Ocean Motion From Space Exploit the synergy of SSH, SST and OC
to estimate improved ocean surface currents
Slide 2
Ocean Motion From Space Exploit the synergy of SSH, SST and OC
to estimate improved ocean surface currents The needs Measuring
ocean currents from space: existing approaches Limits of the
altimeter system Improving altimeter currents through the merging
with OC/SST data The Mercatini et al, 2010 approach The challenges
Workplan
Slide 3
Ocean Motion From Space The needs Ship routing Search And
Rescue coastal development and management Offshore operations
Maritime Pollution navigation Ice forecasting aquaculture fisheries
Climate change research
Slide 4
The GlobCurrent Project The GlobCurrent Project was kicked-off
in October 2013. The project has a 3-years duration and is
supported by the European Space Agency (ESA) under the Data User
Element (DUE) programme. The overall objective of the project is to
advance the quantitative estimation of ocean surface currents from
satellite sensor synergy and demonstrate impact in user-led
scientific, operational and commercial applications that, in turn,
will increase the uptake of satellite measurements. The project is
led by NERSC with expertise from four partners, i.e. IFREMER, CLS,
PML, ISARDSAT
Slide 5
Measuring ocean currents from space: existing approaches At the
moment there is no satellite observing system that provides direct
observations of the ocean surface currents. However, a large
variety of active and passive remote-sensing instruments have been
put into orbit in the last few decades providing continuous, global
information about the ocean. This includes altimeters,
scatterometers, Synthetic Aperture Radars (SAR), imaging
radiometers operating at different wavelengths, spectrometers.
Estimates of the surface current can be retrieved by transformation
of these satellite-measured quantities based on a range of
assumptions, feature tracking methods and empirical based retrieval
algorithms.
Slide 6
Measuring ocean currents from space: existing approaches
Altimetry, by providing global, accurate and repetitive
measurements of the Sea Surface Height (SSH), has been by far the
most exploited system for the study of the ocean surface currents
variability over the last 20 years ERS-1 ERS-2 1991-07 2000-03
1995-04 2011-07 ENVISAT2002-03 2012-04 GFO1998-02 2008-10
TOPEX1992-08 2005-10 JASON-12001-12 2013-07 JASON-22008-06 2015+
JASON-3 CRYOSAT-22010-04 2014+ SARAL2013-02 2018+ SENTINEL-3
GOCE2010 2013 GRACE2002-03 2014+
Slide 7
Theoretical Courses on Altimetry October 2012 - 7 - The ocean
Mean Dynamic Topography orbit hOhO hAhA =h O -h A h G The altimeter
Principle Geoid E = G + h Geoid: poorly known at short scales
Measured with altimetry at cm accuracy Signal of interest for
oceanographers Altimeter missions repetitivity = G + Altimeter Sea
Level Anomalies h P Example of Sea Level Anomaly after multimission
mapping
Slide 8
Theoretical Courses on Altimetry October 2012 - 8 - The ocean
Mean Dynamic Topography SSALTO DUACS: computed relative to a 7
years mean profile (P=1993- 1999) In order to reconstruct the
dynamic topography h from the Sea Level Anomaly h= the missing
component is: The ocean Mean Dynamic Topography (MDT) for the
period 1993-1999 The altimeter Principle = G + h Geoid: poorly
known at short scales Measured with altimetry at cm accuracy Signal
of interest for oceanographers Altimeter missions repetitivity = G
+ + Mean Dynamic Topography = Absolute Dynamic Topography h
Altimeter Sea Level Anomalies h 93-99
Slide 9
Theoretical Courses on Altimetry October 2012 - 9 - The ocean
Mean Dynamic Topography MSS 9399 m GEOID (t,x,y) = G(t,x,y) +
h(t,x,y) Calculating the ocean Mean Dynamic Topography: The direct
method P (x,y)= P (x,y) G(x,y) MDT=MSS GEOID
Slide 10
Theoretical Courses on Altimetry October 2012 - 10 - The ocean
Mean Dynamic Topography (t,x,y) = G(t,x,y) + h(t,x,y) Calculating
the ocean Mean Dynamic Topography: The direct method P (x,y)= P
(x,y) G(x,y) MDT 9399 =MSS 9399 GEOID cm
Slide 11
Theoretical Courses on Altimetry October 2012 - 11 - The ocean
Mean Dynamic Topography Calculating the ocean Mean Dynamic
Topography: The direct method MDT 9399 Rc=200 km Rc=300 km Rc=400
km Gaussian filter cm
Slide 12
