modeling and applications OF swot satellite data

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/JPL3CNES, 4LEGOS/CNRS

970

km

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-scalephenomena (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

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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

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Simulator output: water heightThe Amazon river, Brazil

Simulator principle• Based on works of:

S. Biancamaria and M. Durand: swath calculation, principleV. Enjolras: residual error calculation

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Simulator principle• Based on works of:

S. Biancamaria and M. Durand: swath calculation, principleV. Enjolras: residual error calculation

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Simulator principle• Based on works of:

S. Biancamaria and M. Durand: swath calculation, principleV. Enjolras: residual error calculation

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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…)• ….

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Residual height errors: Roll

• Roll

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H

h

B a

ir1r2

R

Residual height errors

• Baseline

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H

h

B

ir1r2

R

E_b

Residual height errors

• Coherence loss

g = gSNR + gSQRN + gg

N number of looks

10

H

h

B

ir1r2

R

Simulator principle• Based on works of:

S. Biancamaria and M. Durand: swath calculation, principleV. Enjolras: residual error calculation

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Simulator principle• Based on works of:

S. Biancamaria and M. Durand: swath calculation, principleV. Enjolras: residual error calculation

12

m

Simulator principle• Based on works of:

S. Biancamaria and M. Durand: swath calculation, principleV. Enjolras: residual error calculation

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Simulation: Ohio River

Input: Model LisFLOODReference water height (m)

Output: Water height observed by SWOT (m)

3 months modelization courtesy: K. Andreadis

40.5

40

39.5

39

38.5

40.5

40

39.5

39

38.5

Latit

ude

Latit

ude

275 276 277 278 279 275 276 277 278 279Longitude Longitude

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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)

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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

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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 …

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Thank for your attention