M.I.Pujol, G.Dibarboure, G. Larnicol (CLS) P.Y.Le Traon, P.Klein (IFREMER),

GOV-CLIVAR workshop 13-17 june, Santa Cruz - 1 -

New diagnostics to assess the impact of satellite constellation for (sub)mesoscale

applications

Complementarity between SWOT and a large constellation of pulse-limited altimeters

M.I.Pujol, G.Dibarboure, G. Larnicol (CLS)

P.Y.Le Traon, P.Klein (IFREMER),


Introduction

• SWOT will provide an unprecedented sampling capability by 2019

• Iridium-NEXT telecommunication constellation renewed, starting from 2015

– Iridium satellites can take payloads of opportunity

– It is technically possible to have AltiKa-like pulse-limited altimeters on Iridium-NEXT

– The constellation itself would have intrinsic advantages (very cost efficient, temporal sampling, robustness vs failures, near real time…)

– But a constellation of traditional sensors cannot replace SWOT images

• What could be the benefits of having a constellation of 6 Iridium-NEXT altimeters (+upcoming missions) in addition to SWOT for (sub)mesoscale retrieval ?

Are Lagrangian diagnostics relevant for this study ?


OSSE approach

• Protocol: – Reality: Earth Simulator outputs from Ifremer: mesoscale and submesoscale

– Simulate observation by remote sensor (with error)

– Reconstruct « observed ocean topography » from profile/swath observations using optimal interpolation mapping (DUACS center to generate the AVSIO products)

• The difference between the reality and the observed state is the sum of :– Remote sensing sampling weaknesses (blind spots)

– Remote sensing measurement errors

– Reconstruction imperfections (e.g. oversmoothing)

• Error is always measured in percentage of the reality signal variance


Simulation details• Configurations studied :

– Classical nadir constellation: 3 x nadir altimeters (Jason-CS, Sentinel3-B, S3-C)– SWOT alone – SWOT + 3 altimeters – SWOT + 11 altimeters (6 Iridium, Jason-CS, S3-B, S3-C, HY-C, GFO2)

• Error levels: optimistic (both on SWOT and nadir altimetry)– only noise and residual roll for SWOT (after good cross-calibration)– 1 cm noise for nadir (radiometer, dual frequency/Ka, and good POD)

• Reconstructing the topography at each time step and position:– Straightforward optimal interpolation (no model + assimilation) derived from DUACS tools– Mapping #1: standard DUACS mapping 100 km / 10 days (mesoscale)– Mapping #2: 2-step optimal down to 30 km and 5 days (small mesoscale / submesoscale)– Regional reconstruction (not just within the swath temporal coherency analyzed)


Ocean reality

• One year of Earth Simulator from Ifremer (Klein et al) mesoscale and submesoscale

• Theoretical model: can be « projected » to any region or bathymetry configuration

North Pacific at two locations : [38°N,210°E] and [45°N,210°]

• RMS of the sea surface height anomaly:

TOTAL > 100km < 100km


Instantaneous observations (typical snapshot) 3 x Nadir 1 x SWOT

11 x Nadir(Iridium 6 + Jason-CS

+GFO2+ HYC+ S3A + S3B)

1 x SWOT +11 x Nadir

2 x SWOT


Objective Analysis (OA) method (Le Traon et al, 1998; Ducet et al, 2000) used for SLA reconstruction:

Large and medium mesoscale signal : direct OA with correlation scales 100km/10days

Short mesoscale signal : 2-step OA method with correlation scales 100km/10days and 30km/5days

Along-track total SLA field

Map of large/medium

mesoscale SLA

OA100km/10days

Map of residual short mesoscale

SLA

OA30km/5days

Map of large/medium+short

mesoscale SLA+

Along-track residual sub-

mesoscale SLA field

-

Method

Errors on reconstructed fields are analyzed for SLA, surface geostrophic velocities (U,V), vorticity and vertical velocities (W)

Map reconstructed with 1/8°x1/8° and 3 days

resolution.


