APPROACHES TO MITIGATE ATMOSPHERE ARTEFACTS IN SAR INTERFEROGRAMS: GPS VS. WRF MODEL

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1 APPROACHES TO MITIGATE ATMOSPHERE ARTEFACTS IN SAR INTERFEROGRAMS: GPS VS. WRF MODEL P. Mateus (1), G. Nico (1), R. Tomé (1), J. Catalão (1) and P. Miranda (1) (1) University of Lisbon, Faculty of Sciences, Portugal, ABSTRACT In this work we present the first results of an experiment to measure and model atmospheric delays by means of GPS, Synthetic Aperture Radar Interferometry (InSAR) and the Weather Research and Forecasting (WRF) model. The aim of the experiment is to device strategies to mitigate atmospheric phase delay artefacts in SAR interferograms. 1. INTRODUCTION In the last decades many geophysical and geodetic applications based on space-borne Synthetic Aperture Radar Interferometry (InSAR) were presented [1]. Even if SAR systems work at frequencies minimizing the atmospheric absorption, the interferometric phase is affected by a delay mainly due to the propagation in the tropospheric layer [2][3]. An increase in the amount of atmospheric water vapour fraction between the acquisition times affects the atmosphere index of refraction which induces an increase in the propagation time of microwave radar pulses. All this appears as an increase of interferometric phase and so an apparent increase in the distance to the ground surface, indistinguishable from topography or real ground deformation. Approaches to mitigate atmospheric artefacts in InSAR data, which could be applied on a scene-to-scene basis, would be highly desirable. Approaches such as stacking SAR interferograms and calibration with external data sources have been suggested to mitigate the effects of variable water vapour-induced phase delays [4]-[9]. In this work we present the first results of an experiment where the atmospheric delay measured by means of GPS and SAR interferometry is compared to the delay computed by means of a numerical meteorological model. The Lisbon region, Portugal, was chosen as a study area. This region is monitored by a network of GPS permanent stations covering an area of about squared kilometers. A set of 25 SAR interferograms with a 35-day temporal baseline was processed using ASAR-ENVISAT data acquired over the Lisbon region from 2003 to 2005 and from 2008 to Terrain deformations related to known geological phenomena in the Lisbon area are negligible at the time scale of 35 days. The Weather Research & Forecasting Model (WRF) [10] was used to generate the three-dimensional fields of temperature, atmospheric pressure, water vapour fraction, geopotential and precipitable liquid water at a given time. They were used to compute the hydrostatic and wet components of the atmospheric delay. Finally, both components were compared to GPS time series and SAR interferograms. 2. ES TIMATIONS OF TOTAL ZENITH DELAY FROM GPS The GPS network consists of 12 stations of which 4 belonging to the Instituto Geográfico Português (IGP), namely, IGP0, PALM, GRIB and CASC and 7 belonging to Instituto Geográfico do Exército (IGEOE), namely, CRAI, SMAR, VNOV, ARRA, PARC, MAFR and ALCO. The site FCUL (Lisbon University) has a further GPS station. The CASC station is also a EUREF station. The location of the 12 GPS stations is shown in Fig. 1. All stations were installed between 1997 and the beginning of In this study, the GAMIT (v10.34) software was used to process the GPS data and estimate the atmosphere Total Zenith Delays (TZDs) at the location of stations. We used the Saastamoinen model to estimate a priori values, and Vienna Mapping function 1 (VMF1) in both components [11]. The wet zenith delay for each station was modeled by a piecewise-linear function over the span of the observations. The IGS precise orbits were used in the solution and the cut-off angle chosen for the GPS data was 20 [3]. For each site the atmospheric delays were determined every 15 min, resulting in 96 parameters per site throughout the 24-h observation span. Rothacher and Mervart [12] recommend one estimation about 2-4 h in 24-h observation span for geodetic control surveys. However, the procedure suggested in [12] does not allow to extract all the information about atmosphere contained in GPS data and to compare to InSAR data. For this reason, the TZDs were estimated each 15 minutes to get the TZD at a time as close as possible to that of acquisition on InSAR data. For all the 12 stations, only for the CASC site meteorological data (atmospheric pressure, temperature and relative humidity measured at surface) are available. They are acquired with a sampling time of 15 min and introduced in the process in the form of met-file. For stations without explicitly introduced values, the standard atmosphere model GPT (Global Pressure and Temperature) is used [13]. Proc. Fringe 2009 Workshop, Frascati, Italy, 30 November 4 December 2009 (ESA SP-677, March 2010)

