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ATMS Optimal Striping Filters
X. Zou1, Y. Ma1 and F. Weng2
May 13, 2014
1Department of EOAS, Florida State University
STAR JPSS Annual Meeting, May 12-16, 2014
2Center for Satellite Applications and Research, NOAA
Outline
• ATMS TDR/SDR Striping Issues
• User Complains
Qin, Z., X. Zou and F. Weng, 2013: Analysis of ATMS and AMSU striping noise from their earth scene observations. J. Geophy. Res., 118, 13,214-13,229.
• Requirements for Characterization and Correction • AMSU-A/MHS/AMSU-B
• ATMS Striping (TVAC, Pitchover Data, Earth Scene ...) • De-striping Methodology
• Optimal Striping Filters for Radiances
• Optimal Striping Filters for Calibration Counts
PCA Decomposition for ATMS Channel 10
30N
20N
10N
EQ
10S
20S
30S156E 168E 180 168W 156W
30N
20N
10N
EQ
10S
20S
30S156E 168E 180 168W 156W
30N
20N
10N
EQ
10S
20S
30S156E 168E 180 168W 156W
30N
20N
10N
EQ
10S
20S
30S156E 168E 180 168W 156W
202.0
202.8
203.6
204.4
205.2
206.0
206.8
207.6
208.4
209.2
210.0
202.0
202.8
203.6
204.4
205.2
206.0
206.8
207.6
208.4
209.2
210.0
-1.8
-1.5
-1.2
-0.9
-0.6
-0.3
0.0
0.3
0.6
0.9
1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Tb obs.
The 2nd component
The 1st component
The 3rd component
re1
ru1
re2
ru2 re3
ru3
PC mode A =
rejruj
j=1
96
∑
The ATMS data can then be expressed as in PCA:
PC coefficient
A =
TB1,1 TB1,2 L TB1, j L TB1,K
TB2,1 TB2,2 L TB2, j L TB2,K
M OTBk ,1 TBk , j TBk ,K
M OTB96,1 TB96,K
K -total number of scanlines
The First Three IMFs of ATMS Ch10 Obs.
Scanline
The 1st IMF
The 3rd IMF
The 2nd IMF
Scanline
e 48,
1 u1,
j (K
)
The 1st PC Component at Nadir
Frequency (s-1)
Pow
er S
pect
rum
Den
sity
Original data
After taking away the three IMFs
The Optimal Striping Filters: Mathematical Formula
u1,k = α n
n=−N
N
∑ u1,k+n , α n = α−n
rm = α n
n=0
N
∑ cos(2π f ∆t)
u1,k = Cme
− i2πmkK
m=0
K−1
∑ u1,k = Cme
− i2πmkK
m=0
K−1
∑
(k = 1,2,L , K ) u1,k{ }
The first PC coefficient
The filtered first PC coefficient
uk ,1
eemd = uk ,1 − IMFm(k)m=1
L
∑
minαnJ = min (
k=1
K
∑ α nn=−N
N
∑ u1,k+n − u1,keemd )2
α nn=−N
N
∑ = 1, α n = α−n
Tb,k ,i
destriping = e1,i α nn=−N
N
∑ u1,k+n + ej ,iu j ,kj=2
96
∑
i = 1,2,L ,96
where
k = 1,2,L , K
represents scan position
represents scan line
Cm = rmCm , f = m
K∆t, ∆t = 8
3s
Power Spectrum Density of the First Seven IMFs and Residuals of ATMS Brightness Temperatures
Am
plitu
de
ATMS Ch1 ATMS Ch9 ATMS Ch22
The total number of IMFs removed are two for channels 1-2 and three for channels 3-22.
Decision:
Scanline (n)
Cos
t Fun
ctio
n (J
)
Frequency (f, unit: s-1)
Filter Span (N)
Wei
ghtin
g Fu
nctio
n (α
n)
Pow
er S
pect
rum
Den
sity
The Optimal Striping Filters: Numerical Results
This is a set of optimal filters for ATMS radiances designed to
smooth out the striping noise but not to alter lower frequency
weather signals.
J = (
k=1
K
∑ α nn=−N
N
∑ u1,k+n − u1,keemd )2
J = (
k=1
K
∑ α nn=−N
N
∑ u1,k+n − u1,keemd )2
rm = α n
n=0
N
∑ cos(2π f ∆t)
Variation of Cost Function J with Filter Span
J = (
k=1
K
∑ α nn=−N
N
∑ u1,k+n − u1,keemd )2
Cos
t Fun
ctio
n (J
)
Filter Span (N)
Cos
t Fun
ctio
n (J
)
Filter Span (N)
Cos
t Fun
ctio
n (J
)
Filter Span (N)
Cos
t Fun
ctio
n (J
)
Filter Span (N)
Optimal Weighting Coefficients
J = (
k=1
K
∑ α nn=−N
N
∑ u1,k+n − u1,keemd )2
Scanline (n)
Wei
ghtin
g Fu
nctio
n (α
n)
Scanline (n)
Wei
ghtin
g Fu
nctio
n (α
n)
Scanline (n)
Wei
ghtin
g Fu
nctio
n (α
n)
Scanline (n)
Wei
ghtin
g Fu
nctio
n (α
n)
Response Functions of the Optimal Striping Filters
rm = α n
n=0
N
∑ cos(2π f ∆t)
Frequency (f, unit: s-1)
Pow
er S
pect
rum
Den
sity
Frequency (f, unit: s-1)
Pow
er S
pect
rum
Den
sity
Frequency (f, unit: s-1)
Pow
er S
pect
rum
Den
sity
Frequency (f, unit: s-1)
Pow
er S
pect
rum
Den
sity
Ch1 Ch9 Ch22
Striping noise Spectrum removed by the optimal striping filters
without with without
with
Global O-B Spectrum with and without Applying the Optimal Striping Filter
Before After
Before minus After
Global O-B Distributions of ATMS Channel 8
Striping noise
Pitch-Over Maneuver Data with and without Optimal Filtering
without with without with
ATMS Channel 1 ATMS Channel 9
Striping Index (SI)
Valong =1N
1M
Tb (k, j)− 1M
Tb (k, j)k=1
M
∑
2
k=1
M
∑
j=1
N
∑
SI =
Valong
Vcross
Along-track variance
Cross-track variance
Vcross =1M
1N
Tb (k, j)− 1N
Tb (k, j)j=1
N
∑
2
j=1
N
∑
k=1
M
∑
Striping Index (SI) of Pitch-Over Maneuver Data
Channel
SI
VC
T V
DT
Variance of down-track (VDT), variance of cross-track (VCT), and striping index (SI) before (red) and after (blue) applying
the optimal striping filter.
SI is significantly reduced to one for ATMS all channels.
Summary
• Twenty two optimal striping filters are developed for 22 ATMS channels
• Two months of de-striping ATMS data are being produced for NWP impact test
Future Plan
Similar optimal striping filters will be developed for calibration counts, and impact of striping noise on
NEDT will be quantified.