ESA Oceans2006 Hamburg © I. S. Robinson (2006)
SST Mesoscale ocean features 1
Ian RobinsonIan Robinson
2525--29 Sep, Hamburg29 Sep, Hamburg
ESA Training Course: Ocean2006 ESA Training Course: Ocean2006
National Oceanography Centre Southampton
Professor of Oceanography from Space, University of Southampton.Co-head of the Ocean Observations and Climate Research Group, NOCS
SST Observations of mesoscale ocean features
SST Observations of mesoscale ocean features
SST: Mesoscale features 225-29 Sep, HamburgESA Training Course: Ocean2006
Observing mesoscale ocean features in SST: Lecture outline
Observing mesoscale ocean features in SST: Observing mesoscale ocean features in SST: Lecture outlineLecture outline
Near-surface ocean thermal structure. Do satellites and ships measure the same SST and does it matter?What is ocean mesoscale variability and why is it important?Examples of mesoscale features in SST imageryObservational sampling needed to resolve the mesoscaleShould we use infrared sensors, microwave radiometers or blend the data?Case study of enhancing the view of an eddy in a single ATSR image
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
SST Mesoscale ocean features 2
SST: Mesoscale features 325-29 Sep, HamburgESA Training Course: Ocean2006
SST measured from spaceSST measured from space
The measured SST is the surface skin temperatureThe measured infra-red radiation is emitted by water within about
ten microns of the air-sea interface
It differs from “bulk” SST as typically measured from ships because of:-The THERMAL SKIN TEMPERATURE DEVIATION
Normally tends to lower the skin temperature below the bulk temperature
The DIURNAL THERMOCLINERaises the temperature above the bulk temperature
The presence of SURFACE FILMS and SLICKSMay raise or lower the temperature compared with the bulk
temperature
SST: Mesoscale features 425-29 Sep, HamburgESA Training Course: Ocean2006
The skin The skin -- bulk temperature differencebulk temperature difference
δT is typically 0.1 – 0.2 K (skin cooler than water 1 mm below surface,largely independent of day-night, sun or cloud.
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
SST Mesoscale ocean features 3
SST: Mesoscale features 525-29 Sep, HamburgESA Training Course: Ocean2006
Predicting the skin temperature Predicting the skin temperature deviationdeviation
A theoretical modelStrong turbulence weakens the skin effect and vice versaA strong heat flux strengthens the skin effect and vice versaSuggested model by Saunders (1967):
where ν = kinematic viscosity,k = molecular conductivity,λ* = a coefficient to be determined.U* = friction velocity (related to wind stress,
Estimates of λ* vary between 2 and 9, not a robust model.
δλ ν
TNQ
kU=
**
SST: Mesoscale features 625-29 Sep, HamburgESA Training Course: Ocean2006
Measurements of ∆T = TS - TbulkMeasurements of Measurements of ∆∆T = TT = TSS -- TTbulkbulk
0.2
0.4
0.0
-0.2-0.4
-0.65 10 150 20
Day data
T, K
ROSSA, AMT-3, Sep-Oct 1996ROSSA, AMT-7, Sep-Oct 1998CHAOS, May-June 1998
Geophys. Res. Let., 26, pp 2505-2508.
Measured by Donlon et al (1999), over the Atlantic Meridional Transect
5 10 150 20
0.2
0.0
-0.2
-0.4
-0.6
Night data
Wind speed at 10m, m/s
T, K
Wind speed at 10m, m/s
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
SST Mesoscale ocean features 4
SST: Mesoscale features 725-29 Sep, HamburgESA Training Course: Ocean2006
Defining Sea “Surface” TemperatureDefining Sea “Surface” TemperatureDefining Sea “Surface” Temperature
Skin
Sub-skin
SST types
Foundation
SST: Mesoscale features 825-29 Sep, HamburgESA Training Course: Ocean2006
The diurnal thermoclineThe diurnal thermoclineThe diurnal thermocline
Typical temperature structure in the top few metres of the sea
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
SST Mesoscale ocean features 5
SST: Mesoscale features 925-29 Sep, HamburgESA Training Course: Ocean2006
Tem
pera
ture
(o C)
Arabian Sea WHOI Mooring Data - Spring 1995(1mm data estimated using Fairall et al. (1996))
Thermal structure of top 5m (from sub-skin to 5m)
(From the PhD of A. Stuart-Menteth – who did this course in 1999)
Temperatures at all depths collapse overnight to the same value at dawn
The temperature at dawn (uniform through the top 5 m) is called the Foundation Temp.
