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Impact of Tropospheric Mapping Functions and other Analysis Options on the

Impact of Tropospheric Mapping Functions and other Analysis Options on the TRF, CRF and Position Time Series Estimated from VLBI. 1 Volker Tesmer, 2 Johannes Boehm, 2 Robert Heinkelmann, 2 Harald Schuh. 1 Deutsches Geodaetisches Forschungsinstitut (DGFI)

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Impact of Tropospheric Mapping Functions and other Analysis Options on the

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  1. Impact of Tropospheric Mapping Functions and other Analysis Options on the TRF, CRF and Position Time Series Estimated from VLBI 1Volker Tesmer, 2Johannes Boehm, 2Robert Heinkelmann, 2Harald Schuh 1Deutsches Geodaetisches Forschungsinstitut (DGFI) 2Institute of Geodesy and Geophysics (IGG), TU Vienna • - Motivation and solution setup • - Testing the influence of • * tropospheric mapping functions (MAPFUN) • * tropospheric parameterisation (TROPAR) • * other influences (OTHER) • on estimated CRF, TRF + station position time series • Summary and conclusions

  2. Motivation and solution setup

  3. Motivation Geophysical applications of modern geodesy require - physically interpretable results - highest precision Maximum interpretability can only be achieved if all data is - homogeneously (re-)processed - free of not modeled technique dependent effects => Necessity to understand - systematic influence of solution approaches on TRF + CRF - annual signals in station positions - episodic behaviour of station positions and to make - solutions as precise as possible

  4. Solution setup (I) Technical attributes: -VLBI software OCCAM 6.1 (LSM) - 2699 daily sessions between 1984 and 2005 - 49 telescopes observing 1954 sources - normal equations set up per session, accumulated to one large equation system with DOGS-CS - NNT and NNR of 25 telescopes w.r.t. ITRF2000 - NNR of 199 stable sources (Feissel, A&A, 2003) w.r.t. ICRF-Ext1 100% consistent TRF, CRF and EOP =>

  5. { { { - GMF - VMF1 - IMF - temporal ZD-rate resolution 1h -> 2h - a-priori constant, trop. gradients (GSFC, DAO 90-95) - a-priori constant ZD, not using surface met data - atmospheric loading (Petrov & Boy, JGR 2004) - thermal deformation (IVS An. Coord. homepage) - refined stoch. model (Tesmer & Kutterer, GM 2004) Solution approaches testing systematic effects and precision criteria: -MAPFUN: impact of tropospheric mapping functions NMF vs. -TROPAR: impact of tropospheric parameterisation usual solution vs. -OTHER:other influences solutions with vs. w/o Solution setup (II)

  6. station position time series 1 (NNR+NNT per session w.r.t. TRF1) solution approach 1 CRF1 TRF1 => + station position time series 2 (NNR+NNT per session w.r.t. TRF2) solution approach 2 CRF2 TRF2 => + … solution approach … ... => + Solution setup (III) Types of parameters computed: 100 % consistent TRF, CRF, EOP and homogeneous+undeformed position time series

  7. => can be used as indicators for: of the different solution approaches => help understanding inconsistencies between series interpretability precision Solution setup (IV) Compared parameters:

  8. Impact of tropospheric mapping functions (MAPFUN)

  9. MAPFUN Solution approaches MAPFUN: impact of tropospheric mapping functions - “NMF” (Niell, JGR, 1996) * based on parameters PHI, h and doy * determined from one year of radiosonde data (-60° to 65°) Compared with: - “IMF” (Niell, PCE, 2001) * based on one pressure level and on water vapor distribution * determined from NWM each 6 hours - “VM1”: VMF1 (Boehm et al., JGR, 2006) * based on exact ray trace at 3° * determined from NMW each 6 hours - “GMF” (Boehm et al. submitted to GRL, 2006) * based on PHI, LAM, h and doy * determined from VMF1 using spherical harmonics (“averaged”)

  10. NMF-GMF NMF-VM1 NMF-IMF MAPFUN CRF CRF differences: solution approach NMF vs. GMF, VM1 and IMF IMF: small systematic effect in dDE

  11. GMF, VM1, IMF: -2 mm vs. NMF GMF, VM1, IMF: -6 mm vs. NMF GMF, VM1, IMF: -3 mm vs. NMF GMF, VM1, IMF: -4 mm vs. NMF GMF, VM1, IMF: -15 mm vs. NMF NMF-GMF NMF-VM1 NMF-IMF MAPFUN TRFheight (I) TRF height differences: solution approach NMF vs. GMF, VM1 and IMF