Theoretical Courses on Altimetry October 2012 - 12 - The ocean
Mean Dynamic Topography MSS CLS_SHOM98 MSS OSU 95 MSS CNES_CLS_2011
MSS DTU10 MSS DNSC08 MSS CLS01 Calculating the ocean Mean Dynamic
Topography: The direct method 20 years of MSS improvements
Slide 13
Theoretical Courses on Altimetry October 2012 - 13 - The ocean
Mean Dynamic Topography RMS differences (in cm) between geoid
models and GOCE-TIM-R3 filtered at 100km (on oceans) Satellite-only
geoid models 19951999200320052010 120 98 50 44 5 ModelYearMax DO
Data GRIM4S4199570Geodetic satellites GRIM5S1199999Geodetic
satellites CHAMP3S200314033 months of CHAMP GGM02S/ EIGEN3S
20051502 years of GRACE ITG- GRACE201 0s 20101807 years of GRACE
GOCE2010- 2014 200- 250 2 months (R1) 6 months (R2) 1 year (R3) 2
years (R4) Calculating the ocean Mean Dynamic Topography: The
direct method 20 years of GEOID improvements
Slide 14
Theoretical Courses on Altimetry October 2012 - 14 - The ocean
Mean Dynamic Topography 1995 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination
Slide 15
Theoretical Courses on Altimetry October 2012 - 15 - The ocean
Mean Dynamic Topography 1999 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination
Slide 16
Theoretical Courses on Altimetry October 2012 - 16 - The ocean
Mean Dynamic Topography 2003 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination
Slide 17
Theoretical Courses on Altimetry October 2012 - 17 - The ocean
Mean Dynamic Topography 2005 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination
Slide 18
Theoretical Courses on Altimetry October 2012 - 18 - The ocean
Mean Dynamic Topography 2006 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination
Slide 19
Theoretical Courses on Altimetry October 2012 - 19 - The ocean
Mean Dynamic Topography 2009 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination
Slide 20
Theoretical Courses on Altimetry October 2012 - 20 - The ocean
Mean Dynamic Topography 2010 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination GRACE: Resolution 150 km
Slide 21
Theoretical Courses on Altimetry October 2012 - 21 - The ocean
Mean Dynamic Topography 2010 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination GRACE: Resolution 125 km
Slide 22
Theoretical Courses on Altimetry October 2012 - 22 - The ocean
Mean Dynamic Topography 2012 20 YEARS OF Geoid IMPROVEMENTS: Impact
on MDT determination GOCE: Resolution 125 km
Slide 23
Theoretical Courses on Altimetry October 2012 - 23 - The ocean
Mean Dynamic Topography MSS CLS01-EIGEN - GRGS.RL02 300 km 2009: 5
years of GRACE data Example in the Gulfstream Region Mean
Geostrophic Velocities In-Situ mean velocities
Slide 24
Theoretical Courses on Altimetry October 2012 - 24 - The ocean
Mean Dynamic Topography 2009: 5 years of GRACE data In-Situ mean
velocities SMO CLS01-EIGEN-GRGS 133 km Example in the Gulfstream
Region Mean Geostrophic Velocities
Slide 25
Theoretical Courses on Altimetry October 2012 - 25 - The ocean
Mean Dynamic Topography In-Situ mean velocities MSS CLS10 -
EGM-DIR-V3 100 km 2011: 1 year of GOCE data Example in the
Gulfstream Region Mean Geostrophic Velocities
Slide 26
Theoretical Courses on Altimetry October 2012 - 26 - The ocean
Mean Dynamic Topography (u,v) At each position r and time t for
which an oceanographic in-situ measurement of surface velocity
u(r,t),v(r,t) is available - the in-situ data is processed to match
the physical content of the altimeter measurement (geostrophic
currents) - the altimeter velocity anomaly is subtracted from the
in-situ velocity Computing the ocean Mean geostrophic velocities
from in-situ and altimeter data (=synthetic mean velocities) cm/s
SVP- Drifting buoy velocities 1993-2010 - the altimeter velocity
anomaly u a (r,t),v a (r,t) is interpolated to the position/date of
the in-situ data. (u a,v a )
Slide 27
Mean geostrophic currents from the MDT CNES-CLS13 (Rio et al,
in preparation)
Slide 28
Altimetry Topex-Poseidon/Jason ERS/Envisat Multi-mission
merging to obtain gridded maps of altimeter data. Final resolution
of gridded products depends on the number of satellites flying at
the same moment and their orbit characteristics Impact the spatial
coverage capability: Spatial coverage limited to 66N/S for
TP/Jason, 82N/S for ERS/Envisat Impact the spatial and temporal