(SSTfnd)
SST: Mesoscale features 1025-29 Sep, HamburgESA Training Course: Ocean2006
SST features in shelf seas SST features in shelf seas SST features in shelf seas
A and B are examples of diurnal warming
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 1125-29 Sep, HamburgESA Training Course: Ocean2006
Six Year Pathfinder Climatology of ∆T14:00-02:00 (18km)
(Stuart-Menteth et al., JGR, 2003)
Monthly mean ∆T Days p/m when ∆T > 0.5oC
SST: Mesoscale features 1225-29 Sep, HamburgESA Training Course: Ocean2006
Importance of the diurnal thermocline Importance of the diurnal thermocline for Rfor R--S of SSTS of SST
Develops during the day Surface temperature 0.5 to 1 K warmer in the early afternoon than the previous or subsequent night. Max amplitude 5 K
Varies with meteorological conditionsStrongest in summer (longer and more direct solar heating).Strongest in calm conditions.
Spatially variable within an imagePatchiness on daytime images - the so-called ‘afternoon effect’..Masks underlying meso-scale mixed-layer temperature patterns.
Introduces a warm bias to SST recordsEliminate by using only night-time images,Or ignore daytime images under particular conditions,Or predict and correct for the effect (difficult to do confidently).
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
SST Mesoscale ocean features 7
SST: Mesoscale features 1325-29 Sep, HamburgESA Training Course: Ocean2006
Ocean mesoscale variabilityOcean mesoscale variabilityOcean mesoscale variabilityWhat do we mean by the mesoscale?
The scale of ocean dynamical features that are controlled by geostrophySmallest size scale is defined by the Rossby radius of deformation, LRb
The distance a disturbance propagates in the time to reach geostrophic balanceEquivalent to the Baroclinic wave speed ×half pendulum dayDepends on mixed layer depth h1 and the density contrast ∆ρ across the thermoclineTypically 10 – 50 km
At large size scales other mechanisms start to control (e.g β-effect)Turbulent energy is trapped at this scaleFeatures stabilise: persist for days, weeks
fgh
LRb01 ρρ∆
=
SST: Mesoscale features 1425-29 Sep, HamburgESA Training Course: Ocean2006
The importance of mesoscale variabilityThe importance of mesoscale variabilityThe importance of mesoscale variability
It represents the 2-dimensional ocean turbulenceThe cause of the randomness of drifter tracks
It is a source of energy for mixing in the oceanEnhancing nutrient supply to the upper oceanVentilating below the thermocline
It is a source of sampling “noise” when mapping ocean propertieson the large (e.g. basin) scale
Creates problems for interpreting locally and sparsely sampled data Mesoscale variability grows out of the major ocean fronts
Contributes to cross-frontal transports of heat, etc.Ocean eddies allow strong heterogeneity to persist
Strong local fronts can form around the eddiesContributes to the patchiness of primary production
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 1525-29 Sep, HamburgESA Training Course: Ocean2006
A variety of ocean dynamical phenomenaA variety of ocean dynamical phenomenaA variety of ocean dynamical phenomena
SST measured by the AVHRR (infra-red) sensor. 8-day average at 9 km resolution on 15-22 March, 2001
E = eddy
SST: Mesoscale features 1625-29 Sep, HamburgESA Training Course: Ocean2006
Remote sensing of mesoscale eddiesRemote sensing of mesoscale eddiesRemote sensing of mesoscale eddies
Needs to detect a surface property disturbed by a mesoscale eddy:
Sea surface heightAltimetry – note that the independent geoid is not needed for observing variable signals of the SSH
Sea surface temperatureInfrared or microwave radiometer
Tracers visible in the water colour (e.g. chlorophyll, SPM etc)Ocean colour sensors
Surface roughness patterns associated with eddiesSynthetic aperture radar
Needs to sample at a space-time resolution which contains the eddy length and time scales.