  12. nyal south tsuk - - IMF, GMF & VM1 heights: * bigger scale than NMF * same local effects * signature at ~40 deg latitude GMF & VM1 resemble more than IMF NMF-GMF NMF-VM1 NMF-IMF NMF-IMF NMF-GMF NMF-VM1 MAPFUN TRFheight per station + scale (II) solution approach NMF vs. GMF, VM1 and IMF

  13. MAPFUN Single annual signalsheight (I) - height amplitudes < 5mm, max. 8 mm - horizontal amplitudes < 3 mm, max. 5 mm - generally agree, BUT: some very clear exceptions

  14. NMF-IMF NMF-GMF NMF-VM1 MAPFUN Annual signalsper station (II) Height: diff. < 1mm & 2m, less amplitude 30-50° Horizontal: diff. < 0.3mm & 1m, almost no systematics

  15. dPHI [mm] dLAM [mm] dR [mm] NMF-IMF NMF-GMF NMF-VM1 MAPFUN Station position time seriesGILCREEK (I) Height: 5 mm annual common for IMF, VM1 & GMF => annual fraction in NMF erroneous

  16. dPHI [mm] dLAM [mm] dR [mm] NMF-IMF NMF-GMF NMF-VM1 MAPFUN Station position time seriesTSUKUB32 (II) Height: 8 mm annual common for IMF, VM1 & GMF => annual fraction in NMF very erroneous

  17. dPHI [mm] dLAM [mm] dR [mm] NMF-IMF NMF-GMF NMF-VM1 MAPFUN Station position time seriesNYALES20 (III) Height: +- 5 mm same episodic signals for IMF & VM1 => necessity of MF from 6h NWM

  18. dPHI [mm] dLAM [mm] dR [mm] NMF-IMF NMF-GMF NMF-VM1 MAPFUN Station position time seriesKOKEE (IV) Latitude: IMF & VM1 with semi-annual => GMF to be extended by semi-annual fraction

  19. RMS wrt NMF [%] RMS wrt NMF [%] RMS wrt NMF [%] WRMS wrt NMF [%] WRMS wrt NMF [%] WRMS wrt NMF [%] “NMF-GMF” “NMF-VM1” “NMF-IMF” MAPFUN Repeatabilities (I) RMS (all stations) annuals removed WRMS (all stations) annuals removed IMF small, VM1 good improvement (especially @30-50° lat, in some cases up to 15%)

  20. Impact of tropospheric parameterisation (TROPAR)

  21. TROPAR Solution approaches TROPAR: impact of tropospheric parameterisation - “usual” standard solution approach COMPARED TO: - “2hour” changes w.r.t. “usual”: * ZD-rate estimated each 2 h instead of 1h - “aprgr” changes w.r.t. “usual”: * a-priori constant trop. gradients (GSFC, DAO 90-95) instead of zero values - “zdcon” changes w.r.t. “usual”: * a-priori constant ZD (generally @GPS) instead of surface met data

  22. usual-2hour usual-aprgr usual-zdcon TROPAR CRF CRF differences: solution approach usual vs. 2hour, aprgr and zdcon aprgr & zdcon: small systematic effect in dDE

  23. zdcon: wrong apriori hydrostatic ZD can have big influence on estimated heights (here up to 38 mm) usual-2hour usual-aprgr usual-zdcon TROPAR TRFheight per station + scale (II) solution approach usual vs. 2hour, aprgr and zdcon

  24. aprgr: -2 mm effect in the South usual-2hour usual-aprgr usual-zdcon TROPAR TRFhorizontal per station (III) TRF horizontal differences: solution approach usual vs. 2hour, aprgr and zdcon

  25. usual-2hour usual-aprgr usual-zdcon dPHI [mm] dLAM [mm] dR [mm] TROPAR Station position time seriesNYALES20 (I) zdcon: 4 mm episodic (annual, semi-annual) in height

  26. usual-2hour usual-aprgr usual-zdcon dPHI [mm] dLAM [mm] dR [mm] TROPAR Station position time seriesWETTZELL (II) zdcon: shift in height 1mm + horiz. 0.5mm before 1990 => jump in surface pressure data

  27. RMS wrt usual [%] RMS wrt usual [%] RMS wrt usual [%] WRMS wrt usual [%] WRMS wrt usual [%] WRMS wrt usual [%] “usual-2hour” “usual-aprgr” “usual-zdcon” TROPAR Repeatabilities RMS (all stations) annuals removed WRMS (all stations) annuals removed 2hour: 2-3% loss zdcon: 3% height WRMS gain => errors in surface met ?

