OPTIMAL ESTIMATION TECHNIQUE FOR SST FROM MTSAT-2
Yukio Kurihara, Meteorological Satellite Center (MSC) / JMA, Kiyose-city, Tokyo, Japan
yukio.kurihara-a@met.kishou.go.jp
4a. MTSAT-2 versus buoy
MTSAT-2 SSTs are compared
p
with buoy
y SSTs. To reduce
the cloud screening errors and evaluate the effect of the
new algorithm, robust statistics technique is adopted for
the validation. Statistics are calculated from the 50% of
match-ups centered at median. Though this comparison
shows a good agreement between them, bias of -0.31 K
in summer ((July
y and August)
g ) and +0.21 K in winter
(January and February) are also shown. Furthermore,
bias slightly decreasing in higher range of buoy SST is
found. Problems with background values and/or
calibration (MIttaz et. al, 2011)
are possible for the reason
of these biases.
MTSAT-2 SST as a function of
buoy SST. SSTs in July and
August in 2011 are plotted in
magenta and blue, and red and
green show the SSTs in January
and Februaryy in 2012.
N
July
August
total
26106
21757
47863
N
January
February
total
37058
31910
68968
RMSD
(K)
0.43
0.41
0.42
BIAS
(K)
-0.32
-0.29
-0.31
STD
(K)
0.28
0.29
0.29
RMSD
(K)
0 35
0.35
0.38
0.36
BIAS
(K)
+0 18
+0.18
+0.24
+0.21
STD
(K)
0 30
0.30
0.30
0.30
Robust statistics of MTSAT
MTSAT-2
2 SST by comparing with buoy
measurements (MTSAT-2 minus buoy) for July and August in 2011
(top) and January and February in 2012 (bottom). RMSD, bias and
STD were calculated from the 50% of match-ups centered at
median.
di
Robust bias and STD as a function of buoyy SST ((left)) and
satellite zenith angle (right)
T10.8 -T12.0
is generated using annual observations and JMA NWP
outputs from July 2010 to June 2011. Ta and UH2O are
calculated from 10.8 and 12.0 micron radiance and buoy
SST by solving simultaneous equation of (5).
(5) Buoy SST
and calculated Ta and U H2O are gathered into 3dimensional bins with a bin interval of 1 K for 10.8
micron brightness temperature corresponding to 10.8
10 8
micron radiation intensity, 0.1 K
for the difference of brightness
temperature (10.8
(10 8 micron minus
12.0 micron) and 1 degree for
the satellite zenith angle. Then
bin-averaged SST (Ts),
) Ta and
T
UH2O are compiled into a look up
LUT of background values
table (LUT) for the background
values of analysis parameters.
parameters
10.8
6) Error covariance matrix of background values (B) is
generated from their standard deviations (STD)
calculated
l l t d for
f each
h bins
bi off LUT.
LUT It is
i assumed
d that
th t the
th
error covariance between different parameters is always
zero. Error covariance matrix of MTSAT-2 data (R) is
generated using uncertainty of radiance which is
3) Sensor Planck function (Br) is an approximation assumed to be equal to radiation intensity
function which takes into account the spectral response of corresponding to 0.2 K of brightness temperature. Error
each channel and calculates radiance from brightness covariance between different channels is assumed to be
-1 and R-1 in (1) are the inverse matrixes of B and
zero.
B
temperature.
R, respectively.
4) For the calculation of the atmospheric transmittance (τr),
only the contribution of water vapor is taken into account.
τr is calculated using (6) - (8) (Roberts et. al, 1976). To cut
down the total number of variables, a parameter UH2O
defined by (9) is introduced. Then transmittance is
calculated using (10).
2) Sea surface emissivity (εr) is calculated with the
Isotropic Gaussian (IG) model with the Surface-emitted
S f
Surface-reflected
fl t d (SESR) emission
i i
b Masuda
by
M
d (2006).
(2006) In
I
the IG model, emissivity is expressed by a function of
emission angle (satellite zenith angle) and surface wind
speed.
d
5) The background values (x
( 0) of Ts, Ta and UH2O are
calculated from a match-up data set of MTSAT-2 radiance
(10.8 and 12.0), buoy SST and surface wind speed. The
surface wind speed is calculated from surface wind
vectors forecasted by JMA NWP. This match-up data set
4b. MTSAT-2
4b
MTSAT 2 versus MGDSST
MTSAT-2 SSTs are compared with the objectively analyzed global daily SST product (MGDSST) by JMA. Monthly
mean differences between MTSAT-2 SST and MGDSST (no robust average of MTSAT-2 minus MGDSST) have
similar
i il spatial
ti l distributions
di t ib ti
t those
to
th
b t
between
MTSAT 2 and
MTSAT-2
d buoy
b
(MTSAT 2 minus
(MTSAT-2
i
b
buoy)
) in
i corresponding
di
month.
th
Mean differences are generally larger in summer (July and August) than winter (January and February). In the
summer, large negative differences exceeding -1.0 K are found in the northern Pacific and positive differences
exceeding +0.5
+0 5 K are found between Australia and New Guinea.
