# Presentation: 125084

## GPS in the ITRF Combination

**GPS in the ITRF Combination**

- D. Angermann, H. Drewes, M. Krügel, B. Meisel

- Deutsches Geodätisches Forschungsinstitut, München
- E-Mail: angermann@dgfi.badw.de

## Outline

- ITRS Combination Center at DGFI
- Input data for TRF computations
- Analysis and accumulation of GPS time series
- GPS in the inter-technique combination
- Contribution of GPS to the datum realization
- Conclusions and outlook

## ITRS Combination Center at DGFI

-     General concept: Combination on the normal equation level
-     Software: DGFI Orbit and Geodetic Parameter Estimation
- 	         Software (DOGS)

- Geodetic
- datum

## Input data sets for TRF computations (1/2)

| Techn. | Service / AC | Data | Time Period |
| --- | --- | --- | --- |
| GPS | IGS / NRCan | weekly solutions | 1996 - 2005 |
| VLBI | IVS / IGG | 24 h session NEQ | 1984 - 2005 |
| SLR | ILRS / ASI | weekly solutions | 1993 - 2005 |
| DORIS | IGN - JPL/LCA  | weekly solutions | 1993 - 2005 |

**ITRF2005**: Time series of station positions and EOP

- ITRF2005 data sets are not fully consistent, the standards and
- models were not completely unified among analysis centers
- Shortcomings concerning GPS:
-   IGS solutions are not reprocessed (e.g., model and software changes)
-   Relative antenna phase center corrections were applied

## Input data sets for TRF computations (2/2)

| Techn. | Institutions | Data  | Time Period |
| --- | --- | --- | --- |
| GPS | GFZ | daily NEQ | 1994 - 2007 |
| VLBI | IGG / DGFI | 24 h session NEQ | 1984 - 2007 |
| SLR | DGFI / GFZ | weekly NEQ | 1993 - 2007 |

**GGOS-D**: Time series of station positions and EOP

- Improvements of GGOS-D data compared to ITRF2005:
-   Homogeneously processed data sets
-     -  Identical standards, conventions, models, parameters
-     -  GPS: PDR (Steigenberger et al. 2006, Rülke et al. 2008)
-   Improved modelling
-     -  for GPS: **absolute** instead of relative **phase centre** corr.
-     -  for VLBI: **pole tide model** was changed

- GGOS-D: German project of BKG, DGFI, GFZ and IGG funded by BMBF

## TRF computation strategy

**First step: **Analysis of station coordinate time series and computation of a reference frame per technique
- Modelling time dependent station coordinates by
-  epoch positions
-  linear velocities
- - (seasonal signals)
- - discontinuities

**Second step: **Combination of different techniques by
- - relative weighting
- - selection of terrestrial difference vectors (local ties)
- - combination of station velocities and EOP
- - realization of the geodetic datum

## TRF per technique (1/4)

-   Instrumentation changes              Earthquakes                    Seasonal variations

- Analysis of GPS station position time series

- ITRF2005. 221 discontinuities in 332 GPS stations (1996 - 2005)
- GGOS-D:    95 discontinuities in 240 GPS stations (1994 - 2007)

## TRF per technique (2/4)

- Effect of annual signals ?

- Equating of station velocities ?

| Solution
ID | Velocities
[mm/yr] |
| --- | --- |
| Sol. ID 1
Sol. ID 2 | 4.5 ± 0.9
8.7 ± 0.7 |
| Δ (2 – 1) | 4.2 ± 1.2 |

- Sol. ID 1

- Sol. ID 2

| obs time span  
[yrs] | 0.5 | 1.0 | 2.0 | 3.0 |
| --- | --- | --- | --- | --- |
|  velocities 
[mm/yr] | -37.5
 3.2 | -3.7
 2.3 | 2.7
 0.8 | -0.3
 0.5 |

- Velocity differences w.r.t. a linear model

**GPS station Irkutsk (Siberia)**

**GPS station Hofn (Iceland)**

**1997                            2000                          2003                            2006**

**1997                            2000                          2003                            2006**

## TRF per technique (3/4)

**Seasonal signals - Comparison with geophysical data**

- Models consider
**atmospheric,**
**oceanic** and
**hydrologic**
- mass loads:
- NCEP, ECCO,
- GLDAS

