TU3.T10.5.ppt

Estimation of wave spectra with SWIM on CFOSAT – illustration on a real case

C. Tison (1), C. Manent(2), T. Amiot(1), V. Enjolras(3), D. Hauser(2), L. Rey(3), P. Castillan(1)

(1) CNES, « Altimetry and Radar » department, France(2) UVSQ, CNRS, LATMOS-IPSL, France(3) Thalès Alenia Space, France

[email protected]

IGARSS’11 – July, 2011 2

Overview of the presentation

■SWIM instrument and measures

■Performance budget SimuSWIM – an end-to-end simulator A real sea state condition Results


The CFOSAT mission

■ Status of the program: Conception and Development phase Launch planned end of 2014

■ SWIM Measurement of the oceanic wave properties Real-aperture radar with 6 beams (Ku band)

■ SCAT Measurement of wind sea surface Real-aperture radar (bi polar, Ku band)

■ KuROS Airborne sensor developed by LATMOS Validation of SWIM and SCAT

More on the CFOSAT mission tomorrow – session WE4.T10 Altimetry I

China France Oceanography SATellite


SWIM instrument (1/2)

SWIM: Surface Wave Investigation and Monitoring

Ku-band radar (scatterometer)6 beams


SWIM instrument (2/2)

Wind sea

Swell Nadir signal SWH and wind speed- Accuracy SWH: max(10% of SWH, 50 cm)-Accuracy wind speed: 2 m/s

10°, 8° and 6° beams wave spectrum- spatial sampling of 70 x 70 Km²- Detectable wave wavelength : λ ~ [70 - 500] m- Azimuth accuracy: 15°- Energy accuracy: 15%

All beams backscattering coefficient profiles:- Absolute accuracy < +/- 1 dB- Relative accuracy (between beams) < +/- 0.1 dB


Estimation of wave spectra

Wave topography: ξ(x,y)2),((),( yxTFkkF yx ξ=

Directional wave spectrum F(kx,ky)

Modulation of the backscattering coefficient

),()(2

)),((),( 222 ϕθαπφφ kFkL

XmTFkPy

m ==

Signal modulation

Modulation spectrum Pm

Received power

X∂∂∝ ξ

σδσ

Wave slopesX∂

∂ξ

)(),(σδσφ fXm =

Link slope/signal modulation

IGARSS’11 – July, 2011

Simulations

■Simulations from the sea surface to the estimated signal Input = sea state conditions Output = wave spectrum

computation of backscattered intensity and processing similar to the future ground segment

use SWIM parameters

End-to-end simulation tool: SimuSWIM

320320320320320320Bc (MHz)

2.34.86.88.711.924.3SNR (dB)

2371921446060110Nimp (fixed)

6340-6667 variable

6378-6707 variable

6407-6739variable

2125fixed

2125fixed

2125fixed

PRF (Hz)

37.430.122.531.531.551.8Max integration time (ms)

10°8°6°4°2°0°


SimulationsEnd-to-end simulation tool: SimuSWIM

Input spectrum(models, measurements)

Surface computation

Backscattered signal(knowing SWIM

geometry and properties)

At a given azimuth direction:- Computation of the Nimp backscattered

pulses2 options:1. Computation of the Nimp pulses (with

geometrical migrations and noises for each)

2. Computation of one pulse and additions of noise (thermal+speckle) to create the Nimp pulses with central migrations

- Addition of the Nimp signals

Nimp pulses per cycles


SimulationsEnd-to-end simulation tool: SimuSWIM

Input spectrum(models, measurements)

Surface computation

Backscattered signal(knowing SWIM

geometry and properties)

Estimated modulation spectrum

Quality criteria


Impact of migrations (1/2)

Nimp

Nimp/2

NRER

MR

FR

ΔxMR

Due to satellite advection and antenna rotation: MIGRATIONS range (of each target) different at each impulse

-3 kinds of migration:-Centre migration-Migration along elevation-Migration along wave front

-NB: at the cycle scale, no impact of antenna rotation

Corrected by chirp scalingNon correctable

Two ways of simulation (for computation time constraints):2. With only central migrations and elevation migrations3. With all migrations


Impact of migrations (2/2)

(a) Reference 2D modulation spectrum

(b) Estimated 2D modulation spectrum WITHOUT complete migration

(c) Estimated 2D modulation spectrum WITH complete migrationsDirection Wave-

length

Energy

Swell 0°

0°

0%

3%

13% 10%

Wind sea 11°

14°

27% 16%

19% 12%


A real sea state condition: “Prestige case”

■Case of November, 2002 storm in Atlantic ocean Lead to the sinking of the Prestige (oil

tanker)

■Very different conditions during the day00:00: low wind sea + dominant swell06:00: very young wind sea (high

wind) + dominant swell 08:00: mature wind sea + dominant

swell15:00: crossed wind seas (old +

young) Wind sea rotated by about 120°

SpainGalician coast

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■Available data MFWAM output with ALADIN winds (Météo France models of wind and waves)

2D polar azimuth/frequency height spectrum converted into 2D cartesian wavenumber by bilinear interpolation

Subset of results: 00, 06, 08, 10, 15 UTC (different wind and waves cases)

■Simulation conditions Incidence angle: 10°

Nimp = 237 pulses per cycle ( averaging for noise reduction)

Rotation speed = 5.7 rpm ( 49 cycles / 360°)


Simulation results06:00 UTC

Reference: 2D spectrum from WAM model CFOSAT/SWIM estimation

(simulations from SimuSWIM)

Same detection of swell and wind sea partitionsSmall underestimation


1D modulation spectra

6h UTC

Swell Φ=135° (SE-NW look direction)

Sea wind Φ = 235° (NE-SW look direction)

Φ = angle between satellite track (assumed S-N) and radar look direction


00:00 UTC 06:00 UTC 08:00 UTC 15:00 UTC10:00 UTC

Reference: 2D spectrum from WAM model

CFOSAT/SWIM estimation (simulations from SimuSWIM)

Hs: 6.5 mU: 17.3 m/s

Hs: 6.1 mU: 8.8 m/s

Hs: 5.8 mU: 22.2 m/s

Hs: 5.1 mU: 11.7 m/s

Hs: 6.5 mU: 21.0 m/s

Prestige SOS 14:00 UTC


Performance quality

5%19%7°6%12%3°15h

7%6%11°8%8%0°10h

6%18%11°7%1%0°08h

19%27%11°13%0%0°06h

---4%0%3°00h

EλΦEλΦ

WIND SEASWELL

Estimation errors on wave direction (Φ), wave wavelength (λ) and energy (E):

<15° <10-20% <15%Requirements:


Conclusions

■Simulations of SWIM wave products End-to-end simulations Software with realistic sensor conditions

Accurate results with a large variety of sea state conditions

■Next steps Keep-on the definition of the inversion algorithms

Optimize inversion up to wave spectrum estimation of the transfer function (α)

),()(2

)),((),( 222 ϕθαπφφ kFkL

XmTFkPy

m ==

Technology

TU3.T10.5.ppt