Study of MSU Channel-3 Brightness Temperature Time Series
Using SNO calibration method
Zhaohui Cheng*, Cheng-Zhi Zou, and Mitch Goldberg
*QSS Group Inc., Lanham, MD 20706
NOAA/NESDIS/Office of Research and Applications, NOAA Science Center, 5200 Auth Road, Camp Springs, MD 20746
1. Introduction
•
1.2
Ntn and N6
N10 and N11
N8 and N9
0.8
N11 and N12
N11 and N14
0.6
N12 and N14
N6 and N9
0.6
40
60
80
Linear (NOAA11)
227
-1
-1
-1
N14 = 0.47 K Dec
N10 = -0.5 K Dec , N11 = 0.91 K Dec
N12 = -0.47 K Dec ,
10
100
228
Linear (NOAA10)
Trend:
20
Combined1
NOAA14
228
0
0
229
NOAA12
N9 and N10
0.2
0.2
20
30
40
50
60
70
80
90
100
110
Distance (km)
Distance (km)
MSU channel 3 trends are obtained with different
algorithms.
226
1987
1989
1991
1993
1995
1997
1999
2001
227
Linear
(Combined1)
Linear (NOAA12)
-1
Linear (NOAA14)
2003
226
1987
1989
1991
1993
1995
Ntn and N6
Number of Points
N7 and N8
1000
N8 and N9
N6 and N9
100
N9 and N10
N10 and N11
N10 and N12
10
N11 and N12
N11 and N14
1
N12 and N14
10
2. Calibration algorithms used in this study
20
30
40
50
60
70
80
90
100
110
Distance (km)
STD of channel 3 brightness
temperature
differences
between
satellite pairs versus center distance of
the nadir overpass pixels. (a) Satellite
pairs that have a well defined
relationship, (b) satellite pairs that do
not show well defined SDT-distance
relationship for smaller distance due to
sampling size problems, and (c) SNO
numbers used in the STD computation
for a corresponding distance.
1999
2001
2003
Time series and trend for the pentad dataset of the global ocean-averaged MSU channel 3 brightness
temperature for NOAA 10, 11, 12 and 14 with linear calibration algorithm. (a) Individual time series and
trend for NOAA 10, 11, 12 and 14. (b) Combined time series and trend for NOAA 10, 11, 12 and 14. The
mean biases between different satellites has been removed with NOAA 10 as the reference satellite.
228
-1
Trend:
N10 = -0.64 K Dec , N11 = -0.07 K Dec
-1
N12 = -0.08 K Dec ,
228
-1
N14 = -0.04 K Dec
NOAA10
Trend: Combined1= -0.091 K/Dec
-1
NOAA11
227
227
NOAA12
Combined1
226
NOAA14
• Linear Calibration Rl = Rc + S (Ce − Cc )
1997
(b)
(a)
10000
N6 and N7
• Based on Zou et al. (2005) and Goldberg (2004) research,
MSU channel 2 and channel 3 trends are combined to
minimize the influence of the stratosphere.
NOAA11
229
0.4
0.4
Trend: Combined1=0.13 K/Dec
NOAA10
N7 and N8
1
N10 and N12
There are biases among different satellites. To merge the
multi-satellite time series, different inter-satellite
calibration algorithms are compared.
230
230
1.2
N6 and N7
1
0.8
S T D (K )
•
6. Channel 3 trend comparison for different algorithms
3. Channel 3 SNO dataset (continued)
MSU observations have been extensively used to
investigate the temperature trends of the atmosphere. This
paper focuses on the channel 3 trend analyses.
