Walton McBride U.S. Naval Research Lab, Stennis Space Center MS Robert Arnone University of Southern Mississippi, Stennis Space Center MS Jean-François Cayula Qinetiq North America, Stennis Space Center MS May 1, 2013 Improvements of Satellite SST Retrievals at Full Swath 1
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Walton McBride U.S. Naval Research Lab, Stennis Space Center MS Robert Arnone University of Southern Mississippi, Stennis Space Center MS Jean-François.
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1
Walton McBride U.S. Naval Research Lab, Stennis Space Center MS
Robert Arnone University of Southern Mississippi, Stennis Space Center MS
Jean-François Cayula Qinetiq North America, Stennis Space Center MS
May 1, 2013
Improvements of Satellite SST Retrievals at Full Swath
2
A FRESH PERSPECTIVE
Search for Clues on how to Improve SST RetrievalsSST algorithms Used to Create CluesScatter Plots Used to Identify CluesModel Run Results to Discern Clues
Putting the Clues Together
Interpretation of Results
Conclusions
3
MCMCMCMCMC cTTScTTcTcSST 41211312112111
NLNLfield
NLNLNL cTTScTTTcTcSST 41211312112111
fieldTfieldTfieldTfieldTfieldTfield
Tfield TccTTScTTcTcSST 541211312112111
SST ALGORITHMS
MC SST
NL SST
Tfield SST
(same as MC SST, except for addition of Tfield as separate predictor)
1sec zenithS
First guess temperature field
4
BUOY DATA SET
75.1282.1475.2009.1 4321 MCMCMCMC cccc
MCMCMCMCMC cTTScTTcTcTSST 4121131211211111 1
YoffsetTTSccTSST MCMCMC 12113211
NAVOCEANO buoy data set for the month of June 2012: 115,036 daytime points After routine NAVOCEANO filtering: 61,782 points from 0° to 53° zenith angle (53.70%) 97,496 points from 0° to 70° zenith angle (84.75%)
MCMCMCMCMC cTTScTTcTcSST 41211312112111
Linear regression coefficients using MC SST:
In the original spirit behind the MC SST formulation:
Zenith angles from 0 to 53 degrees |Tfield-Tbuoy|=0.7°
Zenith angles from 0 to 70 degrees |Tfield-Tbuoy|=0.7°
ANOTHER CLUE!
MCi
TfieldTfieldi ccc 1Clue 2:
15
fieldTfield
MCTfield
Tfield TcSSTcSST 11 1
fieldMCTfield
fieldTfield TSSTcTSST 1
MCi
TfieldTfieldi ccc 1
fieldTfieldMCMCMCMCTfield
Tfield TccTTScTTcTccSST 5412113121121111
151 TfieldTfield cc
22
122
12 1 Tfield
TfieldSST
TfieldSST cc
MCTfield
22
2
1
MCSSTTfield
TfieldTfieldc
22
2
511MC
MC
SSTTfield
SSTTfieldTfield cc
PUTTING IT ALTOGETHER
Clue 2:Clue 1:
fieldTfieldTfieldTfieldTfieldTfield
Tfield TccTTScTTcTcSST 541211312112111
If the errors in Tfield and SSTMC are uncorrelated, then the variances:
Substituting:
Simple Linear Weighting
and
where
Past Present - Past
or
16
SCATTER PLOT OF ERRORS
R2 < 0.04
Zenith angles from 0 to 70 degrees
Blue Red Color Plotting Red Blue Color Plotting
PRACTICALLY NO CORRELATION!
17
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
rms error Tfield
rm
s er
ror
SS
TM
C
Desired rms error = 0.1
Desired rms error = 0.2
Desired rms error = 0.3
Desired rms error = 0.4
RMS ERRORS RELATIONSHIPS
222
111
MCTfield SSTTfieldSST
Guarantees that will always be less than or !TfieldSST
MCSST Tfield
If the errors in Tfield and SSTMC are uncorrelated, then the variancesare related as:
Resistors in Parallel Analogy
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
rms error Tfield
rm
s er
ror
SS
TM
C
Desired rms error = 0.1
Desired rms error = 0.2
Desired rms error = 0.3
Desired rms error = 0.4
RMS ERRORS RELATIONSHIPS
Interestingly, we have control of Tfield rms errorthrough more aggressive filtering. But at what price?