Guinea These differences may be caused by cloud
screening errors. In the large blank in the northern Pacific in summer, brightness temperatures from the 10.8 micron
channel exceeded MGDSST. Contamination by very low fog or stratus, which has higher temperature than sea
surface is possible to cause such high brightness temperatures,
surface,
temperatures but further examination is required.
required In the
meantime, differences are generally between ±0.5 K in winter.
MTSAT-2 - B
M
BUOY
3. SST Retrieval from MTSAT-2
MTSAT 2 SSTs
MTSAT-2
SST are retrieved
i
d with
i h the
h new algorithm
l i h from
f
infrared 10.8 and 12.0 micron imageries by MTSAT-2 for
July and August in 2011 and for January and February in
2012 CLAVR based
2012.
b
d tests
t t are adopted
d t d for
f
th cloud
the
l d
screening. For the surface wind speed, JMA NWP output
is used. Conjugate minimization method is used to
minimize
i i i the
th costt function.
f
ti
SST calculated
SSTs
l l t d for
f each
h cloud
l d
free pixels are gathered into bins with a bin interval of 0.04
degree both for latitudinal and longitudinal. Then
maximum
i
SST in
i each
h bin
bi is
i selected
l t d as the
th MTSAT-2
MTSAT 2
SST for the corresponding grid. Note that SSTs are not
estimated from 15 UTC to 17 UTC, because of significant
biases in midnight MTSAT-2
MTSAT 2 SSTs.
SSTs
2. SST Algorithm
1) 1DVAR method
th d is
i applied
li d to
t calculate
l l t SST from
f
MTSAT-2 data. Equations (1) - (4) show the cost function.
For the calculation of radiance in forward operator (H(x)),
an atmospheric single layer radiative transfer calculation
(5) is used. Here, εr shows the sea surface emissivity, Br
shows the sensor Planck function and τr shows the
atmospheric transmittance.
transmittance Analysis parameters are SST
(Ts), atmospheric layer temperature (Ta) and a parameter
relating to water vapor absorption (UH2O). SST and other
analysis parameters are calculated by minimizing the cost
function from the data: radiance observed by the infrared
10.8 micron and 12.0 micron channels of MTSAT-2 and
their observation errors,
errors surface wind speed forecasted by
JMA NWP and an empirically generated look up table
(LUT) for background values of analysis parameters and
their estimated errors.
errors
MTSAT-2
2 - MGDSSTT
1. Introduction
MSC/JMA
operationally
retrieves
Sea
Surface
Temperatures
(SSTs)
from
MTSAT-2
radiance
observations of the 10.8 and 12 micron channels. The
retrieval algorithm is based on empirical method
comparing between the satellite observations and buoy’s
SST observations (Maturi et. al, 2008). To enhance the
SST product,
product a One-Dimensional Variational (1DVAR)
method is planned to be introduced. To take into account
effects of the sea surface emissivity, which varies with the
emission angle and the surface wind speed,
speed and the
absorption of water vapour in atmosphere on the MTSAT-2
radiance observations, single layer radiative transfer
calculation and the Isotropic Gaussian (IG) sea surface
emissivity model is used.
July 2011
August 2011
January 2012
5. References
5
Masuda, K., Infrared sea surface emissivity including multiple reflection effect for isotropic Gaussian slope distribution model, Remote Sensing of
Environment 103, 488-496, 2006
McClain E.
McClain,
E P.,
P Pichel,
Pichel W.
W G.
G and Walton,
Walton C.
C C,
C Comparative performance of AVHRR
AVHRR-based
based multichannel sea surface temperatures,
temperatures Journal of
Geophysical Research 90 (C6), 11 587–11 601, 1985
Maturi, E., Harris, A., Merchant, C., Mittaz, J., Potash, B., Meng, W. and Sapper, J., NOAA’s Sea Surface Temperature Products from Operational
Geostationary Satellites, BAMS, 1877
1877-1888,
1888, 2008
Mittaz, J. and Harris, A., The calibration of the broadband infrared sensors onboard NOAA satellites, GHRSST XII Proceedings, 270-276, 2011
Roberts, R. E., Selby, J. E. and Biberman, L. M., Infrared continuum absorption by atmospheric water vapor in the 8-12 um window, Applied Optics
15, 9, 2085
2085-2090,
2090, 1976
February 2012
Acknowledgements
Calculation of sea surface
emissivity with IG-SESR
model was supported by
Kazuhiko
Masuda
(Meteorological Research
Institute, Japan). Buoy data,
NWP
outputs
and
MGDSST were provided by
JMA.