Bernese GPS Software Version 5.0 - Bernese GNSS Software

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12. Ionosphere Modeling and Estimation GIM-related information may be extracted from the GPSEST output files with ”Menu >Processing>Program output extraction>Parameter estimation/stacking”. Just enter a filename in “GIM summary”. Resulting summary files (default extension SUM are stored in the OUT directory. Figure 12.17 shows an example of an ionosphere file containing 12 2-hour global models. To join a series of global/regional models (type-2 models) stored in individual ionosphere files into a “multi-session” model, you may simply copy these files together in chronological order. The GIMs (corresponding coefficients are listed in Figure 12.17), are visualized in Figure 12.18. TEC snapshots taken at 00:00, 02:00, 04:00, ..., 22:00 UT are shown. Contour lines are given for every 10 TECU. The typical “bulge” (dark area), which may be bifurcated, is aligned to some extent with the Sun (s ≈ 0). The dotted line indicates the geomagnetic equator. Since January 1, 1996, the CODE analysis center is routinely producing Global Ionosphere Maps as an additional product. Apart from that, GIMs for the entire year 1995 have been computed in a re-processing step [Schaer et al., 1996]. The corresponding ION files starting with day 001 of 1995 are available via anonymous ftp (see also Chapter 4). Regional ionosphere models for Europe, routinely generated since December 1995, are available as well. Figure 12.19 shows the mean TEC that has been extracted from the GIMs produced by CODE [Schaer, 1998]. This parameter roughly describes the ionospheric activity on a global scale (compare also Figure 12.2). 12.4.3 Application of Deterministic TEC Models Deterministic TEC models may be used by two programs, namely the pre-processing program MAUPRP and the parameter estimation program GPSEST. The requested ionosphere file has to be specified in the option “Ionosphere models” in panel “MAUPRP 1: Input Files” and panel “GPSEST 1.1: Input Files 1”, respectively. Both programs will automatically detect whether local (type-1), global/regional (type-2), or station-specific (type-3) ionosphere models are introduced. In this context, we may mention that the program CODSPP only supports a very simple ionosphere model with “hard-wired” values for the day- and nighttime electron content which is therefore not really representative for actual ionospheric conditions. Where can deterministic ionosphere models help in GPS/GLONASS data processing? • In pre-processing, if large TEC gradients occur. Note, however, that short-term TEC variations are not reflected in the deterministic ionosphere models, i. e., strong scintillations will still harm pre-processing. • For ambiguity resolution, to make the ambiguity fixing more reliable by reducing the fractional parts of (L1, L2, or especially L5) ambiguities, if you do not use (precise) dual-band code measurements by analyzing the Melbourne-Wübbena linear combination, see Eqn. (2.50). • In parameter estimation steps, to reduce the ionosphere-induced scale bias in GNSS network solutions (see Table 12.1), if you process L1 and/or L2 observations – and not the ionosphere-free (L3) linear combination. Page 274 AIUB
12.5 Stochastic Ionosphere Modeling Technique 12.5.1 Estimation of Stochastic Ionosphere Parameters 12.5 Stochastic Ionosphere Modeling Technique Stochastic Ionosphere Parameters (SIPs), representing the term Ii k in Eqn. (12.14), may be set up in panel “GPSEST 5.1: Setup of Parameters and Pre-Elimination 1”. In the same panel a special parameter pre-elimination algorithm working epoch by epoch may be activated (select EACH EPOCH as pre-elimination method). This is recommended because of the huge number of SIPs usually involved. Panel “GPSEST 6.7: Stochastic Ionosphere Parameters” offers several options concerning SIPs (see Figure 12.20). “Elimination of reference ionosphere parameters” is the option where you decide whether to estimate SIPs on a double-difference or a quasi-single-difference level. The estimation on the quasi-single-difference level should be used when defining so-called relative a priori sigma at “Relative a priori sigma of ionospheric random walk”. If you eliminate reference ionosphere parameters, the resulting SIPs are estimated with respect to a reference satellite, actually the satellite closest to the zenith. The consideration of “Elevation-dependent parameter constraining” is recommended in particular when processing low-elevation data. An absolute a priori sigma must be specified in the field “Absolute a priori sigma on single difference level” to get “hybrid” dual-band observations. By entering 0.00, no SIP constraints are introduced. When using the General-Search ambiguity resolution strategy in conjunction with the stochastic ionosphere modeling, we recommend to specify an absolute a priori sigma between, let us say, 0.01 and 0.1 meters, and between 0.1 and 1 meters when using the Quasi- Ionosphere-Free (QIF) strategy (see also Figure 12.4). Relative a priori constraints between consecutive SIPs of the same satellite may be defined to model the correlation in time of the ionospheric signal. This option may be used only if you do not eliminate reference ionosphere parameters (option “Elimination of reference ionosphere parameters”). Such a “SIP smoothing” might be useful, e.g., for kinematic applications under moderate ionospheric conditions. Figure 12.20: Options for stochastic ionosphere parameters in GPSEST. Bernese GPS Software Version 5.0 Page 275
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Bernese GPS Software Version 5.0 Ed
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Bernese GPS Software Version 5.0 As
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Contents 2.3.6.1 Ionosphere-Free Li
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Contents 6.3.3 Kinematic Stations .