Plot of rms error and % filtered points vs.|Tfield – Tbuoy| Zenith from 0 to 53
0 1 2 3 4 50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
|Tfield - Tbuoy|
rm
s er
ror
0 < Zenith Angles < 53
No Tfield
Tfield=ClimTfield=K100
Tfield=K10
0 1 2 3 4 50
10
20
30
40
50
60
70
80
90
100
|Tfield - Tbuoy|
% b
uo
y d
ata
(so
lid
)
&
% T
fiel
d c
on
trib
uti
on
(d
ash
)
0 < Zenith Angles < 53
No Tfield
Tfield=ClimTfield=K100
Tfield=K10
TRADE-OFF: rms error vs. # buoy data points
SSTMC weakly affected by aggressive filtering
Stability
Low Dropoff
0 1 2 3 4 50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
|Tfield - Tbuoy|
rm
s er
ror
53 < Zenith Angles < 70
No Tfield
Tfield=ClimTfield=K100
Tfield=K10
0 1 2 3 4 50
10
20
30
40
50
60
70
80
90
100
|Tfield - Tbuoy|
% b
uo
y d
ata
(so
lid
)
&
% T
fiel
d c
on
trib
uti
on
(d
ash
)
53 < Zenith Angles < 70
No Tfield
Tfield=ClimTfield=K100
Tfield=K10
Plot of rms error and % filtered points vs.|Tfield – Tbuoy| Zenith from 53 to 70
TRADE-OFF: rms error vs. # buoy data points
SSTMC weakly affected by aggressive filtering
Stability Low Dropoff
0 1 2 3 4 50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
|Tfield - Tbuoy|
rm
s er
ror
0 < Zenith Angles < 70
No Tfield
Tfield=ClimTfield=K100
Tfield=K10
0 1 2 3 4 50
10
20
30
40
50
60
70
80
90
100
|Tfield - Tbuoy|
% b
uo
y d
ata
(so
lid
)
&
% T
fiel
d c
on
trib
uti
on
(d
ash
)
0 < Zenith Angles < 70
No Tfield
Tfield=ClimTfield=K100
Tfield=K10
Plot of rms error and % filtered points vs.|Tfield – Tbuoy| Zenith from 0 to 70
TRADE-OFF: rms error vs. # buoy data points
SSTMC weakly affected by aggressive filtering
Stability
Low Dropoff
Zenith from 0 to 53 |Tfield-Tbuoy| = 0.7
MCSST
TfieldSST with K10
Blue Red Color Plotting Red Blue Color Plotting
EVIDENCE OF REAL RMS ERROR REDUCTION
Zenith from 53 to 70 |Tfield-Tbuoy| = 0.7
MCSST
TfieldSST with K10
Blue Red Color Plotting Red Blue Color Plotting
EVIDENCE OF REAL RMS ERROR REDUCTION
Zenith from 0 to 70 |Tfield-Tbuoy| = 0.7
MCSST
TfieldSST with K10
Blue Red Color Plotting Red Blue Color Plotting
EVIDENCE OF REAL RMS ERROR REDUCTION
25
HELP ME UNDERSTAND, LENA!
SSTMC
MCSST
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SSTMC Tfield
+
MCSSTTfield
uncorrelated noise
HELP ME UNDERSTAND, LENA!
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SSTTfield SSTMC Tfield
=
22
2
1
MCSSTTfield
TfieldTfieldc
22
2
5
MC
MC
SSTTfield
SSTTfieldc
MCSSTTfield
MCTfield SSTTfield
SST c 1
fieldTfield
MCTfield
Tfield TcSSTcSST 51
+
Always clearer image!
HELP ME UNDERSTAND, LENA!
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A FRESH PERSPECTIVE TWO-PRONGED IMPROVEMENT EFFORTS
fieldTfield
ANYTfield
Tfield TcSSTcSST 51
Using Satellite Data Only!
NAVOCEANOK100, K10, K2
IN SITUBUOY DATA
HIGH ZENITH ANGLES(emissivity, sea roughness)
HARDWARE (2 or more looks) CLOUD MASK
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CONCLUSIONSAlthough Tfield is used in NLSST, its additional information is tamed due to its appearance as a multiplier of T11-T12.
Use of Tfield as a separate predictor results in a significant increase in accuracy forall existing SST algorithms, daytime and nighttime, due to resulting variance always being less.
Tfield now is on equal footing with existing SST algorithms predicitions and improvements in its accuracy will benefit all combinations of existing SST algorithms and Tfield.
TfieldSST algorithm was found to be extremely stable. Reduced to a formulation that leads to specific relationships between variances of MCSST and Tfield.
NAVOCEANO’s Tfield characterizations, K100 and K10, only use previous satellite data. NAVOCEANO is currently working on K2, at 2km resolution.
TfieldSST algorithm allows for rms error under 0.3K over the full swath (0 to 70), while sacrificing a very modest number of buoy data points (from original 84.75%):
75% of buoy data points left from 0 to 70 with TfieldSST versus 55% of buoy data points left from from 0 to 53 PRESENTLY