Illustration of common diagnostics used for OSSEs• SSH reconstruction error (diff = reference – reconstructed)

• Analysis made at 38°N (SWOT temporal sampling is optimal)

• Mesoscale SSHA reasonably resolved with 3 satellites(current applications ofDUACS)

• SWOT alone performslike 4 altimeters

• Adding more sensorsreduces the error but the gain is small

SSHA Reconstruction error (% of reality signal variance)


Mesoscale sampling (influence of latitude)• Only one SWOT sensor results change with latitude

• Blue is for 38° (optimal temporal sampling : 1 sample every 11 days)

• Grey is for 45° (poor sampling : 2 samples in 4 days, then 18 days with no data)

• Sampling discrepancies disappear when a large constellation is addedDelta Time between ascending and descending arcs on SWOT x SWOT

crossovers

-10 days +10 days

38°

45°


Mesoscale sampling (geostrophic velocities)• Reconstruction error at 45°N on U (blue) and V (red) components• Observing true gradients is much more difficult, even on « simple » mesoscale• Second SWOT or constellation error divided by a factor of 2• Direct benefit for traditional altimetry applications at regional scale

Geostrophic velocities reconstruction error

(% of reality signal variance)


The lyapunov exponents

• Potential of using Lagrangian metrics to charactrise the impact of satellite constellation

•Test has been performed using a Lagrangian approach with the calculation of the Lyapunov exponents (FSLE for finite size Lyapunov exponents) of the velocity data set

direct measure of the local stiring characterise the trajectories of initially close particules that are

quickly separated along the streaching directions

• In practice: a set of tracers (initially separated with a specific distance) are followed in time during the advection by the velocity field.FSLE is the time it takes to the tracers to reach a given separation distance

Ref papers: D’Ovidio et al. (GRL, 2004), D’Ovidio et al. (DSR, 2009)D’Ovidio et al.., (2004) software is rewriting and will be available soon.


Reconstructing lyapunov exponents3 x Nadir 1 x SWOT 1 x SWOT +11 x NadirReality (Earth Simulator)


Do we need optimal 2-step mapping ? SWOT + 11 Nadir (standard)Reality (Earth Simulator) SWOT + 11Nadir (2-step)


Integrated advection error

Average error on tracer position

5 days

• Initial state : hundreds of particules to be advected

• Position analyzed every 3 hours over 5 days

• The mean distance between reference and observed trajectories

gives an estimate of the integrated error

• SWOT alone still has an average error (42km) superior to the mapping scales (30km) observation lacking

• Adding a second SWOT or (better)a constellation of 11 nadir reduces theerror by 50% (25km)


Do we need optimal 2-step mapping ?

• If SWOT is alone, the 2-step mapper significantly reduces the error at regional scale (optimal interpolation uses statistical decay between sparse images)

• When a constellation is merged with SWOT, the dense 1D profiles can preserve the SWOT 2D information until a new swath refreshes the scene improvements from 2-step mapping is marginal (standard maps are good enough if observation is not filtered)

SWOT (standard)SWOT (2-step)SWOT+11 Nadir (standard)SWOT+11 Nadir (2-step)


Conclusions : OSSE results

• A large altimetry constellation can complement SWOT images:• To fill SWOT temporal gaps between 2D images (with dense 1D profiles)

• To fill SWOT observation weaknesses at certain latitudes (22-day orbit)

To better observe smaller scales (error divided by a factor of 2 for signals > 30km)

• Optimal 2-step mapping (vs. traditional DUACS mapping) :• 2-step is not necessary for SWOT+constellation (dense measurements)

• 2-step is needed for SWOT alone to balance the sparser temporal observation (standard mapping would over-smooth between 2D images)


Conclusions on Lagrangian metrics

• Lyapunov exponents useful but qualitative further work need to understand the fiability/sensitivity of this Lagrangian method ?

(impact of the parameterisation, sensitivity to the sampling)

• Quantitative diagnostics with the Integrated advection error are satisfying.

• Are Lagrangian diagnostics relevant for a NRT monitoring (OSE)?

DUACS Global product DUACS régional AMESD


Conclusions on Lagragian metrics

• Lyapunov exponents useful but qualitative further work need to understand the fiability/sensitivity of this Lagrangian method ?

(impact of the parameterisation, sensitivity to the sampling)

• Quantitative diagnostics with the Integrated advection error are satisfying.

• Are Lagrangian diagnostics relevant for a NRT monitoring (OSE)Where is the truth ? How could we verify that the small scale introduced in the field are realistic ?

Is there specific signatures on FSLE/FTLE results of some specific signals? (internal wave, sampling discontinuity, ..),

Consistency with tracers like ocean colour ?

Interest to use Lyapunov exponent for model simulations intercomparison and validation ?

M.I.Pujol, G.Dibarboure, G. Larnicol (CLS) P.Y.Le Traon, P.Klein (IFREMER),

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Transcript of M.I.Pujol, G.Dibarboure, G. Larnicol (CLS) P.Y.Le Traon, P.Klein (IFREMER),