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Contents 9.4.4 Pre-Elimination Opti
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Contents 13.2 How to Correct P1-P2
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Contents 18.3.2 Command Bar . . . .
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Contents 20.4 Description of the Pr
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Contents 22.8.2 Station Abbreviatio
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Contents Page XVI AIUB
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List of Figures 5.1 Flow diagram of
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List of Figures 13.3 P1-C1 code bia
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List of Figures 22.20 Tabular orbit
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List of Tables 12.2 Influences of t
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1. Introduction and Overview • bu
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1. Introduction and Overview Table
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1. Introduction and Overview • Th
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1. Introduction and Overview The Be
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1. Introduction and Overview for Ca
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2. Fundamentals 2.1.1 GPS System De
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2. Fundamentals Table 2.2: Componen
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2. Fundamentals Clock correction [n
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2. Fundamentals Figure 2.5: GLONASS
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2. Fundamentals Table 2.6: GLONASS
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2. Fundamentals In contrast to the
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2. Fundamentals 2.2 GNSS Satellite
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2. Fundamentals 2.2.2.1 The Kepleri
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2. Fundamentals Table 2.9: Perturbi
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2. Fundamentals R.A. of Ascending N
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2. Fundamentals where aROCK is the
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2. Fundamentals By taking the deriv
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2. Fundamentals δi error of satell
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2. Fundamentals Ionospheric refract
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2. Fundamentals 2.3.6.1 Ionosphere-
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2. Fundamentals Table 2.10: Linear
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3. Campaign Setup variable to the f
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3. Campaign Setup Figure 3.1: Examp
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3. Campaign Setup In this section w
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3. Campaign Setup 3.5 Processing Ov
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4. Import and Export of External Fi
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5. Preparation of Earth Orientation
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6. Data Preprocessing Preprocessing
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6. Data Preprocessing (a) AS turned
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6. Data Preprocessing 6.2.4 Code Sm
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6. Data Preprocessing is set to 2.
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6. Data Preprocessing 6.3.2 Preproc
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6. Data Preprocessing RMS of kin. s
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6. Data Preprocessing The ambiguiti
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6. Data Preprocessing no loss of si
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6. Data Preprocessing This epoch-di
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6. Data Preprocessing result in lar
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6. Data Preprocessing (e.g., from C
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6. Data Preprocessing The second pa
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6. Data Preprocessing GAR small pie
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6. Data Preprocessing -------------
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6. Data Preprocessing automated pro
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6. Data Preprocessing 6.6.2 Generat
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6. Data Preprocessing 6.6.3 Detect
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6. Data Preprocessing Page 138 AIUB
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7. Parameter Estimation p u × 1 ve
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7. Parameter Estimation The two bas
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7. Parameter Estimation contains a
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7. Parameter Estimation The binary
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7. Parameter Estimation 7.5.2 Piece
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7. Parameter Estimation The equatio
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7. Parameter Estimation 7.5.4.4 Fix
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7. Parameter Estimation where Q 11
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7. Parameter Estimation consequence
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7. Parameter Estimation are address
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7. Parameter Estimation ... ! Pre-p
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7. Parameter Estimation The RINEX m
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7. Parameter Estimation In a succes
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7. Parameter Estimation Page 166 AI
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8. Initial Phase Ambiguities and Am
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9. Combination of Solutions 9.2 Seq
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9. Combination of Solutions Substit
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9. Combination of Solutions We may
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9. Combination of Solutions files a
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9. Combination of Solutions x x 1 t
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9. Combination of Solutions Both re
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9. Combination of Solutions 9.4 The
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9. Combination of Solutions 9.4.2 G
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9. Combination of Solutions Excepti
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9. Combination of Solutions As alre
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9. Combination of Solutions Table 9
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9. Combination of Solutions The poi
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9. Combination of Solutions (2) Tra
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9. Combination of Solutions Step 1:
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10. Station Coordinates and Velocit
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Page 338 and 339: 15. Estimation of Satellite Orbits
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15. Estimation of Satellite Orbits
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15. Estimation of Satellite Orbits
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16. Antenna Phase Center Offsets an
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17. Data Simulation Tool GPSSIM 17.
