2013 Microwave Ocean Remote Sensing Training School
Sea Surface Temperature
Jordi Isern-Fontanet
Institut Català de Ciències del Clima
Motivation
•
SST is an important indicator of the state of a climate system
•
It is also a key variable for establishing atmosphere and ocean boundary conditions
•
SST affects atmosphere-ocean coupling, which includes water circulation and heat
and gas exchanges.
•
SST can be used to derive ocean currents.
Highly accurate SST data are required for climate monitoring, for atmospheric and oceanic
simulations and operational applications.
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Large scale patterns of SST
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ENSO
•
Coupled ocean-atmosphere phenomenon.
•
Peridicity: 2 to 7 years.
•
Recent episodes: 1997-1998, 2002-2003, 2006-2007, ...
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Mesoscale variability of SST: microwave observations
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Submesoscale variability of SST: infrared observations
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Ocean time and scales
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The thermocline structure of the ocean
•
Mixed Layer Depth has a strong seasonal signature
◦
•
SST is representative of different depths depending on the period of the year.
The vertical structure also varies with latitude.
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Spatial variability of the Mixed Layer
Isern-Fontanet et al. JGR 2008
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The diurnal thermocline
Robinson 2004
Price et al. JGR 1986
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Foundation temperature
•
It is defined as the temperature at the first time of the day when the heat gain from
the solar radiation absorption exceeds the heat loss at the sea surface
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The thermal skin layer
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Heat fluxes
30 < QS < 260 Wm−2
−60 < QIR − QG < −30 Wm−2
−42 < QH < −2 Wm−2
−130 < QL < −10 Wm−2
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Remote sensing of the oceans
Robinson 2004
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Blackbody radiation
•
Spectral emitance at wavelength λ emitted by body in thermodynamic equilibrium at
temperature T (Planck’s law)
2hc2
1
Mλ(T ) = 5
λ exp hc − 1
k λT
(1)
B
◦
Planck constant
h = 6.62606957(29) × 10−34Js
◦
Boltzmann constant
kB = 1.3806488(13) × 10−23JK−1
◦
speed of light
c = 299, 792, 458ms−1
Robinson 2009
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Importance of clouds
Chelton and Wentz, BAMS, 2005
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Approximations for the microwave band
•
Spectral emitance at frequency f emitted by body in thermodynamic equilibrium at
temperature T (Planck’s law)
2hf 3
1
B̂f (T ) = 2
c exp hf − 1
k T
(2)
B
•
At microwave frequencies
hf
exp
kB T
•
hf
≈1+
kB T
(3)
Rayleigh-Jeans approcimation:
2kB f 2T
B̂f (T ) ≈
c2
(4)
The emitted radiance is directly proportional to the temperature of the emitting
surface.
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Emissivity
•
Seawater is not a black-body and Planck’s law has to be corrected introducing the
emissivity ε
2kB f 2T
Bf (T ) = εB̂f (T ) ≈ ε
c2
(5)
•
The temperature retrieved without taking into account ε is known as Brightness
Temperature TB
•
It is necessary to know the emissivity to retrieve the temperature of the emitter, which
depends on the viewing angle θ, the polarization H, V and the dielectric constant for
sea water e(f )
εH,V = 1 − ρ2H,V
(6)
p
cos θ − e − sin2 θ
p
=
cos θ + e − sin2 θ
(7)
p
e cos θ − e − sin2 θ
p
ρV =
e cos θ + e − sin2 θ
(8)
ρH
Robinson 2004
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Dependence with wind
•
•
The roughness of the sea surface creates fluctuations in the incidence angle within the
footprint of the radiometer.
◦
The resulting brightness depends on the sea surface slope statistics.
◦
Since this is controlled by wind stress, it contains information about winds.
◦
An effective emissivity ε̂ can be introduced to take roughness into account.
The presence of foam also modifies the emissivity.
◦
The presence of foeam also depends on wind stress.
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The dielectric constant of sea water e
•
The emissivity not only depends on the viewing angle but also with e.
es − e∞
σ
e(f ) = e∞ +
−i
,
1 + i2πf τr
2πf ε0
(9)
valid for f < 85GHz.
◦
ε0 is the permittivity of free space
◦
e∞ is the dielectric constant at very high frequency
◦
es is are the dielectric constant at zero frequency
◦
τr is the relaxation time
◦
•
σ is the ionic conductivity







Depend on T and S






The consequent dependence of ε on T implies that the relation between microwave
brightness and T is not linear.
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Penetration depth
•
The penetration depth in the microwave range
is given by
√
2πf e
d(f ) =
c
•
Consequently, it depends on T and S
•
Penetration depth for IR (λ = 12µm)
Hasoda, JO, 2010
(10)
dIR ∼ 4µm
•
Penetration depth for MW (f = 5GHz)
dM W ∼ 5mm
•
Thickness of the skin-layer
dT ∼ 100µm
Robinson 2004
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Distinguishing different types of SST
•
SSTint: hypothetical temperature at
the exact air-sea interface
◦
•
SSTskin: temperature within the
conductive diffusion-dominated sublayer ∼ 10 − 20µm
◦
•
https://www.ghrsst.org/
Well approximated to the measurement by a MW radiometer (6-11 GHz)
SSTdepth: temperature measurements beneath the SSTsubskin
◦
•
Measured by an IR radiometer
SSTsubskin: temperature at the base
of the conductive laminar sub-layer of
the ocean surface
◦
•
No practical use.
