This document discusses modeling and applications of SWOT satellite data. The SWOT mission, launching in 2019, will use Ka-band radar interferometry and a wide swath altimeter to measure ocean and inland water elevations. A simulator was developed to generate realistic SWOT observations for scientific users studying hydrology. The simulator works quickly, is easy to use, and incorporates errors from various sources like thermal noise and baseline variation. The simulator output has been used to assimilate simulated SWOT data into hydraulic models of rivers, like the Ohio River, to estimate water surface elevations. Further improvements to the simulator are still needed to account for additional error sources like layover and atmospheric delays.
1. modeling and applications OF swot satellite data C. Lion1, K.M. Andreadis2, R. Fjørtoft3, F. Lyard4, N. Pourthie3, J.-F. Crétaux1 1LEGOS/CNES, 2Ohio State University/JPL 3CNES, 4LEGOS/CNRS
2. SWOT mission 1 NASA and CNES, launch in 2019 970km orbit, 78°inclination, 22 days repeat KaRIN: InSAR Ka band Wide swath altimeter Ocean: “Low resolution” meso-scale and submeso-scale phenomena (10km and greater) Hydrology: “High resolution” surface area above (250m)² rivers above 100m 970 km
3. 2 Preparing the mission for hydrology Modelisation and simulation for technical use 2. SAR amplitude image: Rhone river, France CNES/ Altamira information simulator 1. Radar cross section CNES/ CAP Gemini simulator
4. Goals Need for a simulator for scientific users (hydrology) “Fast”: 3 months 3min Easy to use: no need for heavy preparation of input data Portable Relatively realistic errors Targets: deltas, rivers, lakes… Output: water elevation 3 Simulator output: water height The Amazon river, Brazil
5. Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 4
6. Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 5
7. Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 6
8. Residual height errors 7 Taken into account Roll Baseline variation Thermal noise Geometric decorrelation BAQ noise Satellite position Not taken into account yet Troposphere Layover Shadow Processing (classification…) ….
11. Residual height errors Coherence loss g = gSNR + gSQRN + gg N number of looks 10 B i R r1 r2 H h
12. Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 11
13. Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 12 m
14. Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 13
15. Simulation: Ohio River 14 3 months modelizationcourtesy: K. Andreadis 40.5 40.5 40 40 Latitude Latitude 39.5 39.5 39 39 38.5 38.5 275 276 277 278 279 275 276 277 278 279 Longitude Longitude Input: Model LisFLOOD Reference water height (m) Output: Water height observed by SWOT (m)
16. Assimilation methodology 15 Assimilating SWOT observations in a identical twin synthetic experiment Ohio River study domain (only main stem) LISFLOOD hydraulic model Ensemble Kalman filter Errors introduced to boundary inflows, channel width, depth and roughness Observation errors from a Gaussian distribution N(0,5cm) courtesy: K. Andreadis
17. 16 Assimilation results Water surface elevation along the river channel at two SWOT overpass times 208 Hours 280 Hours Information is not always propagated down/up stream Small ensemble size could partly be the reason courtesy: K. Andreadis
18. Conclusions Simulation of SWOT data with more representative errors The simulator is more user friendly: output format as input format, GUI, can be used with several models Can be used for assimilations studies (estimate indirect valuables) Need to improve the simulator: layover, decorrelation due to vegetation, troposphere … 17
