2nd godae observing system evaluation workshop - june 2009 - 1 - future altimetry design from impact...

2nd GODAE Observing System Evaluation Workshop - June 2009

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Future altimetry design

From impact studies to operational metrics or the reverse ?

G.DibarboureJ.DorandeuP.Escudier


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Introduction• Should early impact studies define operational metrics or the reverse ?

• Framework : support to future mission design (ESA, CNES, Eumetsat, Ifremer)– Definition of Sentinel-3, Phasing options for the Jason tandem– Figure of merits for future altimetry concepts : wide-swath, large constellations…– Impact of payload changes : noise level reduction, cost reduction…

• Exploratory studies : – Iterative process (mission concept performance assessement)– CLS’ Toolbox for Mission Analysis Testing and Optimisation (TOMATO)– Two types of analysis : mission analysis & OI based OSSEs

• Need : simple yet compelling results– Subtle payload differences ? Metrics used must give a clear answer– Conflicting mission objectives (e.g.: OC vs Altimetry on S3) ?

Altimetry metrics must be convincing for decision-makers E.g: 15% of additional variability observed is not convincing for neophytes

• Illustration of the DUACS approach through two ongoing studies : Post-EPS reference orbit & High-resolution altimetry


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Example 1 : Post-EPS reference orbit• Question asked by Eumetsat : which orbit should be used for future reference missions ?

– Reference orbit (T/P, Jason) is exceedingly aggressive– Onboard anomalies and failure (Jason-1 has burnt most redundant safeties)– The motivations behind the TP orbit choice are no longer major constraints

• Many factors to take into account :– History and existing time series– Sampling capability, Aliasing issues– Error budget (e.g. : POD performance vs orbit parameters)– Many applications : climate, mesoscale, ice monitoring, hydrology…– Mission cost (launch, operations, mission lifespan)– And other altimetry missions !

• Unlikely to find a single perfect orbit, so the study rationale is to :– Sort out (many) bad options first filter : no time wasted– Analyze orbit candidates with more details second filter process : when in doubt, trash it– Keep only a handful of interesting orbits for the community to check out


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Post-EPS : Aliasing analyses

• First filter : orbit geometry and base properties– Acceptable altitude and inclination range– Repetitive– Acceptable repeat (sub)cycle duration– Optimal to host a tandem of 2+ altimeters

• Second filter : tidal components– Must allow tidal wave observation within 3 to 5 years

(aliasing under control)– Tidal components must be separable within a

reasonable time span

• Basic selection leads to 1400 options

• Drastic separability requirements : 0 option

• Trade-offs many options with different pros/cons


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Importance for 2 sat optimisation

Jason + ENVISAT

Jason + T/P Inferring optimisation for Metrics TypeImportance for 3 sat optimisation

Jason + ENV+ GFO

Jason + T/P + ENVISAT

Jason x 3

High + ++ Maps of covariance to observation Qualitative High + ++ +++High + + Quality indicator distribution Quantitative High + ++ +++Low + ++ Equivalence to mapping error Quantitative Low ++ ++ +++High - ++ Homogenity Std of covariance to observation Quantitative Medium - + +++High - + Prob. to detect 150km features Quantitative Low ++ ++ +++Low n/a n/a Prob. to detect 75km features Quantitative High + ++ +++High - + Monitoring of known features Prob to lose tracking Quantitative High + + +++

Medium - + Multi-sat crossover angle Quantitative Low + ++ ++Medium + ++ Ground track angle to equ. plane Quantitative Medium - + ++

Low - -- Beta prime cycle duration Quantitative Medium - - --Medium + -- Multisat crossover coverage for short dt Qualitative Low ++ - --Medium + -- Potential ocean coverage % ocean covered for inclination Qualitative Medium + - --

+ + + ++ +++Overall

2 Satellite standards 3 Satellite standards

Global quality

Detection of new features

Crosscalibration

Velocity observation

Post EPS : multi-satellite sampling analysis• Geometrical sampling analysis (no model, no OI)