  28. Impact of other influences (OTHER)

  29. OTHER Solution approaches OTHER: impact of other influences - “usual” standard solution approach COMPARED TO: - “atmos” changes w.r.t. “usual”: * using atmospheric loading (Petrov & Boy, JGR, 2004) - “therm” changes w.r.t. “usual”: * using thermal deformation (IVS Analysis Coordinator homepage), reference temperatures: avg. of met surface temperatures of sessions in use - “refst” changes w.r.t. “usual”: * using refined stochastic model (Tesmer & Kutterer, GM 2004), mainly: elevation- and station dependent re-weighting

  30. usual-therm usual-atmos usual-refst OTHER CRF CRF differences: solution approach usual vs. therm, atmos and refst refst: systematic effect in dDE

  31. usual-therm usual-atmos usual-refst OTHER Annual signalsper station (II) Exceptions (S-N): Hartrao, Kokee, Wettzell, Gilcreek Height: up to 1 mm, therm & atmos: most stations smaller Horizontal: up to 0.5 mm, therm: almost all stations smaller

  32. usual-therm usual-atmos usual-refst dPHI [mm] dLAM [mm] dR [mm] OTHER Station position time seriesGILCREEK (I) Atmos: annual in height +- 5 mm

  33. usual-therm usual-atmos usual-refst dPHI [mm] dLAM [mm] dR [mm] OTHER Station position time seriesALGOPARK (II) Therm: annual in height +- 3 mm

  34. RMS wrt usual [%] RMS wrt usual [%] RMS wrt usual [%] WRMS wrt usual [%] WRMS wrt usual [%] WRMS wrt usual [%] “usual-therm” “usual-atmos” “usual-refst” OTHER Repeatabilities (I) RMS (all stations) annuals removed WRMS (all stations) annuals removed Therm: seems to be mainly annual -> not be seen here Atmos: gain horizontal 1-2 %, height 2-4 % (up to 10%) Refst: gain horizontal/height 3-4 % (up to 15%)

  35. Summary and conclusions

  36. Summary & Conclusions (I) Mapping functions: - IMF, GMF & VMF1 w.r.t. NMF * similar local diff. in height (up to 15 mm) * comparable diff. in annual signals (generally smaller, esp. @ 30-50° lat.) - IMF & VMF1 w.r.t. NMF & GMF * similar episodic position signals - VMF1 * best repeatability of height & horizontal station positions Proposal: use VMF1 (“best approach”) instead of … BUT: systematic, annual & episodic effects => for GGOS & IERS, consistency is essential

  37. Summary & Conclusions (II) Tropospheric parameterisation: - aprgr (a priori constant gradients) * should give more realistic results * BUT: small systematics in TRF and CRF - zdcon (constant apriori ZD not from surface met data) * slightly improves height position repeatability (bad surface met data ?) * BUT: strong influence on height (here max. 38 mm) - 2hour (ZD-rate estimated each 2 h instead of 1h) * … * BUT: 2-3 % loss of station position repeatability Proposal: use “best approach” BUT: systematic & episodic effects => for GGOS & IERS, …

  38. Summary & Conclusions (III) Other Influences: - therm (thermal deformation) * reduces annual amp. in height (up to 0.7 mm) and horiz. (up to 0.4 mm) * BUT: definition of reference temperature missing - atmos (atmospheric loading) * generally reduces ampl. of annual signals in height (up to 0.7 mm) * improves station height repeatability (2% RMS, 4% RMS; max 5-10%) * BUT: definition of reference pressure missing - refst (refined stochastic model, mainly elev. dep. re-weighting) * improves station position repeatability (height: 3% RMS, 5% WRMS; max 10-15%) * BUT: small systematics in TRF and CRF Proposal: use “best approach” BUT: systematic, annual & episodic effects => for GGOS & IERS, …

  39. Outlook Check influence of solution approaches on: • - tropospheric ZD (offsets, rate, annual + episodic fractions) • - tropospheric horizontal gradients (…) • - time evolution of scale (…) • - long EOP series (…) • - similarity of 67 pairs of EOPNEOS-A / CORE-A • - repeatability of source positions

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