- Potsdam

- Krasnoyarsk

- Bahrain

**Correlation coefficient = 0,50**

**Correlation coefficient = 0,79**

**Correlation coefficient = 0,73**

- 1997	       1999              2 001               2003               2005

- [cm]

-  2
-  0
- -2

-  2
-  0
- -2

-  2
-  0
- -2

## TRF per technique (4/4)

**Shape of seasonal signals **can be approximated by sine/cosine annual and semi-annual functions

- Brasilia

- Ankara

- Estimation of annual signals in addition to velocities ?

**Advantages:**
-  Improved velocity estimation
-  Better alignment of epoch solutions

**Disadvantages / open questions:**
-  More parameters (stability) ?
-  Seasonal signal geophysically meaningful ?
-  How to parameterize seasonal signals ?

## Computation of the TRF (1/3)

- Selection of local ties at co-location sites

- SLR-VLBI (9)

- SLR-GPS (25)

- VLBI-GPS (27)

## Computation of the TRF (2/3)

- ITRF2005
- GGOS-D

**Selection of terrestrial difference vectors (1)**
- Three-dimensional differences between space geodetic solutions (GPS and VLBI) and terrestrial difference vectors [mm]

- = stations in southern hemisphere

- Krügel et al. 2007: Poster presented at AGU Fall Meeting 2007

## Computation of the TRF (3/3)

**Selection of terrestrial difference vectors (2)**

- Mean pole difference

- Network deformation

- Mean pole difference:
- 35 as (≈1 mm)
- Network deformation:
- ≤ 0.3 mm
- Number of
- co-locations: 19
- _ITRF2005:_
- Pole difference:    41 as
- Deformation:      1.0 mm
- No. co-locations:  13

## Realization of the geodetic datum (1/4)

|  | ITRF2005D
(DGFI Solution) | GGOS-D |
| --- | --- | --- |
| Origin | SLR | SLR |
| Scale | SLR + VLBI | SLR + VLBI + GPS  |
| Orientation | NNR conditions
w.r.t. ITRF2000 | NNR conditions
w.r.t. ITRF2005 |
| Orientation
time evolution | NNR conditions w.r.t. horizontal motions by using the Actual Plate Kinematic and
Deformation Model (APKIM) |  |

## Realization of the geodetic datum (2/4)

- Station velocity residuals for 56 core stations used to realize
- the kinematic datum of the ITRF2005D solution w.r.t. APKIM

## Realization of the geodetic datum (3/4)

- Translation and scale estimates of similarity transformations between
- combined PDR05 and weekly solutions (Rülke et al., JGR 2008)

## Realization of the geodetic datum (4/4)

- Cdatum = (GT CGPS-1 G)-1

- Method:

- CGPS: 	Covariance matrix of GPS solution (loose constrained)
- G:	Coefficients of 7 parameter similarity transformation matrix
- Cdatum:	Covariance matrix of datum parameters

- Datum information of GPS observations (compared to SLR)

- Standard deviations for datum parameters [mm]

|  | tx | ty | tz | scale |
| --- | --- | --- | --- | --- |
| GPS | 0.7 | 0.7 | 1.1 | 0.2 |
| SLR | 1.0 | 0.9 | 2.7 | 2.9 |

## Conclusions and outlook

**Discontinuities**: Number of jumps reduced due to homogeneous re- processing; discontinuity tables among techniques should be adjusted.
**Annual signals**: Treatment of seasonal variations in station positions (e.g., by estimating sine/cosine functions) should be investigated.
**Co-locations**: Discontinuities are critical; at least two GPS instruments should be operated at each co-location site.
**Geodetic datum**: GPS reprocessing provides stable results for the scale and for the x- and y-component of the origin, the z-component shows large seasonal variations which should be investigated.
**GPS reprocessing** is essential for the next ITRF (unified standards and models should be applied for different techniques).