MODELING AND APPLICATIONS OF SWOT SATELLITE DATA C. Lion 1, K.M. Andreadis 2, R. Fjørtoft 3, F. Lyard 4, N. Pourthie 3, J.-F. Crétaux 1 1 LEGOS/CNES, 2 Ohio State University/JPL 3 CNES, 4 LEGOS/CNRS

970 km SWOT mission NASA and CNES, launch in km 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 1

Preparing the mission for hydrology 2. SAR amplitude image: Rhone river, France CNES/ Altamira information simulator 1. Radar cross section CNES/ CAP Gemini simulator Modelisation and simulation for technical use 2

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

Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 4

Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 5

Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 6

Residual height errors Taken into account Roll Baseline variation Thermal noise Geometric decorrelation BAQ noise Satellite position Not taken into account yet Troposphere Layover Shadow Processing (classification…) …. 7

Residual height errors: Roll Roll 8 H h B  i r1r1 r2r2 R

Residual height errors Baseline 9 H h B i r1r1 r2r2 R E_b

Residual height errors Coherence loss  SNR  SQRN  g N number of looks 10 H h B i r1r1 r2r2 R

Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 11

Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 12 m

Simulator principle Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation 13

Simulation: Ohio River Input: Model LisFLOOD Reference water height (m) Output: Water height observed by SWOT (m) 3 months modelization courtesy: K. Andreadis Latitude Longitude 14

Assimilation methodology 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) 15 courtesy: K. Andreadis

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 16 courtesy: K. Andreadis

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