S T D (K )
•
226
Linear (NOAA10)
225
Linear (NOAA11)
Linear
(Combined1)
225
• SNO calibration (see Zou et al. 2005)
Re = Rl − δR + u S 2 (Ce − Cc )(Ce − Cw ) = Rl − δR + uZ
4. Apply SNO calibration on channel3 observations
Linear (NOAA12)
224
Linear (NOAA14)
224
1987
223
1989
1991
1993
1995
Scatterplot of Z factor between NOAA 10 and NOAA 11 for their SNO data pairs. Same
as channel 2, the figure shows high colinearity between two satellites for channel3
1997
1999
2001
2003
1
147
293
439
(a)
585
731
877
1023
1169
(b)
Time series and trend for the pentad dataset of the global ocean-averaged MSU channel 3 brightness temperature for
NOAA 10, 11, 12 and 14 with SNO calibration algorithm. (a) Individual time series and trends for NOAA 10, 11, 12
and 14. (b) Combined time series and trend for NOAA 10, 11, 12 and 14. The mean biases between different satellites
as shown in Table 6 have been removed with NOAA 10 as the reference satellite
• SNO calibration to original orbit data and Grody calibration
to global ocean average pentad data
Grody et al. (2004) calibration algorithm:
228
228
Tb' = Tb − δT + U G Z G
-1
N12 = -0.39 K Dec ,
Trend:
N14 = 0.36 K Dec
-1
-1
N10 = -0.64 K Dec , N11 = 0.52 K Dec
Trend: Combined= 0.08 K/Dec
NOAA10
-1
NOAA11
227
227
NOAA12
UG is a constant; ZG=(Tb-Tc)(Tb-Tw), here Tc is the cold space temperature which is
fixed to be 4.2 K, and Tw is the warm target temperature
Combined
NOAA14
226
226
Linear (NOAA10)
Linear (NOAA11)
225
•linear calibration to original orbit data and Grody
calibration to global ocean average pentad data
In this case, UG is computed by the equation from Zou, which is: U G = κ UZ Z G
where κ≈5×104 K (mW) -1 (sr m2 cm-1) is the conversion factor between the radiance and
the brightness temperature. Therefore Z/ZG can be obtained from their linear relation as
shown below.
Linear (Combined)
225
Linear (NOAA12)
To apply non-linear adjustment, we have same assumption as ch2 δR = 0 for
reference satellite NOAA10. Based on the U values for three given reference Dicke
temperature provided by Mo et al. (2001) from the pre-launch calibration, the U is
computed using interpolations. The figure below shows the scatter plot of U between
N10 and N11. We choose U=7.2 for N10
Scatter relationship between Z and ZG factors for
NOAA 10 for the SNO data pairs between NOAA
10 and 11. Unite for Z and ZG are 10-6 (mW)2 (sr
m2 cm-1) -2 and 103 K2, respectively.
Linear (NOAA14)
224
1987
1989
1991
1993
1995
1997
1999
2001
224
1987
2003
1989
1991
1993
1995
1997
1999
2001
2003
(b)
(a)
Pentad time series and trend for the global ocean-averaged MSU channel 2 brightness
temperature for NOAA 10, 11, 12 and 14 calibrated by Grody et al. (2004) calibration
algorithm. The Grody nonlinear adjustment is directly on the global ocean-averaged pentad
time series with SNO calibration ( δR = 0 and UG=0 for N10)
227
227
-1
Trend:
N10 = -0.7 K Dec , N11 = 0.53 K Dec
-1
N12 = -0.38 K Dec ,
NOAA10
-1
N14 = 0.38 K Dec
-1
Trend: Combined= 0.08 K/Dec
NOAA11
226
226
NOAA12
Combined
NOAA14
225
Linear (NOAA10)
225
Linear (NOAA11)
224
Linear (NOAA12)
Linear (Combined)
224
Linear (NOAA14)
223
1987
3. Channel 3 SNO dataset
1989
1991
1993
5. Bias comparison between linear and SNO calibration
Brightness temperature of linear calibrated (Tl)
When two satellites meet each other and view the same location on
earth at nadir at the same time, a SNO data pair is generated.
1995
1997
1999
2001
223
1987
2003
1989
(a)
1991
1993
1995
1997
1999
2001
2003
(b)
Pentad time series and trend for the global ocean-averaged MSU channel 2 brightness temperature
for NOAA 10, 11, 12 and 14 calibrated by Grody et al. (2004) calibration algorithm. The Grody
non-linear adjustment is directly on the global ocean-averaged pentad time series with linear
calibration at level 0 ( δR = 0 , UG =2.7856*10-4 for N10 and UG is computed by the method
introduced from section 2)
7. Adjust channel 2 trend
Weight
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
1
(b)
Brightness temperature of nonlinear calibrated (Tb), offset and u are included.
