Influences of the day‐night differences of ionospheric variability on the estimation of GPS differential code bias

The estimation of differential code bias (DCB) of GPS system is one of the necessary steps for total electron content (TEC) derivation from GPS measurements. Usually, the method for estimating the GPS DCBs follows the assumption of the gentle temporal and spatial variation of the ionosphere, but this assumption is just an approximation because of the ionosphere's inherent variability. It has been indicated that the estimated GPS satellite DCBs are sometimes influenced by the ionospheric conditions. In this paper, we demonstrate a possible influence of ionospheric variability that differs between day and night on the estimated DCBs from measurements of a single GPS station. It is found that the average standard deviations (STDs) of the satellite DCBs estimated with daytime data are higher than that with the nighttime data. To reduce this day-night difference effect on GPS DCB determination, we use an improved estimation method based on the primary features of the ionospheric variability with local time. A local time dependent weighting function was introduced into the original least squares DCBs estimation algorithm. A test with data for BJFS station (39.60°N, 115.89°E) in 2001 indicates that the STD of the DCBs decreases from 2.533 TECU (total electron content unit, 1 TECU = 1016 el m−2) to 2.308 TECU, or by 8.9%, after the improved method was applied. For comparison, another test for the same station in 2009 indicates that the STD decreases from 1.344 TECU to 1.295 TECU. The amplitude of the 2009 improvement is very limited, only about 3.6%. The difference of the percentage improvements can probably be attributed to the different ionospheric conditions between 2001 and 2009.

[1]  Allan T. Weatherwax,et al.  Accuracy of GPS total electron content: GPS receiver bias temperature dependence , 2013 .

[2]  Takashi Maruyama,et al.  Three‐dimensional ionospheric tomography using observation data of GPS ground receivers and ionosonde by neural network , 2005 .

[3]  Zuo Xiao,et al.  Accuracy analysis of the GPS instrumental bias estimated from observations in middle and low latitudes , 2010 .

[4]  Wei Zhang,et al.  The influence of geomagnetic storms on the estimation of GPS instrumental biases , 2009 .

[5]  A. Garcia-Rigo,et al.  The IGS VTEC maps: a reliable source of ionospheric information since 1998 , 2009 .

[6]  Gabor E. Lanyi,et al.  A comparison of mapped and measured total ionospheric electron content using global positioning system and beacon satellite observations , 1988 .

[7]  E. Sardón,et al.  Estimation of total electron content using GPS data: How stable are the differential satellite and receiver instrumental biases? , 1997 .

[8]  A. Coster,et al.  East‐West Coast differences in total electron content over the continental US , 2011 .

[9]  Gregory Bishop,et al.  The effect of the protonosphere on the estimation of GPS total electron content: Validation using model simulations , 1999 .

[10]  Takuya Tsugawa,et al.  A new technique for mapping of total electron content using GPS network in Japan , 2002 .

[11]  Henry Rishbeth,et al.  Patterns of F2-layer variability , 2001 .

[12]  Wei Zhang,et al.  The variation of the estimated GPS instrumental bias and its possible connection with ionospheric variability , 2014 .

[13]  Min Wang,et al.  East‐west differences in F‐region electron density at midlatitude: Evidence from the Far East region , 2013 .

[14]  Anthea J. Coster,et al.  Real‐Time Ionospheric Monitoring System Using GPS , 1991 .

[15]  K. Davies,et al.  Studying the ionosphere with the Global Positioning System , 1997 .

[16]  Peter Daly,et al.  Analysis of the Temporal Stability of GPS and GLONASS Group Delay Correction Terms Seen in Various Sets of Ionospheric Delay Data , 1994 .

[17]  A. Rius,et al.  Estimation of the transmitter and receiver differential biases and the ionospheric total electron content from Global Positioning System observations , 1994 .

[18]  Gregory Bishop,et al.  Autonomous estimation of plasmasphere content using GPS measurements , 2002 .

[19]  Guanyi Ma,et al.  Derivation of TEC and estimation of instrumental biases from GEONET in Japan , 2002 .

[20]  Sandro M. Radicella,et al.  Calibration errors on experimental slant total electron content (TEC) determined with GPS , 2007 .

[21]  Attila Komjathy,et al.  Kalman filter-based algorithms for monitoring the ionosphere and plasmasphere with GPS in near-real time , 2009 .

[22]  Orhan Arikan,et al.  Estimation of single station interfrequency receiver bias using GPS‐TEC , 2008 .

[23]  P. V. S. Rama Rao,et al.  On the validity of the ionospheric pierce point (IPP) altitude of 350 km in the Indian equatorial and low-latitude sector , 2006 .

[24]  Anthony J. Mannucci,et al.  A global mapping technique for GPS‐derived ionospheric total electron content measurements , 1998 .

[25]  Li Qiang,et al.  Methods of Estimation of GPS Instrumental Bias from Single Site's GPS Data and Comparative Study of Results , 2007 .

[26]  Aleksandar Jovancevic,et al.  Ionospheric measurement with GPS: Receiver techniques and methods , 2007 .

[27]  Larry J. Paxton,et al.  Control of equatorial ionospheric morphology by atmospheric tides , 2006 .

[28]  Guo Jia Simulation study of effective ionospheric shell height based on Global Core Plasma Model , 2014 .

[29]  James R. Clynch,et al.  Variability of GPS satellite differential group delay biases , 1991 .

[30]  Attila Komjathy,et al.  Global ionospheric total electron content mapping using the global positioning system , 1997 .

[31]  Keith M. Groves,et al.  Kalman filter estimation of plasmaspheric total electron content using GPS , 2009 .