Analysis of ATMS and AMSU Striping Noise

from Their Earth Scene Observations

Zhengkun Qin1, Xiaolei Zou1, Fuzhong Weng2

1. Center for data assimilation in research and applications Nanjing University of Information Science & Technology, China

2. National Environmental Satellite, Data & Information Service, National Oceanic and Atmsopheric Administration, Washington, DC, USA

March 26, 2014

Outline

• ATMS Striping Phenomena• Striping analysis based on PCA and EEMD• Striping noise of water vapor channels• Striping analysis of Maneuver data• Summary

Suomi NPP ATMS Striping

It was first identified by NWP user communitythrough the global distribution of the bias (O-B)between ATMS observations and simulations. UnlikeAMSU-A, ATMS O-B displays a distinct O-Birregularity in its along-track direction. NWP users referthis irregularity as striping brightness temperatures orradiances.

Global Distribution of ATMS Ch10 O-BFebruary 24, 2012

Obvious Stripping noise can be found in the O-B figure of Ch10

K

K

Principle Component Analysis (PCA)

1,1 1,

96

96,1 96,

N

N

N

TB TBA

TB TB

Data Matrix:

: Brightness temperature at the kth FOV and the jth scanline

k=1,2,…,96; j=1,2,…,N

TBk, j

kj

The covariance matrix: S AAT

i iie eS

Principle Component Analysis (PCA)

: the PC coefficient for the ith mode.

1 2 96

T Tu u u E A

1, 2, 96,i i i ie e e e

: the principle component (PC) for the ith mode. ,1 ,2 ,i i i i Nu u u u

96

1i i

i

e u

A

The ATMS data can be then expressed as:

Scan angle dependence of the brightness temperature

Variations same for all FOVs are tend to appear in the 1st mode

PCA modes of TB for ATMS Ch10

1 1e u

2 2e u

TB

K

K K K

3 3e u

PCA modes of TB for NOAA-18 AMSU-A Ch9TB

K

K K K

Same features as ATMS Ch10, but the first mode is more smooth.

1 1e u

2 2e u

3 3e u

Brightness Temperature at nadir

Number of Scanline

Number of Scanline

TB(1

) (K

)

TB(1

) (K

)

ATMS Ch10

NOAA-18 AMSU-A Ch9

48,1nadir

iTB e u

Obvious oscillations can

be found in ATMS, but not in

AMSU-A

Observation

Simulation

EEMD Method

Huang and Wu (2008) and Wu and Huang (2009)

Tb t Cj t j1

n

Rn t

Rn t Rn1(t)Cn1

Cn1 Intrinsic Mode Function (IMFs) Mean of the envelopes of Rn-1

R0 t Tb (t)

Raw data

Brightness temperature for the first PCA mode at nadir

Scanline

Scanline

Scanline

Scanline

e 48,

1u 1

,j (K

)

e 48,

1u 1

,j (K

)

e 48,

1u 1

,j (K

)

e 48,

1u 1

,j (K

)

TB at Nair TB-1st IMF

TB-first two IMFs TB-first three IMF

Power Spectral of the first three IMFs

Am

plitu

de (K

)

Frequency (s-1)

Scanline

C1

(K)

C2

(K)

C3

(K)

Power spectral density distributions

Frequency (s-1)

Frequency (s-1)

Pow

er S

pect

rum

Den

sity

Pow

er S

pect

rum

Den

sity

SNPP ATMS channel 10

NOAA-18 AMSU-A channel 9

Before

After

Power spectral is modified after striping noise removed for ATMS, but there are just a little differences for AMSU-A before and after EEMD smoothing.

Global distribution of Stripping noise

Before After

Before-After

Of O-B for

ATMS Ch10

K

K K

Obs and O-B of the TBObs ATMS Ch22 O-B ATMS Ch22

Obs MHS Ch3 O-B MHS Ch3 IWP

Kg/m2

LWP

Kg/m2

KK

KK

PCA modes of TB for ATMS Ch22TB

1 1e u

2 2e u

3 3e u

K KK

K

Eigenvector and TB at nadir

Scanline

TB(1

) (K

)

FOV

e 1

Scanline

FOV

TB(1

) (K

)e 1

ATMS Ch22 NOAA-18 MHS Ch3

Striping noise can be found in both ATMS Ch22 and NOAA-18 MHS Ch3

Global distribution of stripping noiseSNPP ATMS Ch 22

NOAA-18 MHS Ch3

NOAA-16 AMSU-B Ch3

Stripping noises are stronger in ATMS and MHS, but weaker in AMSU-B.

K

Striping noise of maneuver TBSc

anlin

e

Scan

line

Scan

line

Scan

line

FOV FOV FOV FOV

ATMS Ch10 ATMS Ch22

Stripping noise can be removed in maneuver data also.

KK

K K

K K

Striping Index (SI) of maneuver TB

Channel

Channel

Varia

nce

(K2 )

Strip

ing

Inde

x

alongVs

alongV

crossVs

crossV

Valong 1N

1M

Tb(k, j) 1M

Tb(k, j)k1

M

2

k1

M

j1

N

SI Valong

Vcross

along-track variance Valong

cross-track variance Vcross

Vcross 1M

1N

Tb (k, j) 1N

Tb (k, j)j1

N

2

j1

N

k1

M

Stripping Index (SI) is reduced to close to one. It shows that our method can removed stripping noise reasonably.

Summary

• For tempearture channels, ATMS’s striping noise is much stronger than AMSU-A

• For water vapor channels, both ATMS and MHS (AMSU-B) have striping noise

• Method based on PCA and EEMD can filter the striping noise obviously and flexibly

• Root-cause and more accurate filters need further investigation

Thank you !

Questions ?