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 1725-29 Sep, HamburgESA Training Course: Ocean2006
Examples of SST mesoscale featuresExamples of SST mesoscale featuresExamples of SST mesoscale features
Gulf Streammeanders
SST: Mesoscale features 1825-29 Sep, HamburgESA Training Course: Ocean2006
Gulf of Tehuantepec, MexicoGulf of Gulf of TehuantepecTehuantepec, Mexico, Mexico
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 1925-29 Sep, HamburgESA Training Course: Ocean2006
S.W.Indian Ocean66°E 76°E
SST: Mesoscale features 2025-29 Sep, HamburgESA Training Course: Ocean2006
Space-time scales of typical oceanic processes
SpaceSpace--time scales of typical oceanic time scales of typical oceanic processesprocesses
Lengthscale, km
Area scalekm2
0.001
0.01
0.1
1
10
100
1000
10000
1
104
108
10-4
10-2
102
106
0.01 0.1 1 10 100 103 104
Time scale, days1 10 years
Mesoscaleeddy
Shortest lengthScale to detect
Shortest timeScale to detect
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 2125-29 Sep, HamburgESA Training Course: Ocean2006
Space-time sampling capabilities of satellite imaging sensors
SpaceSpace--time sampling capabilities of satellite time sampling capabilities of satellite imaging sensorsimaging sensors
Lengthscale, km
Area scalekm2
0.001
0.01
0.1
1
10
100
1000
10000
1
104
108
10-4
10-2
102
106
0.01 0.1 1 10 100 103 104
Time scale, days1 10 years
MeteosatAVHRR
ATSR
SST: Mesoscale features 2225-29 Sep, HamburgESA Training Course: Ocean2006
Eddies in SST : Infra-red or microwave?Eddies in SST : InfraEddies in SST : Infra--red or microwave?red or microwave?
Infra-red radiometry offers the best spatial definitionDown to ~ 1 km
Microwave radiometry has much coarser resolution~ 60-80 km,Sampled on a 20 km gridProcessed on a 0.25 deg lat x long grid
Both have revisit intervals ~ 12 hoursCloud is the problem for IR
Occasional highly detailed cloud-free images – all detailsOtherwise resample onto composite 8-day 4km or 9 km grids
M/W less affected by atmosphereUse individual overpasses or 3-day composites
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 2325-29 Sep, HamburgESA Training Course: Ocean2006
A “clear” infra-red image sequenceA “clear” infraA “clear” infra--red image sequencered image sequence
nightnightnightnightnightnightnightnightnight
AVHRR-Pathfinder 4 km gridded data from podaac.jpl.nasa.gov
SST: Mesoscale features 2425-29 Sep, HamburgESA Training Course: Ocean2006
Infra-red SST off S. AfricaInfraInfra--red SST off S. Africared SST off S. Africa
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 2525-29 Sep, HamburgESA Training Course: Ocean2006
Microwave SST off S AfricaMicrowave SST off S AfricaMicrowave SST off S Africa
2005-Feb 24 2005-Feb 27 2005-Mar 02 2005-Mar 05 2005-Mar 08 2005-Mar 11 2005-Mar 14 2005-Mar 17 2005-Mar 20 2005-Mar 23 2005-Mar 26 2005-Mar 29 2005-Apr 01 2005-Apr 04 2005-Apr 07
SST: Mesoscale features 2625-29 Sep, HamburgESA Training Course: Ocean2006
Infra-red or Microwave ?InfraInfra--red or Microwave ?red or Microwave ?
Depends on the applicationSome situations look for fine detailed structureOther applications require gap-free data even though the resolution is poor
E.g. Hovmöller plots to detect moving eddies
2005
Feb
Mar
Apr
45 S0 E 40 E
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 2725-29 Sep, HamburgESA Training Course: Ocean2006
Tools for the future: SST AnalysesTools for the future: SST AnalysesTools for the future: SST AnalysesOvercome the cloud cover problem by blending data from several sources
Microwave and infraredPolar orbiter and geostationaryDual-view and single-view
Gaps need to be filled by optimal interpolationTo facilitate this, SST data must be provided with
Common format (e.g. netCDF)Error statistics (bias and standard deviation)Ancillary data (wind, insolation etc) to evaluate likelihood of diurnal warming, ice cover etc.
