OSTST - March 2007 - Hobart 1 Impacts of atmospheric attenuations on AltiKa expected performances J.D. Desjonquères (1), N. Steunou (1) A. Quesney (2)

OSTST - March 2007 - Hobart 1

Impacts of atmospheric attenuations on Impacts of atmospheric attenuations on AltiKa expected performancesAltiKa expected performances

J.D. Desjonquères J.D. Desjonquères (1)(1), N. Steunou, N. Steunou(1)(1)

A. QuesneyA. Quesney(2)(2)

P. SengenesP. Sengenes(1)(1), J. Lambin, J. Lambin(1)(1)

J. TournadreJ. Tournadre(3)(3)

(1)(1) CNES, France CNES, France(2) (2) NOVELTIS, FranceNOVELTIS, France(3) (3) IFREMER, FranceIFREMER, France


IntroductionMethod of simulationSimulated clouds : effect on Ka and Ku measurementUse of MODIS dataSimulation 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

ContentsContents


AltiKa main characteristicsAltiKa main characteristics

Parameter Value Comparison with J ason Ku-band

Mean orbit altitude ~800 km 1347 km

Orbit inclination ~98° 66°

Altimeter band 35.75 GHz 13.575 GHz

Pulse bandwidth 500 MHz 320 MHz

Vertical resolution 30 cm 47 cm

Pulse duration 110 s 110 s

Altimeter Pulse repetition f requency

3.8 kHz (adjustable along the orbit)

1.8 kHz

Echo averaging (altimeter) 25 ms 50 ms

Spectrum analyser (altimeter) 128 points 128 points

Altimeter Link budget 11 dB (sigma naught = 6.5 dB) 12 dB (sigma naught = 6.5 dB)

Antenna diameter 1 m 1.2 m

Antenna aperture 0.6° 1.3°

Antenna gain 48.5 dB 42 dB

Data rate 38 kbits/ s 20 kbits/ s (compression)

Mass 33 kg

Power consumption < 80 W

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

Smaller antenna footprint : better sampling and better behavior in transitions areas (coastal zones …)

Shorter decorrelation


AltiKa expected performancesAltiKa 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-bands1 second range noise (cm) versus SWH in Ku- and Ka-bands


Principle of the waveform simulationPrinciple 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 validationSimulation validationEstimation of the parameters on a “perfect” simulated waveform :

Range error : 0.080 cmSWH error : -2.272 cmMean square mispointing : -2.253016e-004 deg²

0 20 40 60 80 100 1200

20

40

60

80

100

120

140

160

180

Simulated waveform

Estimated waveform after retracking


Atmospheric attenuations mapAtmospheric 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


Effect of a simulated cloudEffect of a simulated cloud

Simulated cloud : 5 km diameter, 1 km height, LWC=1g/m3 (1kg/m²) , centered on the track (cumulonimbus characteristics …)

Ka-bandKa-band

10 cm (max) on 10 cm (max) on rangerange

40 cm (max) on 40 cm (max) on SWHSWH


Effect of a simulated cloudEffect of a simulated cloud Simulated cloud : 5 km diameter, 1 km height, LWC=1g/m3 (1kg/m²), centered on the

track (cumulonimbus characteristics …)

Ku-bandKu-band

4 cm (max) on range 20 cm (max) on SWH


Example of MODIS profile : typical weatherExample of MODIS profile : typical weather

Ka-band result :



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



Same profile, with additional Speckle noise on the echoes :

0 50 100 150 200 250 300-25

-20

-15

-10

-5

0

5

10

15

Time, sec

cm

Estimated bias on the range

Cycle

sec

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

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


Other example of MODIS profile : high water content eventOther 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)


Method

Extraction of 13km*13km scenes of attenuation from the MODIS “water content” product.

Classification

For each class, simulation of echoes affected by the characteristic attenuation.

Statistical processing

Statistical studyStatistical study


ClassificationClassification

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


Statistical resultsStatistical results

Ka band and SWH=2m

Atmospheric attenuation effect:

Range error < 0.1 cm : 85 % , < 1 cm : 93 %, < 2 cm : 96 %

SWH error < 1 cm : 88 % , < 5 cm : 95 %


Classification validation : spatial coherence Classification validation : spatial coherence between attenuations and errorsbetween attenuations and errors

Large scale error cartography


Classification validation : spatial coherence Classification validation : spatial coherence between attenuations and errorsbetween attenuations and errors

local error cartography


ConclusionConclusion

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)

Documents

OSTST - March 2007 - Hobart 1 Impacts of atmospheric attenuations on AltiKa expected performances J.D. Desjonquères (1), N. Steunou (1) A. Quesney (2)