2 Figure 1 - Distribution of GPS tracking stations in Lisbon region and InSAR image frame. 3. NUMERICAL WEATHER MODELS The Numerical Weather Models (NWMs) are three dimensional models of the atmosphere conditions in the lowest part of the atmosphere, from the surface of the Earth up to altitude of about 20 to 30 km approximately. These models contain predicted information about different meteorological parameters such as temperature, relative humidity, geopotential height, atmosphere pressure, horizontal wind components, among other important parameters. The principal purpose of the NWMs is to predict the future state of the atmosphere from the information on the present conditions by using numerical approximations of the dynamical equations describing the atmosphere behavior. In this study we used the Weather Research and Forecasting (WRF) Model produced by a collaborative partnership, between the NCAR Mesoscale and Microscale Meteorology Division (MMM), the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP), among others centers for Atmospheric Research Weather Research and Forecasting Model The WRF model is a next-generation mesoscale modeling system [14]. The Advanced Research WRF dynamical core is based on a Eulerian solver for the fully compressible, non-hydrostatic equations with an hydrostatic option available. The variables are solved in the scalar-conserving flux-form. The vertical coordinates are based on a mass-based terrain-following coordinate system. The horizontal and vertical grid staggering is Arakawa C-grid type. The Runge-Kutta third-order scheme and the fifth and third order schemes are used in the horizontal and vertical directions, and a time-splitting integration scheme is used on a shorter time step for the acoustic and gravity wave modes. Simulations from a numerical model are known to be sensitive to the representation of the physical processes. To obtain realistic results it is necessary to incorporate appropriate physics schemes into the model. In the WRF model these schemes are: (1) Planetary Boundary Layer, (2) Convection, (3) Microphysics, (4) Land- Surface Model, and (5) Radiation. A brief description of the physics packages can be found at [14]. In the present study the Advanced Research Weather and Forecasting Model (WRF-ARW) version 3, was setup with a four two-way nested domain at 54, 18, 6 and 1 km horizontal grid resolutions. The top of the atmosphere in the model is located at 10 hpa level, a total of 50 vertical levels are used and the lowest model layer is about 30 m thick. All grids use NOAH Land Surface Model (LSM), the Dudhia shortwave and the Rapid Radiation Transfer Model (RRTM) longwave radiation parameterization schemes, and the WSM 3-Class Simple Ice Scheme for the Microphysics. The Mellor-Yamada-Janjic TKE scheme is used for the Planetary Boundary Layer and the Kain-Fritsch convection scheme is employed on the 54, 18 and 6 km grids. The initial and time-dependent boundary conditions are derived from the ECMWF high resolution analysis. The model contains the parameters of temperature, atmosphere pressure and relative humidity required to derive the total zenith delay. The model was run over a area of 280 km in north-south direction and 180 km in east-west direction. 4. INTERFEROMETRIC S AR PROCESSING A total amount of 25 SAR interferograms with a 35-day temporal baseline were processed using the DORIS (Delft object-oriented radar interferometric software) software [15]. The SAR data were acquired by ENVISAT/ASAR over the Lisbon region during the period from 2003 to 2005 and from 2008 to RES ULTS As a first step, we computed both hydrostatic and wet components of the total zenith delay using the parameters temperature, atmosphere pressure and relative humidity derived from the WRF model. The temporal sampling of WRF s forecast is 15 min to cope with GPS estimates. As a second step, we compared the total zenith delays estimated by GPS to delays derived from W RF forecast at the location of GPS stations. Figs. 2 and 3 show the samples of the hydrostatic and wet delay estimates from both the WRF forecasts and the GPS measurements for the first day (April 12, 2009 corresponding to GPS day 102). Tab. 1 shows some statistics about the zenith delay differences in both components between the delay derived from WRF model and the GPS measurements.

