1. EMS Annual Meeting 2017 September 4-8, 2017 Dublin, Ireland
A comparison of Integrated Water Vapor (IWV) from meteorology and space geodetic techniques
Daniel Landskron, Angelika Xaver, Johannes Böhm, Wouter Dorigo
Technische Universität Wien, Austria

2. Data Sources Numerical Weather Models (NWM)
ECMWF operational data  Zenith Wet Delay (globally)
Very Long Baseline Interferometry (VLBI)
Analyzed with Vienna VLBI and Satellite Software (VieVS)  Zenith Wet Delay (at VLBI stations)
Global Navigation Satellite System (GNSS)
IGS repro1 data  Zenith Wet Delay (at GNSS stations)
GlobVapour
Remote Sensing project exploiting data from several EO missions (MERIS, GOME, SSM,..)  Integrated Water Vapor (globally)
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

3. Data Sources
NWM: (6-hourly, continuously) VLBI: (6-hourly, discretely) GNSS: (6-hourly, continuously) GlobVapour: (weekly, discretely) 18 VLBI/GNSS stations
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

4. Very Long Baseline Interferometry
Plane wavefronts because of huge distance (~10 billion ly)
Determine phase difference τ between 2 sites
Correct for errors (ionosphere, troposphere,..)
 Station positions and velocities, and also water vapor
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

5. Very Long Baseline Interferometry
Use of VLBI for determination of water vapor:
Long time series (starting ~1984)
More or less continuous data
No offsets through antenna changes
Only little data preparation (outliers)
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

6. Conversion ZWD IWV Zenith Wet Delay (ZWD)
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

7. Seasonal Fit Seasonal fit through least-squares adjustment (LSM):
A mean value
A1, B annual amplitudes
A2, B semi-annual amplitudes
k trend
mjd modified Julian date
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

8. Results (1) Comparison of IWV for station MATERA
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

9. Results (1) Comparison of IWV for station HARTRAO
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

10. Correlation Coefficients | Bias | Standard deviation
Results (1)
Correlation Coefficients | Bias | Standard deviation
between techniques
Corr. Coeff.
Bias (kg/m2)
St. Dev. (kg/m2)
NWM / VLBI
0.87
0.2
3.9
NWM / GNSS
0.91
0.3
3.5
NWM / GlobVapour
0.67
0.7
6.9
VLBI / GNSS
0.97
-0.4
1.9
VLBI / GlobVapour -
GNSS / GlobVapour
0.62
7.6
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

11. (= increase/decrease of water vapor per year)
Results (2)
Trend of data
(= increase/decrease of water vapor per year)
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

12. IWV from VLBI IWV from VLBI for station WETTZELL
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

13. Conclusions Good correlation between techniques No significant biases
VLBI good supplement for determining water vapor
Drawbacks:
No safe statements about long-term changes possible
Longer time period and more stations needed
More continuous data needed
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques

14. Outlook In future, there will be… …more remote sensing missions
…more VLBI antennas
…more GNSS antennas
…denser and more accurate NWM
…naturally longer time series
 More reliable/meaningful results!
2017/09/ A comparison of integrated water vapor (IWV) from meteorology and space geodetic techniques