– Observation quality (correlation between structure and observation)

– Ability to detect mesoscale changes in NRT

– Observation isotropy (e.g.: currents mapping, crossovers)

– Structure monitoring/tracking capability

– …

• Protocol validation on historical missions• After this screening process : 12 candidates interesting


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Post-EPS - Output of step 1 : first orbit selection

Altitude (km)

Inc (deg)

Cycle (day

s)

Exact repeat cycle (days)

Tide

Aliasing

Tide separabili

-ty (years)

Optimi-sation for 2 satel-lites

Sub-cycle (day

s)

S1 aliasing (days)

rev/dayScores from 2.5

A878_i66_c10

878.731 66 10 9.901936K1

included3.5 y no 1 100.97

13+9/10

7.001

A1150_i72_c11

1150.317

72 1110.91784

3No K1 4 y yes 5 132.89

13+2/11

105.02

A964_i74_c19

964.879 74 1918.86048

2No K1 5 y yes 3 135.18

13+13/19

116.031

A835_i75_c19

835.619 75 1918.86055

1No K1 5 y yes 1 135.25

14+1/19

116.031

A1076_i68_c11

1076.855

68 1110.90465

3

no K1, but alias K1>2cp

y afterall

6y yes 3 114.3613+4/1

119.01

A801_i71_c22

801.857 71 2221.81043

8K1

inluded6 y no 7 115.05

14+3/22

19.011

A1361_i65_c11

1361.612

65 1110.90533

8K1

included6 years yes 3 115.20

12+7/11

18.01

A912_i70_c11

912.147 70 1110.90547

9K1


13+9/11

18.01

A822_i68_c15

822.474 68 1514.85855

1K1


14+1/15

17.01

A1104_i76_c16 1104.802 76 16 15.895629 K1<2cpy - no 3 152.3 13+5/16104.0

2

A923_i67_c9 923.365 67 9 8.915522 K1>2cpy - no 4 105.53 13+7/9 103

A926_i67_c13 926.436 67 13 12.878103 K1>2cpy - no 4 105.64 13+10/13214.0

1

Initial selection (tidal filter nominal)

Additional selection (relaxed tidal aliasing requirements, except on 4-9 cpy climate band)


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Post-EPS : Mesoscale sampling capability (1/2)• Analysis performed from OI OSSE based on Mercator simulations

• Mercator « reality » Observation simulated OI used to reconstruct

• Reconstruction error gives access the sampling capability


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Post-EPS : Mesoscale sampling capability (2/2)

• Once suboptimal options are removed, the mapping processoffsets uneven sampling minordifferences

• Sampling error on U/V vary by ~10% in non coordinated tandems

• Impact of orbit inclination on sampling isotropy still still visible after mapping (especially combined with high-inclination S3)

• Three good candidates (T/P-like with ~10% more data thanks to lower altitude): results coherent with geometrical analysis

• SWOT orbit 22d is not the best option to host (only) a traditional altimeter

• Any contribution from GODAE would be useful to complete this study (model-based OSSE, metrics suggested…)


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Example 2 : High resolution altimetry

• Explore the benefits of a 24 satellite constellation (Nadir only)– Next generation of Iridium = altimeter payload passengers ?

– Cost minimized (minimal payload, error budget tradeoffs)

• Comparison to a global wide-swath altimeter observation

• Impact of noise reduction (AltiKa, doppler altimetry, SWOT)

• First step : geometrical analysis– To provide a first quantification of the benefits

– To tune the altimeter payload distribution on the 66 potential Iridium slots

– To explore multiple time+spatial scales (e.g.: meteo, mesoscale, submeso)

• Second step : OI impact study on (sub)mesoscale– Needed to quantify the impact on currents and vorticity

and the HF or short scale specific error

• Work performed with support from CNES and Ifremer

945km

315km

315km

630km

Legend

Sub-cycle #1

Sub-cycle #2

Sub-cycle #3

1

10

100

1000

10000

0,1 1 10 100Delta T (days)