3 Table 1. TZD differences in both components between WRF model and GPS estimates (all in mm). They are referred to the day April 12, 2009 (GPS day 102). differential equations. In the case of the wet delay, the mean difference is between 0.5 and 15 mm with an rms in a range of 5 to 17 mm. Station Hydrostatic Wet Mean RMS Max. Mean RMS Max. MAFR GRID ALCO FCUL PARC IGP VNOV PALM ARRA CRAI SMAR CASC Figure 3. Comparison of wet zenith delays derived from GPS (at GPS receiver position), in blue, and regional WRF model, in red, for 24 hours of day 102 ( ) Figure 2. Comparation of hydrostatic zenith delays derived from GPS (at GPS receiver position), in blue, and regional WRF model, in red, for 24 hours of day 102 ( ) The mean value of differences between the WRF and GPS estimates of the hydrostatic delay are for all stations but the ARRA, smaller than 14 mm with an rms in a range of 1 to 14 mm. For the ARRA station the mean difference reached 35 mm. This discrepancy can be related to the altitude of the GPS station and to the filtering of topography needed in the WRF model to avoid instabilities in the numerical solution of partial In a second experiment, we obtained the variation in both hydrostatic and wet components of TZD between the two days April 12, 2009 and May 17, 2009, both processed at 10 p.m. This corresponds to the acquisition time of InSAR data over the study area. In Figs. 4 and 5 we can see, respectively, the variation of tropospheric hydrostatic delay obtained from W RF model GPS estimates. The circles in black represented in Fig. 4 give the location of the GPS stations. Fig. 5 represents GPS measurement of the atmospheric delay. Both WRF forecast and GPS measurements reproduce a West-to- East trend. Results confirm the expected temporal stability of the hydrostatic TZD s. Figs. 6 and 7 represent the corresponding variations in wet component of the atmospheric delay. The circles in black represented in Fig. 6 gives the location of the GPS stations. Fig. 7 reports the GPS measurement of the delay. In this case, both GPS measurements and WRF forecast emphasize an overall North-West to South-East trend in the spatial distribution of the atmospheric delay.

4 Figure 4. Spatial distribution of temporal variation of troposphere hydrostatic delay obtained from WRF model between day 137 and 102, at 10 p.m. Units in mm. Figure 6. Spatial distribution of temporal variation of troposphere wet delay obtained from WRF model between day 137 and 102, at 10 p.m. Units in mm. Figure 5. Spatial distribution of temporal variation of troposphere hydrostatic delay estimates from GPS measurements between day 137 and 102, at 10 p.m. Units in mm. In both examples, it is clear that WRF predictions of atmospheric delay are in the same order of magnitude as GPS measurements. Tab. 2 shows same statistics about the zenith delay differences in both hydrostatic and wet components between the atmospheric delay changes (day 137 of 2009 minus day 102 of 2009) derived from WRF model and the GPS measurements. The mean differences between the WRF model and GPS estimates of the hydrostatic TZD are all but ALCO s maller than 30 mm. They refer to a 35 day time interval. The discrepancy at the ALCO station can be attributed to a local phenomenon which was not well forecast by WRF. Figure 7. Spatial distribution of temporal variation of troposphere wet delay estimate from GPS measurements between day 137 and 102, at 10 p.m. Units in mm. As far as the temporal changes of wet TZD are concerned, it was found that the mean difference is between 0.5 and 15 mm with an rms between 5 and 17 mm. In all the above cases, GPS stations are located close to the oceanic coast. Here the relative humidity is highly variable and as a consequence, also the atmospheric water vapor. Tab. 2 reports the differences between the hydrostatic and wet zenith delay estimated on April 12, 2009 and May 17, These differences were computed for both the WRF and GPS estimates.

5 Table 2. Variation in troposphere hydrostatic delay and wet delay obtain using the WRF model and estimates by GPS in two differences days (102 and 137) at 10 p.m. Station Hydrostatic Wet Variation Variation WRF GPS Diff. WRF GPS Diff. MAFR GRID ALCO FCUL PARC IGP VNOV PALM ARRA CRAI SMAR CASC As a final step of the experiment, TZD estimated by both WRF forecasts and GPS measurements was mapped to a synthetic fringe pattern and compared to the corresponding real interferograms (see Fig. 8). This interferogram has a temporal baseline of 35 days and it was corrected for topography and baseline errors. The West-to-East fringe pattern is not related to any known geological phenomenon occurring in this area able to produce a significant terrain deformation over this temporal scale. Changes in the spatial distribution of TZD occurred between the acquisitions of the two SAR images is a possible explanation for such a phase pattern. to generate the real interferograms. Temporal changes of these estimates were mapped to a synthetic fringe pattern. Figs. 9 and 10 show the synthetic fringe pattern obtained, respectively, from