resolution capability Orbit inclinationRepetitivityEquatorial
inter- tracks distance 6610 days315 km 98.535 days80 km Maps of
surface velocities using the geostrophic approximation:
Slide 29
Altimeter Constellation history Spatial resolution achieved for
a 10 days temporal resolution 2 satellites 250-300km 3 satellites
150 km 4 satellites 100 km
Slide 30
Time-space diagram depicting the characteristic temporal and
spatial scales of the dominant surface current features in the
ocean. Adapted from Chelton, (2001). The dashed lines indicate the
approximate lower bounds of the space and tie scales that can be
resolved in SSH fields constructed from multimission altimeter
measurements (3 satellite configuration) Altimetry Limitation
Spatio temporal resolution achievable Physical content of the
currents: only the geostrophic component is obtained Error
increases toward the coast and at high latitudes (seasonal ice
coverage, limited satellite coverage)
Slide 31
Measuring ocean currents from space: Use of tracer information
TypeSatellite Platform(Instrument) Satellite observation Retrieval
algorithms and Tools Final retrieved geophysical information IR
radiometerMetOp1,2(AVHRR) ERS1-2( ATSR) ENVISAT(AATSR) EOS-
Aqua/Terra/(MODIS) NOAA(AVHRR) TRMM(VIRS) MeteoSat(SEVIRI)
Sentinel-3 (SLSTR) Sea surface temperature and fronts (i) Surface
quasi- geostrophic assumption; (ii) MCC method Surface geostrophic
current at spatial resolution of ~25km and temporal scale of about
1 to 10 days depending on clouds Feature tracking using MCC gives
estimate of surface current at resolution lower that interrogation
window C-band microwave radiometer Aqua-2(AMSR) DMSP (SSM/I)
Coriolis (WindSat) Sea surface temperature and fronts Surface
quasi- geostrophic assumption; Surface geostrophic current at
spatial resolution of ~25 km and temporal scale of 1 day L-band
microwave radiometer SMOS(MIRAS) SAC-D(Aquarius) Sea surface
salinity and fronts Surface quasi- geostrophic assumption; Surface
geostrophic current at spatial resolution of ~100 km and temporal
scale > 3-10 days SpectrometerENVISAT(MERIS)
EOS-Aqua/Terra(MODIS) Sentinel-3 (OLCI) Ocean color, chlorophyll A
distribution and fronts MCC methodFeature tracking using MCC gives
estimate of surface current
Slide 32
TypeSensorModalityResolution Spatial; Temporal Coverage
Spatial; Temporal Potential usage Geo- stationary SEVIRIInfrared,
visible3 Km; 15 minutes Atlantic; 2002-present SST and colour
feature GOCIOcean colour500m; hourly Northwest Pacific ;
2010-present Colour feature MTSATInfrared, visible4 Km; hourly
Pacific; 2005-present SST and colour feature GOESInfrared, visible4
Km; hourly Pacific; 1980-present SST and colour feature
PolarAVHRRInfrared1.1 km; few per day Global; 1981-present SST
feature SeaWIFSOcean colour1.1 km; daily Global; 1997-2011 Colour
feature ATSRInfrared1km, every 3 days1991-2011SST feature
MERISOcean colour300m/1200m, every 1-2 days? 2002-2011Colour
feature MODISInfrared, ocean colour 1.1 km (1 channel at 250m);
daily Global; 2002-present Colour feature MCC methodology From
http://ccar.colorado.edu/colors/mcc.html
Slide 33
MCC methodology From http://ccar.colorado.edu/colors/mcc.html
The solid boxes in the first image is the pattern to search for in
the second image. The dashed boxes in the second image is the
search window". The MCC method (Emery et al, 1986) is an automated
procedure that calculates the displacement of small regions of
patterns from one image to another. The location of the subwindow
in the second image that produces the highest cross-correlation
with the subwindow in the first image indicates the most likely
displacement of that feature. The velocity vector is then
calculated by dividing the displacement vector by the time
separation between the two images.
Slide 34
Limitations Cloud-cover and isothermal/isochromatic ocean
surface conditions drastically limit the spatial and temporal
velocity coverage provided by the MCC method. Clouds block the
ocean surface in both thermal and ocean color imagery, and there
are no features for the MCC method to track in
isothermal/isochromatic regions. Accurate spatial alignment and
coregistration of the imagery used in feature tracking is required.