Del

ta X

(km

)

3 sat

SWOT 15km

Iridium 80

SWOT 5km

Up : Jason cycle / sub-cycle scanning patternDown : Space/Time scale observation limit


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Constellation detection and monitoring skill (left : 150km, right : 20km)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2 4 6 8 10 12 14 16 18 20Scanning time (days)

Probability to detect a new feature (dx=150km)

SWOT

J1 EN G2

IRIDIUM

IRIDIUMx12

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 612 18 24 30 36 42 48 54 60 66 72 78 84 90 96 10

210

811

412

012

613

213

814

415

015

616

216

8

Scanning time (hours)

Probability to detect a new feature (dx=20km)

SWOT J1 EN G2

IRIDIUM IRIDIUMx12

Data mostly obsolete due to the signal time correlation scales

High-resolution altimetry : geometrical analysis

Instantaneous correlation between one snapshot and past altimetry data

(realistic correlation model 150km/15d, arbitrary snapshot from day 12)


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Model EKE : ES

• Reality used : POP or Earth Simulator (ES) outputs– Configurations analysed 1 to 4 sats, 24 Iridium, SWOT– Realistic error levels on simulated observations

• Ongoing work : – Actual mapping reconstruction error (H,U/V, Vorticity)– First step : crude mapping parameters (100km, 5 to 10d)– Separation of error on HF/LF content (time & space)– Separation of error from mapping limitations & sampling limitations

– For POP content SWOT sampling is good and Iridium excellent– For ES, SWOT temporal sampling is more problematic, but correlation scales must be revisited

High-resolution altimetry : OI impact studyModel EKE : Los Alamos 1/10°

2000cm²/s²


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High-resolution altimetry : impact of noise level

• Starting question : how does the altimeter data high frequency error (instrument noise, processing error…) affect the power spectrum ?

• Earth Simulator output Sampled along altimetry ground tracks (50 days of ideal obs)

• Variable white noise Realistic observations

• Consistent with spectrum slope of actual data in GulfStream (-3.4 for [90-200 km] for 2.5 to 3cm HF content)

• Impact of SWOT roll+baseline error : far range spectrum is K-3 and increasing to K-11/3 as the data get closer to the Nadir position in swath

• Reducing the high-frequency error is important : Ka-Band, Doppler, SLOOP project processing…

Noise stdSpectrum slope(50 to 200 km)

Spectrum slope (80 to 200 km)

3 mm -3.72 -3.69

1 cm -3.59 -3.70

2 cm -3.21 -3.60

3 cm -2.90 -3.24

3 cm

1 cm

3 mm

SSH power spectrum (Jason-2 simulated data from ES reality + variable HF error level)

K-5 or K-11/3 ?


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Summary and Conclusion• Overview of ongoing studies

– Long term questions : new reference orbit, impact of HF error, high-resolution sampling…– Short answers : 3+ good orbits, reduce the noise, attractive 24 satellite concept (complement to SWOT )

• Two-types of studies carried out by CLS : mission analysis & OI impact studies– Excellent way to explore unusual configurations or numerous variants (sort out poor options)– Some metrics are more convincing for decision-makers than classical results

E.g. : mesocale can observed in real time with 12 satellites is a stronger message than 4% mesoscale observation error removed with +8 sats

– Full science content must be consolidated afterwards (finer quantification once the general concept is nailed down)

• Past impact studies helped define current operational metrics (DUACS : K.P.I)• Conversely any operational metric can be deployed for such a demonstration • This logic is applicable to GODAE models : design/impact studies operational metrics

• For future concepts, we need to be consistent with future operational metrics• What routine-to-be metrics should be used to help design future observing systems ? • So what will be requirements of GODAE models in 2018+ ?

2nd godae observing system evaluation workshop - june 2009 - 1 - future altimetry design from impact...

Documents

orbit geometry

orbit candidates

reference orbit tp

nd godae

altimetry metrics

system evaluation workshop

mission analysis testing

mission lifespan