WRF forecasts and GPS measurements. Figure 9. Synthetic interferogram corresponding to the temporal variations of TZD estimates from WRF forecasts. It refers to April 12, 2009 and May 17, 2009 at 10 p.m. Units in radians. Figure 10. Synthetic interferogram corresponding to the temporal variations of TZD estimates from GPS measurements. It refers to April 12, 2009 and May 17, 2009 at 10 p.m. Units in radians. Figure 8. InSAR interferogram over Lisbon. It refers to ENVISAR/ASAR data acquired on April 12, 2009 and May 17, 2009 at about 10 p.m. Units in radians. To verify this hypothesis, the atmospheric TZD was estimated by WRF forecast and GPS measurements at time of each of the two ENVISAT/ASAR images used Both synthetic interferograms reproduce the same Westto-East trend observed in the ENVISAT interferogram so confirming that the observed interferometric phase is related to change in TZD. It is worth noting that the synthetic phase pattern derived from GPS estimates seems to be in a better agreement with ENVISAT interferogram. However, the spatial density of stations is not enough to accurately reconstruct all the InSAR fringe pattern. In contrast, the synthetic interferogram derived from WRF has enough samples to get a phase pattern directly comparable to the ENVISAT interferogram. However, even if the overall agreement between the WRF and ENVISAT interferograms is

6 quite good, there are differences related to local changes in TZD. 6. CONCLUS IONS As a conclusion we can draw from these first results, GPS measurements of TZD are locally in a better agreement with InSAR interferograms than WRF forecasts. However, estimates based on WRF data can well reproduce the large scale trend of the TZD spatial distribution. This property can be useful when trying to mitigate atmospheric artefacts in SAR interferograms. In fact, when a GPS network is not available, or it has just a few stations, WRF forecasts could still be useful to effectively mitigate effects of the atmospheric phase delay in interferograms. 7. REFERENCES [1] D. Massonet, K.L. Feigl, Radar interferometry and its application to changes in the earth s surface, Review of Geophysics, 36(4), , [2] H.A. Zebker, P.A. Rosen, S. Hensley, Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic map. Journal of Geophysical Research, 102(B4), , [3] Hanssen, R. F. (2001). Radar Interferometry, Data Interpretation and Error Analysis. Delft University of Technology, Netherlands. ISBN [4] S. Williams, Y. Bock, P. Fang, Integrated satellite interferometry: tropospheric noise, GPS estimates and implications for interferometric synthetic aperture radar product. Journal of Geophysical Research, 103(B11), , [5] P.W. Webley, R.M. Bingley, A.H. Dodson, G. Wadge, S.J. Waugh, I.N. James, Atmospheric water vapour correction to InSAR surface motion measurements on mountains: results from a dense GPS network on Mount Etna. Physics and Chemistry of the Earth, 27, , [6] Z.W. Li, X.L. Ding, G.X. Liu, Modeling atmospheric effects on InSAR with meteorological and continuous GPS observations: algorithms and some test results. Journal of Atmospheric and Solar-Terrestrial Physics, 66, , [7] Z.W. Li, J. Muller, P. Cross, E.J. Fielding, Interferometric Aperture Radar InSAR) atmospheric correction: GPS, Moderate resolution Imaging Spectroradiometer (MODIS), and InSAR integration. Journal of Geophysical Research, 110, B03410, [8] F. Chaabane, A. Avallone, F. Tupin, P. Briole, H. Mâitre, A multitemporal method for correction of tropospheric effects in differential SAR interferometry: application to the Guld of Corinth earthquake. IEEE Transactions on Geoscience and Remote Sensing, 45(6), , [9] J. Foster, B. Brooks, T. Cherubini, C. Shacat, S. Businger, C.L. Werner, Mitigating atmospheric noise for InSAR using a high resolution weather model, Geophysical Research Letters, L16304, [10] W.C. Skamarock, J.B. Klemp, J. Dudhia, D.O. Gil, D.M. Barker, M.G. Duda, X.Y. Huang, W. Wang, J.G. Powers, A description of the Advanced research WRF Version 3, NCAR Tech Note 475, Natl. Cent. for Atmos. Res., Boulder, Colorado, [11] Boehm, J. B. Werl, & Schuh, H. (2006). Troposphere mapping functions for GPS and very long baseline interferometry fromeuropean Centre for Medium-Range Weather Forecasts operational analysis data. J. Geophys. Res., 111, B02406, doi: /2005jb [12] Rothacher, M., & Mervart L. (1996). Bernese GPS Software Version 4.0. Astronomical Institute, University of Berne, Switzerland. [13] Herring, T. A., King, R. W. & McClusky, S. C. (2006). Documentations for the GAMIT, Reference Manual, GPS Analysis at MIT. Department of Earth, Atmospheric, and Planetary Sciences Massachusetts Institute of Technology. Release [14] W.C. Skamarock, J.B. Klemp, J. Dudhia, D.O. Gil, D.M. Barker, M.G. Duda, X.Y. Huang, W. Wang, J.G. Powers, A description of the Advanced research WRF Version 3, NCAR Tech Note 475, Natl. Cent. For Atmos. Res., Boulder, Colorado, [15] Kampes B., Hanssen R., Perski Z., Radar Interferometry with Public Domain Tools, Proceedings of FRINGE 2003, December 1-5, Frascati, Italy, 2003.