Consequently, the technique has been more often used in coastal
regions, where landmarks are available to renavigate the satellite
data. MCC techniques work well for intervals between images of 6-24
hrs, but are not so reliable for longer gaps due to evolution of
the features, including rotation and shear. MCC methodology From
http://ccar.colorado.edu/colors/mcc.html
Slide 35
SST 1/4 (AMSR-E V5)SSH 1/3 (SSALTO/DUACS) General concept:
Exploit the correlation between SST and SSH (Jones et al. 1998) : -
Positive (0.2-0.6) at large scale (>1000km) - Strong (0.7) at
short scales in strong gradients areas Key point of SQG theory: in
the horizontal Fourier transform domain StreamfunctionSurface
Density Anomalies Currents SST anomaly E-SQG Method N(z)=N eff the
effective Brunt-Vaisala frequency (constant stratification
assumed)
Slide 36
Limitations The SQG method is valid in baroclinic instabilities
areas, and strong gradients areas (ACC, Gulfstream, Kuroshio) In
addition, the validity of the SQG approximation is limited to cases
when the SST is a good proxy of the density anomaly at the base of
the mixed layer, which effectively happens after a mixed-layer
deepening period. Therefore the ideal situation for the application
of this method would be after strong wind events. Limited to the
retrieval of mesoscale (30-300km), not the large scale currents
Isern-Fontanet et al, 2006, 2008: the reconstruction can be still
improved if we exploit the synergy between SST and SSH: Courants
issus de SSHCourants issus de SST E-SQG Method
Slide 37
On the use of submesoscale information for the control of ocean
circulations, Gaultier L., Verron J., Brankart J.M., Titaud O. and
Brasseur P. Journal of Marine Systems, 2013 FSLE (in day1) derived
from the geostrophic AVISO velocity first week of July, 2004 Tracer
used as a dynamic informa- tion: high resolution SST images from
MODIS sensor at 1 km resolution: Background velocity to be
corrected: map of velocity derived from AVISO altimetric
observations at 1/8 resolution : Finite-Size Lyapunov Exponents
(FSLE) measure stirring in a fluid, It is a connection between sub-
mesoscale dynamics and tracer stirring. FSLE is the exponential
rate at which two particles separate from a distance 0 to f :
Method: Modify the altimeter velocity field so as to minimize the
cost function J that measures the distance between the binarized
normalized gradient of SST and the binarized FSLE (u) (as a proxy
of the velocity u). SST image from MODIS sensor (in C) on July 2nd,
2004 Geostrophic Velocity (in m.s1) over the SSH (in cm) on the
first week of July, 2004 from AVISO.
Slide 38
Ocean Motion From Space Exploit the synergy of SSH, SST and OC
to estimate improved ocean surface currents SSH SST OC
Slide 39
Starting point: Inverse methods (Kelly et al, 1989): Require
the velocity field to obey the tracer evolution equation and
inverse it for the velocity vector: c(x, y, t) is the normalized
concentration of a tracer known from successive satellite
observations u,v is the velocity field f(x,y,t) represents the
source and sink terms Challenge: there is no contribution to tracer
advection from the along-track gradient velocity, or from either
components in regions of negligible gradients. -> only
cross-gradient velocity information can be retrieved from the
tracer distribution at subsequent times in strong gradients areas.
Ocean Motion From Space Exploit the synergy of SSH, SST and OC to
estimate improved ocean surface currents
Slide 40
Solution from Mercatini et al, 2010 : Use a background velocity
information (u bck, v bck ) so that the satellite tracer
information is used to obtain an optimized blended velocity (u opt,
v opt ). The authors tested the implementation of the method for
the specific application of oil spill monitoring using background
velocities from a numerical model. Ocean Motion From Space Exploit
the synergy of SSH, SST and OC to estimate improved ocean surface
currents
Slide 41
Apply the methodology on successive Ocean Color and SST images
using the low resolution, geostrophic altimeter velocities as
background velocities. -> Altimeter velocities will be improved
in term of resolution by the higher resolution information
contained in the SST/OC images -> Altimeter velocities will be
improved in term of physical content (ageostrophic terms) by the
total velocity information derived from the SST/OC images -> The
application of the method on both OC and SST images increase the
overall spatial and temporal velocity coverage. In addition, ocean
colour can also resolve isothermal flow, i.e. in regions where the
different water masses do not have distinctive temperatures. Ocean
Motion From Space Exploit the synergy of SSH, SST and OC to
estimate improved ocean surface currents Our Approach
Slide 42
Two main issues: Need for high spatial and temporal resolution
time series of cloud free images. Visible/infrared spectral band
radiometers: very high spatial resolution (0.3-1km, 4km in the case
of geostationary satellites) but their availabilities are severely
