1 / 19

modeling and applications OF swot satellite data

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. 1. SWOT mission. NASA and CNES, launch in 2019

tale
Download Presentation

modeling and applications OF swot satellite data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. 1 SWOT mission • 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 1. Radar cross section CNES/ CAP Gemini simulator 2. SAR amplitude image: Rhone river, France CNES/ Altamira information simulator

  4. 3 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 Simulator output: water height The Amazon river, Brazil

  5. 4 Simulator principle • Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation

  6. 5 Simulator principle • Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation

  7. 6 Simulator principle • Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation

  8. 7 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…) • ….

  9. 8 Residual height errors: Roll B a • Roll i R r1 r2 H h

  10. 9 Residual height errors E_b B • Baseline i R r1 r2 H h

  11. 10 Residual height errors • Coherence loss g = gSNR + gSQRN + gg N number of looks B i R r1 r2 H h

  12. 11 Simulator principle • Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation

  13. 12 Simulator principle • Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation m

  14. 13 Simulator principle • Based on works of: S. Biancamaria and M. Durand: swath calculation, principle V. Enjolras: residual error calculation

  15. 14 Simulation: Ohio River 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. 15 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) 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. 17 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 …

  19. Thank for your attention

More Related