Bias corrections should be applied relative to a standardThis is done by the GODAE high resolution SST pilot project (GHRSST-PP) see http://www.ghrsst-pp.org
SST: Mesoscale features 2825-29 Sep, HamburgESA Training Course: Ocean2006
Example of analysis products
Example of Example of analysis analysis products products
See: http://www.hrdds.net
Bay of Biscay:
24th Sep 2006
AVHRR NARAMSREAATSR
OSTIA Analysis
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 2925-29 Sep, HamburgESA Training Course: Ocean2006
SST Analysis Biscay; 11 Sep 2006SST Analysis Biscay; 11 Sep 2006SST Analysis Biscay; 11 Sep 2006
AVHRR 01:29 AMSR 02:36
SEVIRI 08:48
SEVIRI 02:48 AMSR 05:18 SEVIRI 05:48
SEVIRI 11:49ATSR 09:11
SEVIRI 18:45
SEVIRI 14:49AVHRR 10:20
SEVIRI 20:56 SEVIRI 23:48ATSR 20:51 AVHRRI 21:30
OSTIA ANALYSIS 11 Sep 2006
SST: Mesoscale features 3025-29 Sep, HamburgESA Training Course: Ocean2006
Conclusion (almost)Conclusion (almost)Conclusion (almost)
Satellite SST measurements offer a sampling capability well matched to ocean mesoscale variability Infra-red images offer superb “snapshots” of the spatial structure of featuresTracking the time variability with IR is hindered by cloudMicrowave radiometry can effectively monitor the evolution of larger mesoscale phenomenaSST analysis of several data sources is improving, to the point where it will be able to monitor mesoscale featuresENVISAT’s AATSR has a key role to play in GHRSST-PP
Now we turn to practical issues of how to use Bilko to reveal mesoscale processes in individual SST images
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 3125-29 Sep, HamburgESA Training Course: Ocean2006
Example of an Eddy in ATSR-1 dataExample of an Eddy in ATSRExample of an Eddy in ATSR--1 data1 data
Histogram
CumulativeHistogram
Dark Bright
An image in need of enhancementAn image in need of enhancement
500 km square ATSR-1 image over the S.W. Atlantic, 13-10-92. 11µm brightness temp, nadir view
SST: Mesoscale features 3225-29 Sep, HamburgESA Training Course: Ocean2006
Try new look-up tablesTry new lookTry new look--up tablesup tables
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 3325-29 Sep, HamburgESA Training Course: Ocean2006
Image processing: change the colour palette to enhance the image
Image processing: change the colour Image processing: change the colour palette to enhance the imagepalette to enhance the image
The palette determines what colour or shade to paint each pixelBy changing the distribution of grey tones or colours, small variations of DN can be enhanced without remapping into new DNs.
SST: Mesoscale features 3425-29 Sep, HamburgESA Training Course: Ocean2006
Enhancement by colourEnhancement by colourEnhancement by colour
O 10 20 30Degrees C.
ESA Oceans2006 Hamburg © I. S. Robinson (2006)
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SST: Mesoscale features 3525-29 Sep, HamburgESA Training Course: Ocean2006
Filtering Images 1: SmoothingFiltering Images 1: SmoothingFiltering Images 1: Smoothing
Original 5 x 5 Median filter
SST: Mesoscale features 3625-29 Sep, HamburgESA Training Course: Ocean2006
Filtering images 2: edge enhancementFiltering images 2: edge enhancementFiltering images 2: edge enhancement
3 x 3 gradient (up-down)
3 x 3 Laplacian
Original minus Laplacian
Variance
Roberts Gradient
Sobel
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SST: Mesoscale features 3725-29 Sep, HamburgESA Training Course: Ocean2006
The last word !The last word !The last word !
This ATSR image speaks for itself!A beautiful picture
¼ million precise temperature measurements
Data source for a detailed oceanographic study of mesoscale variability
Thank-you ESA !
Thank-you RAL !
Thank-you project scientist David Llewellyn-Jones !