sst observations of mesoscale ocean features...sst mesoscale ocean features 1 ian robinson 25-29...

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




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


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.




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=

**


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




Defining Sea “Surface” TemperatureDefining Sea “Surface” TemperatureDefining Sea “Surface” Temperature

Skin

Sub-skin

SST types

Foundation


The diurnal thermoclineThe diurnal thermoclineThe diurnal thermocline

Typical temperature structure in the top few metres of the sea




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 features in shelf seas SST features in shelf seas SST features in shelf seas

A and B are examples of diurnal warming




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


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).




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∆

=


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




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


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.




Examples of SST mesoscale featuresExamples of SST mesoscale featuresExamples of SST mesoscale features

Gulf Streammeanders


Gulf of Tehuantepec, MexicoGulf of Gulf of TehuantepecTehuantepec, Mexico, Mexico




S.W.Indian Ocean66°E 76°E


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




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


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




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


Infra-red SST off S. AfricaInfraInfra--red SST off S. Africared SST off S. Africa




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


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




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


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




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


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




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


Try new look-up tablesTry new lookTry new look--up tablesup tables




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.


Enhancement by colourEnhancement by colourEnhancement by colour

O 10 20 30Degrees C.




Filtering Images 1: SmoothingFiltering Images 1: SmoothingFiltering Images 1: Smoothing

Original 5 x 5 Median filter


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




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 !

sst observations of mesoscale ocean features...sst mesoscale ocean features 1 ian robinson 25-29...

Documents