Ensemble Empirical Mode Decomposition (EEMD) for MSU/AMSU ...€¦ · Ensemble Empirical Mode...
Transcript of Ensemble Empirical Mode Decomposition (EEMD) for MSU/AMSU ...€¦ · Ensemble Empirical Mode...
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Xiaolei Zou1, Z. Qin1, and Fuzhong Weng2
1. Florida State University 2. Center for Satellite Applications and Research
Ensemble Empirical Mode Decomposition (EEMD) for MSU/AMSU Trend Analysis
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Traditional Method for the Trend Detection of Climate Change
one may derive an expression for estimating climate change trend of temperature based on temperature observations:
Assume { i=1, 2, …, N} is a time series of global average temperature.
Using a linear-regression model
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Trend Detection Error
Climate change trend obtained by linear-regression method contains error. The error variance is�
Observation error
Natural variability
Data length 其中
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Ensemble Empirical Model Decomposition
Huang and Wu, 2008 Wu and Huang, 2009
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Linear vs. Non-Linear Trending
• The traditional linear regression method works well for detecting the trend when data information is linear and stationary.
The EEMD method works without requiring data information being linearity and stationary.
• The trend deduced from the traditional linear regression method does not change during the studying period.
The trend deduced from the EEMD method changes with time.�
• The trend deduced from the traditional linear regression changes when data record is extended in time.
The trend deduced from the EEMD method in the past does not Change when data record is extended. �
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NOAA-15 AMSU-A Channel 3 (50.3 GHz, surface) Global average Tb EEMD components
Tb
R1
R2
R3
R4
R5
R6
R7
R8
C1
C2
C3
C4
C5
C6
C7
C8
1998 2010 1998 2010
EEMD:
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EEMD �
Raw radiance data
1st IMF
2nd IMF
3rd IMF
4th IMF
5th IMF
6th IMF
7th IMF
8th IMF
9th IMF Trend
13 years: October 26, 1998
August 7, 2010
11 years: October 26, 1998
December 31, 2008 �
(day)�
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NOAA-15 AMSU-A Channel 5 (53.596 GHz, 700 hPa)
Global daily mean Tb
Annual variation
Time (10 days)
T b (K
) T b
(K)
Linear trend 0.219 K/decade
EEMD trend
One data point per 10 days 50 members
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Climate Trend Derived by EEMD Method for All AMSU-A Channels on NOAA-15
Surface Troposphere
Stratosphere
Time (month) Time (month)
Time (month)
T b (K
)
T b (K
)
T b (K
)
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Climate Trend Derived by Linear Regression for All AMSU-A Channels on NOAA-15
T b (K
)
October 26 1998 --- August 7, 2010
Raw Tb data Traditional annual mean subtracted EEMD annual mean subtracted
Troposphere
Stratosphere
surface 900 700 350 130 70 39 8 1
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Regressed Mode
Linear trend (0.12 K/decade)
EEMD trend
Channel 5 (53.596 GHz, 700 hPa) �
(0.1K)
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Regressed Mode
Linear trend (-0.15K/decade)
EEMD trend
Channel 9 (57.29 GHz, 70 hPa) �
(0.1K)
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Stratospheric Channels in Northern Hemisphere
Ch13 Ch12
Ch10 Ch9
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Stratospheric Channels in Southern Hemisphere
Ch13 Ch12
Ch10 Ch9
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Tropospheric Channels in Northern Hemisphere
Ch8 Ch7
Ch5 Ch4
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Tropospheric Channels in Southern Hemisphere
Ch8 Ch7
Ch5 Ch4
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Channel 5 EEMD Derived Trend Evolution
2001 2004 2002 2003
2005 2008 2006 2007
2009 2010
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2001 2004 2002 2003
2005 2008 2006 2007
2009 2010
Linear regression
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Summary and Future Work
• EEMD is a very powerful tool and can be used for trending the climate from a data set that has a shorter period of time
• EEMD can derive the trend evolution while the linear trend can bias toward the data set at a particular data point
• The EEMD trends may be best connected from the different platforms by requiring the continuity. The cross-calibration of the instruments may not be required