10
Pressure (mb)
(a)
MSU23
MSU2
MSU3
MSU4
MSU24
100
1000
Influence of the stratosphere on MSU2, MSU23 and
MSU24 – sensitivity study
Assume constant stratospheric cooling of -0.4 C, and
tropospheric /surface warming of 0.2 C per decade. The
resultant trend would be:
Tropopause @ 500 MB: -0.10, 0.25, -0.05
Tropopause @ 400 MB: -0.03, 0.22, 0.02
Tropopause @ 300 MB: 0.02, 0.20, 0.09
Tropopause @ 200 MB: 0.11, 0.19, 0.18
Tropopause @ 100 MB: 0.17, 0.19, 0.22
weighting function shows that the channel 2 observations contain contributions of atmospheric
radiation from not only the lower troposphere , but also the upper troposphere and lower stratosphere
To remove the effect of upper-troposphere and stratospheric effect on channel2 :
Goldberg: 1.43*channel2 -0.43*channel3
Fu (2004): 1.15*channel2-0.15*channel4
Scatter plot of the brightness temperature difference versus time
difference between two nadir pixels of the overpass satellites for
some selected overlaps. The brightness temperature is computed
using the linear calibration equation.
Stratospheric effect is better removed by Goldberg.
(c)
(d)
(e)
Scatter plots showing effects of the
nonlinear calibration on the error statistics
and distribution of the brightness
temperature difference between NOAA 10
and NOAA 11. (a) SNO data between Tl
(N10) and Tl (N11 ); (b) SNO data
between Tl (N10) and Tl (N11 )-Tl (N10).
(c) SNO data between Tb(N10) and
Tb(N11); (d) SNO data between Tb(N10)
and Tb(N11)- Tb(N10); (e) SNO data of
versus ∆Tb-∆Tl versus Tl (N10).
Mean biases of the global ocean-averages between two satellites during their overlap periods for both linear calibration
and nonlinear calibration.
Overlapping
Satellites
J
Scatter plot of the brightness temperature difference versus the
center distance between two nadir pixels of the overpass satellites
for some selected overlaps. The brightness temperature is
computed using the linear calibration equation.
Overlap
Period
Pentad data
Number
Bias (K) for linear cal
(k–j)
Bias (K) for
nonlinear cal
(k–j)
k
N10 N11
10/88- 08/91
213
-0.37412
0.106188
N10 N12
06/91- 08/91
19
0.704
0.645842
N11 N12
06/91- 12/94
261
0.921954
0.465912
N12 N14
04/95- 11/98
267
-0.41306
-0.1166
8. Summary and Future work
• The inter-satellite biases are largely removed after using the SNO calibration
and Grody calibration on MSU channel 3 observations.
• The biases resulting from the non-linear SNO calibration is 50% ~ 75% less
than that of the linear calibration.
• Applying Grody’s calibration on pentad data after the original orbit data
calibration results in a MSU channel 3 trend 0.08K/Decade.
• Using the above MSU channel 3 trend, Zou et al’s MSU channel 2 trend
(0.17K/Decade) and Goldberg’s channel 2 and channel 3 combination
algorithm, the MSU channel 2/3 trend is 0.21K/Decade
• Future work will investigate the combination algorithm of trend analyses on
MSU channels 2, 3, and 4. Compare Fu’s combine algorithm (channel 2 and
channel 4) with that of Goldberg’s.
Reference: Zou,C.-Z., M. Goldberg, Z. Cheng, N. Grody, J. Sullivan, C. Cao,
and D. Tarpley, 2005:MSU channel 2 brightness temperature trend when
calibrated using simultaneous nadir overpasses, to be submitted to JGR.
Correspondent: Cheng-Zhi.Zou@NOAA.gov