limited by the presence of clouds. Microwave imagers provide
accurate satellite SST measurements under clouds but the spatial
resolution is much lower (about 50 km). -> merging techniques to
compute cloud-free images of SST and Ocean colour from the
different available datasets. The drawback of these merged products
is that adjacent pixels may have been actually measured at
different times and from different sensors. The impact on the
accuracy of the derived velocities will need to be carefully
assessed. Accurate prescription of the Source and Sink terms of the
evolution equation is needed in cases where the tracer evolution is
not driven only by advection. SST Insolation net infrared radiation
sensible and latent heat fluxes. -> These terms may be measured
directly or indirectly via a Bulk Formulae. OC The main contributor
to OC is phytoplankton, which is both a passive tracer transported
by water circulation and an active biomass growing under favorable
conditions (light, nutrients, etc.) Ocean Motion From Space Exploit
the synergy of SSH, SST and OC to estimate improved ocean surface
currents
Slide 43
The applicability of the method will be tested using an OSSE
(Observing System Simulation Experiment) based on ocean numerical
modeling outputs SST test, OC test : Test a number of
configurations regarding the SST and OC observing systems:
different time acquisition of adjacent pixels in high resolution
SST and OC merged products different parameterizations for the
source and sink terms of the tracer evolution equation impact of
growing time steps between two consecutive images Model outputs
Nominal conf SST m Oc m U m,V m Degraded Model outputs SST test OC
test MER10 Altimeter data U bck, V bck Blended Velocities U opt, V
opt Ocean Motion From Space Exploit the synergy of SSH, SST and OC
to estimate improved ocean surface currents
Slide 44
Task 1 Gaining expertise on Sea Surface Temperature and Ocean
color measurements from space Task 1.1: Bibliographical work Task
1.2: Intercomparison of merged images and Level 3 single sensor
images in term of spatial/temporal resolution Task 1.3: Analysis of
the different driving mechanisms of the SST and Ocean color
evolution, and their relative contribution (advection versus
diffusion versus ) Task 1.4: Selection of areas/periods with
successive cloud free high resolution images Task 2 Observation
System Simulating Experiments Task 2.1: Use of model fields to
create synthetic maps of Sea Surface Temperature/Ocean color Task
2.2: Implement the MER10 methodology to create blended velocities
from altimetry and synthetic SST/Ocean color images Task 2.3:
Sensitivity studies Task description Ocean Motion From Space
Exploit the synergy of SSH, SST and OC to estimate improved ocean
surface currents
Slide 45
Task 3: Implementation and testing of the method on real
datasets Task3.1: Testing the method capacity of retrieving
shortest scales of the geostrophic currents we will consider a
study period for which four altimeters have been flying
simultaneously (2002-2008) and for which therefore altimeter sea
level maps (and the corresponding geostrophic velocities) have been
computed using the four altimeter data measurements. Concurrent
maps computed using only 2 altimeters out of the four available
will be used as background velocities (with therefore a decreased
spatial resolution) and the methodology will be applied using high
resolution SST and OC maps. We will check that the resulting
blended surface velocities compare better to the full resolution,
four altimeter data based geostrophic velocities than the
background velocities. Task3.2: Testing the method capacity of
retrieving the ageostrophic component of the currents We will apply
the methodology using the altimeter surface velocities as
background for a given study period and compare the resulting
blended velocities to the total velocities provided by the OSCAR
(Bonjean et al, 2002) or SURCOUF (Larnicol et al, 2005) products
that also include the Ekman current component. Task description
Ocean Motion From Space Exploit the synergy of SSH, SST and OC to
estimate improved ocean surface currents
Slide 46
Task 4: Organization of the OceanMotionFromSpace workshop Task
5: Validation Task 5.1: Intercomparison to MCC methodology Task
5.2: Intercomparison to SQG methodology Task 5.3: Intercomparison
synthesis Task 6: Outreach activity Task 7: Scientific paper
writting Task description Ocean Motion From Space Exploit the
synergy of SSH, SST and OC to estimate improved ocean surface
currents
Slide 47
Task 1 Task 2 Task 3 Task 4 Task 5 Task 6 2014
marsmaijuil.sept.nov. 2015 janv.marsmaijuil. 2015 End of project
31/8/2015 Soul Food Festival 15/5/2015 OceanMotionFromSpac e
Workshop 15/3/2015 Project Presentation Seminar 15/3/2014 Beginning
of project 1/3/2014 31/8/2015 1/1/201515/3/2015 1/1/201531/8/2015
1/9/20141/3/2015 1/4/20141/10/2014 1/3/20141/3/2015 Gantt Diagram 9
months Ocean Motion From Space Exploit the synergy of SSH, SST and
OC to estimate improved ocean surface currents