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17. Data Simulation Tool GPSSIM The
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17. Data Simulation Tool GPSSIM The
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17. Data Simulation Tool GPSSIM Res
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18. The Menu System 18.2 Starting t
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18. The Menu System 18.3.1 Menu Bar
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18. The Menu System 18.3.5 Panel Co
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18. The Menu System the menu the ne
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18. The Menu System Table 18.1: Pre
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18. The Menu System 18.5.3 System E
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18. The Menu System 18.7 Change Opt
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18. The Menu System 18.9 Technical
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18. The Menu System ... ! Environme
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18. The Menu System The keyword ENV
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18. The Menu System ! List of Remai
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18. The Menu System The MENUAUX-mec
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18. The Menu System Keyword values
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19. Bernese Processing Engine (BPE)
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20. Processing Examples inspect the
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20. Processing Examples 20.3.1 How
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20. Processing Examples • (option
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20. Processing Examples PID 111 PRE
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20. Processing Examples 20.4.1.5 Ta
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20. Processing Examples 20.4.2.1 Co
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20. Processing Examples appears in
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20. Processing Examples PID 313 MPR
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20. Processing Examples Further rea
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20. Processing Examples # # Create
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20. Processing Examples PID 101 GPS
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20. Processing Examples The RINEX n
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20. Processing Examples # # Preproc
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20. Processing Examples the loop, d
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20. Processing Examples PID 903 CLK
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20. Processing Examples and IGS 00B
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20. Processing Examples • Save th
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20. Processing Examples PID 321 GPS
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20. Processing Examples PID 301 CLK
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21. Program Structure $C / %C% PGM
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21. Program Structure (program GPSE
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21. Program Structure continued fro
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21. Program Structure continued fro
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21. Program Structure ! -----------
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22. Data Structure "Program" Area $
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22. Data Structure Table 22.1: List
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22. Data Structure 22.4.2 Geodetic
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22. Data Structure 22.4.4 Antenna P
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Page 484 AIUB ANTENNA PHASE CENTER
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22. Data Structure 22.4.5 Satellite
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22. Data Structure 22.4.6 Satellite
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22. Data Structure DIFFERENCE OF GP
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22. Data Structure 22.4.9 Subdaily
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22. Data Structure EGM96 EGM96, VER
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22. Data Structure SINEX : OPTION I
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22. Data Structure 22.4.16 Panel Up
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22. Data Structure 22.6.2 Header an
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22. Data Structure 15 Antenna type(
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22. Data Structure SVN-NUMBER= 2 ME
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22. Data Structure -1 2 1 5 TITLE :
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22. Data Structure Used by: ORBGEN
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22. Data Structure 53064.00 53065.0
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22. Data Structure CODE’S MONTHLY
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22. Data Structure Table 22.4: Stat
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22. Data Structure (3) TYPE 003: HA
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22. Data Structure 22.8.4 Station P
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22. Data Structure Remarks: • Eac
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22. Data Structure The following st
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22. Data Structure Table 22.7: List
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22. Data Structure AJAC $$ FES2004
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22. Data Structure Station sigma fi
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22. Data Structure GOPE 11502M002 Z
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22. Data Structure 22.8.18 Receiver
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22. Data Structure CODE RAPID 3-DAY
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22. Data Structure 22.9.3 Meteo and
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22. Data Structure IONOSPHERE MODEL
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22. Data Structure 22.10 Miscellane
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22. Data Structure 22.10.4 Variance
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22. Data Structure 22.10.5 Normal E
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22. Data Structure 22.10.7 Observat
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22. Data Structure Created by: Each
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22. Data Structure ----------------
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22. Data Structure 22.10.17 BPE log
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23. Installation Guide 23.1.2 Conte
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23. Installation Guide 23.1.3.2 Ins
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23. Installation Guide BERN50 : $C
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23. Installation Guide If you have
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23. Installation Guide To make the
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23. Installation Guide List of Inst
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23. Installation Guide 23.2.4 Confi
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23. Installation Guide Configure me
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23. Installation Guide ${P}/EXAMPLE
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23. Installation Guide To recompile
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23. Installation Guide Page 574 AIU
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24. The Step from Version 4.2 to Ve
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BIBLIOGRAPHY Bock, H. (2004), Effic
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BIBLIOGRAPHY Janes, H. W., R. B. La
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BIBLIOGRAPHY Schaer, S. (1998), COD
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BIBLIOGRAPHY Page 588 AIUB
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INDEX OF PROGRAMS NEQ2ASC, 207, 541
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Index of Program Panels CODSPP 2: I
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Index of Program Panels MKCLUS 3: R
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INDEX OF KEYWORDS Antenna verificat
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INDEX OF KEYWORDS Cluster definitio
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INDEX OF KEYWORDS variance-covarian
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INDEX OF KEYWORDS orbit products, 1
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INDEX OF KEYWORDS Multi-session sol
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INDEX OF KEYWORDS Orbital elements
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INDEX OF KEYWORDS Receiver antenna
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INDEX OF KEYWORDS orbit modeling, 9
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INDEX OF KEYWORDS WGS-84, 19, 88, 2
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