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

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.

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

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  1. New diagnostics to assess the impact of satellite constellation for (sub)mesoscale applicationsComplementarity 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),

  2. 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 ?

  3. 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

  4. 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)

  5. 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: < 100km TOTAL > 100km

  6. Instantaneous observations (typical snapshot) 11 x Nadir (Iridium 6 + Jason-CS +GFO2+ HYC+ S3A + S3B) 3 x Nadir 1 x SWOT 1 x SWOT + 11 x Nadir 2 x SWOT

  7. Method • 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 OA 100km/10days Map of large/medium mesoscale SLA Along-track total SLA field Map reconstructed with 1/8°x1/8° and 3 days resolution. Map of large/medium+short mesoscale SLA - + Along-track residual sub-mesoscale SLA field OA 30km/5days Map of residual short mesoscale SLA  Errors on reconstructed fields are analyzed for SLA, surface geostrophic velocities (U,V), vorticity and vertical velocities (W)

  8. 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)

  9. 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 added Delta Time between ascending and descending arcs on SWOT x SWOT crossovers 45° 38° -10 days +10 days

  10. 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)

  11. The lyapunov exponents • Potential of usingLagrangianmetrics to charactrise the impact of satellite constellation • Test has been performedusing a Lagrangianapproachwith the calculation of the Lyapunovexponents (FSLE for finite size Lyapunovexponents) of the velocity data set •  direct measure of the local stiring •  characterise the trajectories of initially close particules that are • quicklyseparatedalong the streaching directions • In practice: a set of tracers (initiallyseparatedwith a specific distance) are followed in time during the advection by the velocityfield. • FSLE is the time ittakes to the tracers to reach a givenseparation distance • Refpapers: D’Ovidio et al. (GRL, 2004), D’Ovidio et al. (DSR, 2009) • D’Ovidio et al.., (2004) software is rewriting and willbeavailablesoon.

  12. Reconstructing lyapunov exponents Reality (Earth Simulator) 1 x SWOT +11 x Nadir 3 x Nadir 1 x SWOT

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

  14. Integrated advection error • 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) Average error on tracer position 5 days

  15. Do we need optimal 2-step mapping ? SWOT (standard) SWOT (2-step) SWOT+11 Nadir (standard) SWOT+11 Nadir (2-step) • If SWOT isalone, the 2-step mapper significantlyreduces the erroratregionalscale (optimal interpolation uses statisticaldecaybetweensparse images) • When a constellation ismergedwith SWOT, the dense 1D profiles canpreserve the SWOT 2D information until a new swathrefreshes the scene improvementsfrom 2-stepmappingis marginal (standard maps are good enough if observation is not filtered)

  16. 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)

  17. 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 régional AMESD DUACS Global product

  18. 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 ?

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