Aug 2023
  • RC3: 'Comment on angeo-2023-17', Anonymous Referee #2, 18 Aug 2023

Linlin Li and Shuanggen Jin

Linlin Li and Shuanggen Jin

Viewed

Total article views: 379 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
300 51 28 379 11 12
  • HTML: 300
  • PDF: 51
  • XML: 28
  • Total: 379
  • BibTeX: 11
  • EndNote: 12
Views and downloads (calculated since 12 Jun 2023)
Cumulative views and downloads (calculated since 12 Jun 2023)

Viewed (geographical distribution)

Total article views: 364 (including HTML, PDF, and XML) Thereof 364 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Oct 2023
Download
Short summary
In this study, the LSS assumption and SHF method are used to calculate the LEO satellite GPS DCBs. The SHF method is more stable and precise than the LSS assumption. The daily DCB estimation is more accurate and stable than the hourly DCB due to the more amount of data. Hourly DCBs have changes in one day, but these can mainly be attributed to random errors because these error time series conform to a normal distribution.