Title: New diagnostics to assess the impact of satellite constellation for (sub)mesoscale applications Complementarity between SWOT and a large constellation of pulse-limited altimeters
1New 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),
2Introduction
- 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 ? -
3OSSE 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
4Simulation 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)
5Ocean 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 38N,210E
and 45N,210 - RMS of the sea surface height anomaly
lt 100km
TOTAL
gt 100km
6Instantaneous 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
7Method
- 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
Map reconstructed with 1/8x1/8 and 3 days
resolution.
? Errors on reconstructed fields are analyzed for
SLA, surface geostrophic velocities (U,V),
vorticity and vertical velocities (W)
8Illustration of common diagnostics used for OSSEs
- SSH reconstruction error (diff reference
reconstructed) - Analysis made at 38N (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)
9Mesoscale 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
10Mesoscale sampling (geostrophic velocities)
- Reconstruction error at 45N 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)
11The 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 DOvidio et al. (GRL, 2004), DOvidio
et al. (DSR, 2009) - DOvidio et al.., (2004) software is rewriting
and will be available soon.
12Reconstructing lyapunov exponents
Reality (Earth Simulator)
1 x SWOT 11 x Nadir
3 x Nadir
1 x SWOT
13Do we need optimal 2-step mapping ?
Reality (Earth Simulator)
SWOT 11Nadir (2-step)
SWOT 11 Nadir (standard)
14Integrated 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
15Do we need optimal 2-step mapping ?
SWOT (standard) SWOT (2-step) SWOT11 Nadir
(standard) SWOT11 Nadir (2-step)
- 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)
16Conclusions 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 gt 30km) - Optimal 2-step mapping (vs. traditional DUACS
mapping) - 2-step is not necessary for SWOTconstellation
(dense measurements) - 2-step is needed for SWOT alone to balance the
sparser temporal observation (standard mapping
would over-smooth between 2D images)
17Conclusions 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
18Conclusions 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 ? -