Wide variety of platforms and sensors and distinct from those obtained using
remote sensing techniques
SSTfnd: temperature free of diurnal temperature variability
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Sensitivity to SST
•
The sensitivity of microwave
radiance to SST is necessary to set
the mounted channel at the most
sensitive frequency.
dTB
dT
Hasoda, JO, 2010
(11)
•
Maximum sensitivity within the
range 4–10 GHz.
•
The higher frequency channels of microwave radiometers (10 GHz) are less sensitive
to SST change in the low SST range.
•
The optimal frequency range was 4–6 GHz for all-range SST estimation.
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Effect of the atmosphere
•
•
The brightness temperature TB as
seen from the antenna has three
contributions:
Robinson 2004
◦
SST Ts
◦
Cold space Tc = 2.7K
◦
Atmosphere TatmU and TatmD , which depend on the temperature profiel Ta(z)
The resulting brightness temperature is given by
TB = TatmU + τH εTs + τH R(1 + Ω)(TatmD + τH Tc)
(12)
where R = 1 − ε is the reflectivity, Ω accounts for the additional reflectivity due sea
state and τH is the transmittance.
•
Transmittance depends on oxygen, water vapor and cloud liquid water.
•
Rain absorbs and scatters microwave radiation and makes impossible the retrieval of
SST.
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Attenuation
Robinson 2004
•
The spectral dependency of the attenuation due to oxygen (- - -), water vapor (—) and
cloud water (...) allows to develop corrections based on multispectral measurements
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Summary of contributions to TB
Robinson 2004
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Retrieval of SST: Wentz and Meissner’s algorithm
•
Proposed by Wentz and Meissner 2000 and updated by Wentz and Meissner 2007
•
Based on the Radiative Transfer Model (RTM)
◦
Atmospheric absorption model for water vapor, oxygen and liquid cloud water
◦
Sea surface emissivity model that depends on SST, SSS, sea surface wind speed
and direction
•
The RTM is used to Monte Carlo simulate TB at the Top of the Atmosphere (TOA)
•
Simulated TB are then used to train a two-stage regressions to retrieve SST.
•
◦
First-stage uses a set of regressions trained with global data: good but not
optimal
◦
Second-stage uses a set of regressions trained for localized range of environmental
conditions around the first-stage retrievals.
This algorithm is the basis of the algorithm used by REMSS to retrieve SST from
TMI, AMSR-E and WindSat.
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AMSR
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AMSR-E on October 15 2007
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SST measured by TMI, AMSR-E and WindSat on October 15 2007
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Spatial resolution
•
The footprint ∆ is given by
cH
∆=
Df
(13)
where D is the antenna diameter and H
the satellite altitude.
•
The spatial resolution of the output will
depend on the dominant band for a
given product
Robinson 2004
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Example: hurricane Bonnie
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Applications: ocean currents
Isern-Fontenet et al. GRL 2006
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Main microwave radiometers
1
Sensor acronym
Platform(s)
Agency
Dates
Channels
Cent. freq.
Polarization
GHz
Main data products
TMI
TRMM
NASA/
JAXA
11/1997-
AMSR
ADEOS-2
JAXA/
NASA
12/2002-10/2003
AMSR-E
Aqua
5/2002-10/2011
AMSR-2
GCOM-W
5/2012-
SST
Wind speed
Water vapor
Cloud liquid water
Rain rate
SST
Wind speed
Atmos. water vapor
Cloud liquid water
Rain rate
Sea ice
WindSat
Coriolis
10.7
19.4
21.3
37.0
85.5
6.925
10.65
18.7
23.8
36.5
89.0
50.3/52.81
6.8
10.7
18.7
23.8
37.0
U.S. DoD
1/2003-
V, H
V, H
H
V, H
V, H
V, H
V, H
V, H
V, H
V, H
V, H
V
V, H
FP
FP
V, H
FP
SST
Wind speed
Wind direction
AMSR only
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Further reading
Papers:
•
Hasoda, K. (2010). A review of satellite-based microwave observations of sea surface
temperatures. Journal of Oceanography, Volume 66, Issue 4, pp 439-473
Web:
•
http://www.remss.com/
•
https://www.ghrsst.org/
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Data sources
•
•
•
Remote Sensing Systems: http://www.remss.com/
◦
AMSR-E, TMI and WindSat
◦
L3 data
GCOM-W1 Data Providing Service: https://gcom-w1.jaxa.jp/auth.html
◦
AMSR, AMSR-E, AMSR-2
◦
L1B, L1R, L2 and L3 data
National Snow & Sea Ice Centre:
ftp://sidads.colorado.edu/pub/DATASETS/nsidc0302 amsre qtrdeg tbs/
◦
AMSR-E
◦
L1B and L2 data
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Practical: basic examples
•
Before you start you have to load the environmental variables
IDL> @startup
•
Run example 1: explore the fields available in a AMSR-E L3 file from REMSS
IDL> example1
•
Run example 2: compare AMSR-E L3 and TMI L3 files from REMSS
IDL> example2
•
Run example 3: explore averaged AMSR-E L3 files from REMSS
IDL> example3
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Practical: additional examples
•
Explore AMSR-E L2 field
data/AMSR E L2A BrightnessTemperatures V10 200710150436 D.hdf
•
Explore AMSR-2 sample data
http://suzaku.eorc.jaxa.jp/GCOM W/data/data w sampledata.html
•
Explore WindSat data and REMSS products and tools
ftp://ftp.remss.com/windsat/
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