Constructing high-resolution, absolute maps of atmospheric water vapor by combining InSAR and GNSS observations

Transcription

1 Constructing high-resolution, absolute maps of atmospheric water vapor by combining InSAR and GNSS observations Fadwa Alshawaf, Stefan Hinz, Michael Mayer, Franz J. Meyer INSTITUTE OF PHOTOGRAMMETRY AND REMOTE SENSING KIT University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association

2 Atmospheric water vapor Weather and Climate: Most active greenhouse gas Key element in the hydrological cycle (In)SAR: (Interferometric) Synthetic Aperture Radar GNSS: Global Navigation Satellite Systems 2 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

3 Atmospheric water vapor Weather and Climate: Most active greenhouse gas Key element in the hydrological cycle Highly variable in time/space Available data are limited in temporal/spatial resolutions (In)SAR: (Interferometric) Synthetic Aperture Radar GNSS: Global Navigation Satellite Systems 3 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

4 Atmospheric water vapor Weather and Climate: Most active greenhouse gas Key element in the hydrological cycle Highly variable in time/space Available data are limited in temporal/spatial resolutions Noise Signal Geodesy and Remote Sensing: Source of error Methods for error mitigation Empirical models Calibration using external data Time series analysis (In)SAR: (Interferometric) Synthetic Aperture Radar GNSS: Global Navigation Satellite Systems 4 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

5 Objectives PS InSAR 2D fields of partial wet delay Data Combination 2D fields of total precipitable water vapor GNSS Pointweise total wet delay 5 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

6 Study area and data sets (In)SAR ( ) GNSS (since 2002) Meteorology MERIS (Reference) Upper Rhine 6 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

7 Atmospheric wet delay from PS InSAR 800 p Perpendicular Baseline [m] master Track 294 φ i, j = φ topography+ φdisplacement + φatmosphere + φ + φ + φ orbit ref noise Temporal Baseline [years] Poster Constructing high-resolution maps of atmospheric water vapor using InSAR 7 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

8 Atmospheric wet delay from PS InSAR p 800 Track 294 φi, j =φ topography +φ displacement + φ atmosphere Perpendicular Baseline [m] 400 φorbit + φ ref + φ noise master Temporal Baseline [years] Interferograms Persistent Scatterer InSAR (PSI) Series of temporal and spatial filtering Wet day difference maps 8 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

9 Atmospheric wet delay from PS InSAR Least squares inversion Maps of partial wet delay water vapor Very good agreement with MERIS observations PSI: Partial water vapor MERIS: Partial water vapor MEAN [mm] STD [mm] 0.86 RMS [mm] 0.87 Corr. Coeff April 23, F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

10 Atmospheric wet delay from PS InSAR Least squares inversion Maps of partial wet delay water vapor Very good agreement with MERIS observations PSI: Partial water vapor MERIS: Partial water vapor MEAN [mm] STD [mm] 0.86 RMS [mm] 0.87 Corr. Coeff April 23, Maps of high spatial resolution Partial measurements no absolute values 10 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

11 Atmospheric wet delay from PS InSAR Least squares inversion Maps of partial wet delay water vapor Very good agreement with MERIS observations PSI: Partial water vapor MERIS: Partial water vapor MEAN [mm] STD [mm] 0.86 RMS [mm] 0.87 Corr. Coeff April 23, 2007 How to reconstruct the total water vapor content? It is not only one offset A value has to be determined for each point 11 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

12 Data combination Total water vapor Partial water vapor Elevation-dependent water vapor Long wavelength water vapor 12 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

13 Data combination: Approach GNSS zenith total path delay Meteorological data P, T, RH GNSS absolute water vapor content Model elevationdependent signal L( z) = C exp( αz) + zαc exp( αz) + L min L(z) C, α, L z min Elevation-dependent water vapor Model parameters GNSS site altitude P: Pressure T: Temperature RH: Relative Humidity 13 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

14 Data combination: Approach GNSS zenith total path delay Meteorological data P, T, RH GNSS absolute water vapor content Model elevationdependent signal + - Model a 2D linear trend P: Pressure T: Temperature RH: Relative Humidity 14 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

15 Data combination: Application to the data Water vapor [mm] observed fitting Altitude [m] C α Lmin [mm] [km -1 ] [mm] F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

16 Data combination: Application to the data Water vapor [mm] observed fitting Altitude [m] C α Lmin [mm] [km -1 ] [mm] Compute a value at each persistent scatterer digital elevation model is required Iterative solution 16 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

17 Data combination: Approach GNSS total path delay Meteorological data P, T, RH GNSS absolute water vapor content Estimate elevationdependent parameters + - PSI maps of partial water vapor content Estimate 2D linear trend parameters + + Maps of absolute water vapor content + + Non-turbulent water vapor maps P: Pressure T: Temperature RH: Relative Humidity 17 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

18 Data combination: Results Water vapor: PSI+GNSS Water vapor: MERIS April 23, 2007 April 23, 2007 Difference MEAN [mm] STD [mm] 0.84 RMS [mm] 0.91 Corr. Coeff F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

19 Data combination: Results Water vapor: PSI+GNSS Water vapor: MERIS April 23, 2007 Day CC [%] RMS [mm] MEAN [mm] STD [mm] June 27, September 5, July 17, April 23, Alshawaf, F., S. Hinz, M. Mayer, F.J. Meyer (2015), Constructing accurate maps of atmospheric water vapor by combining interferometric synthetic aperture radar and GNSS observations, Journal of Geophysical Research: Atmospheres, 120 (4), pp F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

20 Conclusions and Outlook Correcting for atmospheric errors PS InSAR 2D fields of partial wet delay Data Combination 2D fields of total precipitable water vapor WRF 3D fields of total precipitable water vapor GNSS Pointweise total wet delay Data Fusion Correcting for atmospheric errors WRF: Weather Research and Forecasting 20 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015

21 Conclusions and Outlook Thank you very much for your attention 21 F. Alshawaf, Institute of Photogrammetry and Remote Sensing Water vapor mapping by combining InSAR & GNSS March 24, 2015