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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 k
m
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
1
Preparing the mission for hydrology
2. SAR amplitude image:Rhone river, FranceCNES/ Altamira information simulator
1. Radar cross sectionCNES/ 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 heightThe 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…)
• ….
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Residual height errors: Roll
• Roll
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H
h
B
ir1r2
R
Residual height errors
• Baseline
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H
h
B
ir1
r2
R
E_b
Residual height errors
• Coherence loss
SNR SQRN g
N number of looks
10
H
h
B
ir1r2
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 LisFLOODReference water height (m)
Output: Water height observedby SWOT (m)
3 months modelization courtesy: K. Andreadis
40.5
40
39.5
39
38.5
40.5
40
39.5
39
38.5
Lati
tud
e
Lati
tud
e
275 276 277 278 279 275 276 277 278 279
Longitude 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)
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
