Impacts of atmospheric attenuations on AltiKa expected performances

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Impacts of atmospheric attenuations on AltiKa
expected performances
J.D. Desjonquères (1), N. Steunou(1) A.
Quesney(2) P. Sengenes(1), J. Lambin(1) J.
Tournadre(3) (1) CNES, France (2) NOVELTIS,
France (3) IFREMER, France
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Contents

• Introduction
• Method of simulation
• Simulated clouds effect on Ka and Ku
  measurement
• Use of MODIS data
• Simulation of return waveforms above MODIS tracks
• Impacts on range, SWH in several real clouds
  configurations
• Comparison Ka/Ku
• Statistical study
• Classification of atmospheric attenuation scenes
• Results
• Conclusion / Perspectives

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AltiKa main characteristics

• Ka-band
• Larger bandwidth higher vertical resolution
• negligible ionospheric effect

Shorter decorrelation
Smaller antenna footprint better sampling and
better behavior in transitions areas (coastal
zones )
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AltiKa expected performances

• Expected range measurement noise (1 Hz) on ocean
  surfaces
• Accuracy of the altimeter range measurement over
  sea surface about 1 cm for a SWH of 2 meters
• Improvement of about 40 on the range noise
  versus Ku-band performances

1 second range noise (cm) versus SWH in Ku- and
Ka-bands
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Principle of the waveform simulation

• Construction of the waveform by application of
  the radar equation above 100 m x 100 m pixels
• Sea conditions SWH 2 m
• Atmospheric attenuations map
• Profile along track
• Loop on variable footprints (no temporal
  correlation)
• Retracking MLE4
• Range, SWH, level, square mispointing ?²
• With or without Speckle

Simulation validation Estimation of the
parameters on a perfect simulated waveform
Range error 0.080 cm SWH error -2.272
cm Mean square mispointing -2.253016e-004 deg²

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Atmospheric attenuations map

• Simulated clouds characterized by
• Cloud size (length, width, height Hc) and
  positioning on the track
• Att_dB 2 ? Hc ? kp with kp ? LWC.
• ? Ka  1.070049578 (dB/km)/(g/m3)
• ? Ku  0.16968466 (dB/km)/(g/m3)
• Use of MODIS data (Noveltis and CNES study)
• Use of MODIS cloud product (MOD06), around 1
  km-pixel
• cloud water path (CWP), cloud phase (liquid or
  ice), cloud optical thickness, cloud particle
  effective radius are selected for our study
• Att_dB 2 ? kp x CWP with kp ? LWC.
• ? Ka  1.070049578 (dB/km)/(g/m3) for liquid or
  ice phase (worst case)
• Interpolation of CWP data at 100 m - resolution

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Effect of a simulated cloud

• Simulated cloud 5 km diameter, 1 km height,
  LWC1g/m3 (1kg/m²) , centered on the track
  (cumulonimbus characteristics )

Ka-band
? 10 cm (max) on range 40 cm (max) on SWH
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Effect of a simulated cloud

• Simulated cloud 5 km diameter, 1 km height,
  LWC1g/m3 (1kg/m²), centered on the track
  (cumulonimbus characteristics )

Ku-band
? 4 cm (max) on range 20 cm (max) on SWH
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Example of MODIS profile typical weather

• Ka-band result

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Example of MODIS profile typical weather

• Comparison of Ka/Ku-band errors for 40-Hz or
  20-Hz data on the same profile

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Example of MODIS profile typical weather

• Same profile, with additional Speckle noise on
  the echoes

Range estimations at 40 Hz and 1 Hz, clouds with
Speckle

• Effect of clouds on the range with Speckle
  simulation
• Average error due to clouds -0.09 cm
• Increase of noise (taking into account a cloud
  event at the beginning)
• from 3.8 cm to 4.1 cm (40 Hz data), from 0.6 cm
  to 0.95 cm (1 Hz data)
• In most cases, presence of cloud cells in
  footprint induces a low increase of noise w.r.t.
  Speckle noise

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Other example of MODIS profile high water
content event

• Evolutions of parameters estimations could be
  used to discriminate contaminated waveforms
• Rain effect, CNES/CLS study on rain rates from
  TRMM/TMI data shows that
• Average for one year and all geographical areas
  show that around 3 of data will be unavailable
• Unavailability can reach 10 locally depending
  on season (e.g. Bengal Golf)

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Statistical study

• Method
• Extraction of 13km13km scenes of attenuation
  from the MODIS water content product.
• Classification
• For each class, simulation of echoes affected by
  the characteristic attenuation.
• Statistical processing

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Classification

• Neuronal Classification of the attenuation
  profiles (differential attenuation in the
  footprint)
• Input
• 11 724 250 scenes for the classification (12 days
  1 day / month)
• 3 882 373 scenes for neuronal network training (4
  days 1 day / trimester)
• Output
• A referent profile of attenuation for each class
• Cardinality of the classes
• Mean attenuation histogram for each class

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Statistical results
Ka band and SWH2m
Atmospheric attenuation effect Range error lt
0.1 cm 85 , lt 1 cm 93 , lt 2 cm 96
SWH error lt 1 cm 88 , lt 5 cm 95
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Classification validation spatial coherence
between attenuations and errors

• Large scale error cartography

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Classification validation spatial coherence
between attenuations and errors

• local error cartography

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Conclusion

• Data unavailability due to clouds has been
  estimated
• More than 90 of waveforms should be nominally
  processed
• We expect that most of contaminated waveforms
  could be processed through dedicated algorithms
• results in representative situations (along track
  simulation with Speckle)
• Averaging elementary data (e.g. from 40 Hz to 1
  Hz) induces a reduction of the errors due to
  clouds
• In typical situations, the effect of clouds is
  equivalent to an increase of noise measurement
• Perspectives
• A general study of waveforms classification is in
  progress
• To build editing method (see Jean TOURNADRE
  presentation)
• Study on geographical and seasonal availability
  is being performed (with Noveltis)