international uv/vis limb workshop bremen, april 14-16 2003
DESCRIPTION
International UV/Vis Limb Workshop Bremen, April 14-16 2003. Development of Generalized Limb Scattering Retrieval Algorithms. Jerry Lumpe & Ed Cólon Computational Physics, Inc. John Hornstein, Eric Shettle, Richard Bevilacqua Naval Research Laboratory. Overview. - PowerPoint PPT PresentationTRANSCRIPT
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CPICPICPI
International UV/Vis Limb Workshop
Bremen, April 14-16 2003
Development of Generalized Limb Scattering Retrieval
Algorithms
Jerry Lumpe & Ed Cólon
Computational Physics, Inc.
John Hornstein, Eric Shettle, Richard Bevilacqua
Naval Research Laboratory
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CPICPICPI Overview
• NRL/CPI is developing a generalized algorithm
for inversion of limb scattering data.
• Initial motivation: provide an alternative,
research-grade algorithm for testing and
validation of the operational OMPS algorithms.
• However, the algorithm is not specific to OMPS
and we plan to apply it to other limb scatter data
sets.
• The retrieval algorithm has a strong heritage
from the POAM II and III solar occultation
retrieval algorithms.
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CPICPICPI Overview of OMPS
• OMPS - Ozone Mapping and Profiler Suite
• The primary ozone measuring component of NPOESS
Limb Profiler
- Measures limb scattered sunlight (dayside O3 profiles)- Spectral range : 290 - 1000 nm- Spectral resolution : 1.5 - 40 nm- Vertical resolution : 2 - 3 km
• OMPS consists of three components:
Nadir MapperNadir ProfilerLimb Profiler
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CPICPICPI OMPS Spectral Sampling
Channels are obtained by binning spectral pixels.
Nominal spectral binning:
4 pixels/channel; < 400 nm2 pixels/channel; > 400 nm
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CPICPICPIPrimary Scattering &
Absorption Features for OMPS
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CPICPICPI Optimal Estimation Routines
Features:
- modular design - just define external forward model.
- linear or nonlinear retrievals.
- calculate kernel analytically or by finite difference.
- returns important retrieval diagnostics:
• CPI/NRL algorithm uses optimal estimation routines which have been applied to a number of satellite data sets: POAM II1, POAM III2, MAS3.
K
ˆ ˆx xD A
y x
1 Lumpe et al., JGR.,102, 1997; 2Lumpe et al., JGR,107, 2002, 3Hartmann et al., GRL, 23, 1996.
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CPICPICPI Application to Limb Scattering Problem
• The data space consists of normalized limb radiance versus tangent altitude in N spectral channels:
( )( ) ln
(60 km)i
i
i
R zz
R
1 2( ), ( ), ..., ( )
Ny z z z
• The retrieval space consists of gas density and aerosol extinction profiles versus geometric altitude:
3 2 2 1( ), ( ), ( ), ( ), ( , ),..., ( , )aer aermol O NO H O Nx n z n z n z n z z z
* Fully coupled, simultaneous retrieval of all species *
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CPICPICPI Forward Model
Herman et al., Appl. Optics, 33, 1994; Herman et al., Appl. Optics, 34, 1995.
• We use the same forward model as the operational OMPS codes [Herman et al., 1994;1995].
• Minor modifications made to the model include:
- updated O3 and NO2 spectroscopy
- more realistic aerosol models (in situ stratospheric size distributions
and polar stratospheric cloud models)
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CPICPICPI Treatment of Aerosols
• We parameterize the aerosol spectral dependence globally:
2
0
( , ) ( ) ln( )aer ii
i
z a z
• The aerosol extinction profile is retrieved in all channels.
• However, the aerosol phase function is calculated from an underlying size distribution which is held fixed.
Potential source of systematic error.
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CPICPICPI Retrieval Simulations
Retrievals are tested using simulated data from the OMPS forward model with different O3/aerosol profiles.
A priori profiles:
O3 - mid-latitude profile (300 DU). aerosol - MODTRAN background model.
“Truth” profiles:
- high O3 high-latitude profile (575 DU)- low O3 SH vortex, ozone hole (175 DU).- aerosol MODTRAN moderate volcanic model.
( ) ; 0.1 ; 1.7o
bckgN z r m
( ) ; 0.16 ; 1.7o
volgN z r m
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CPICPICPI Retrieval Simulations
• For the coupled O3/aerosol retrievals the state vector takes the form:
• We currently use the same retrieval channels as the operational algorithm. An extra channel at 880 nm is added to aid aerosol retrievals.
3 0 1 2( ), ( ), ( ), ( )Ox n z a z a z a z
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CPICPICPIChannel Selection used in
OMPS Retrieval Simulations
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CPICPICPI Coupled O3/Aerosol Retrieval - High O3.
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CPICPICPI Coupled O3/Aerosol Retrieval - High O3.
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CPICPICPI Coupled O3/Aerosol Retrieval - High O3.
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CPICPICPI Coupled O3/Aerosol Retrieval - Low O3.
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CPICPICPI Retrieval Characterization
• The retrieval system is best characterized by studying the averaging kernel matrix:
ˆ ˆx x yA D K
x y x
• describes response of the retrieved atmospheric state vector , to variations in the true atmospheric state .x̂ x
A
• We define the retrieval vertical resolution as the FWHM of the averaging kernels.
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CPICPICPI Retrieval Characterization Results
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CPICPICPI Future Work
• Optimize aerosol retrievals.
• Explore simultaneous retrieval of: NO2
H2OTotal
• Perform a comprehensive retrieval error analysis and characterization. This analysis is straightforward with a fully coupled retrieval*.
• Apply the algorithm to other limb scattering data sets (e.g., OSIRIS).* Lumpe et al., JGR, 107,
2002.
NO2
H2OTotal density
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CPICPICPI NO2 Retrieval
*Harder et al., JGR, 1997
• New, temperature-dependent NO2 cross sections * have been implemented.
• NO2 has been integrated into the forward model.
• NO2 retrieval tests should follow soon.
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CPICPICPI H2O Retrieval
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CPICPICPI
• We have developed algorithms for retrieving aerosol and trace gases from limb scattering data.
• Initial tests using simulated OMPS data show good results for ozone and aerosol retrievals.
• Future efforts will focus on including simultaneous retrievals of total density and other trace gases (NO2).
• Although the initial emphasis is on OMPS, the algorithm design is general. We intend to apply it to other limb scattering data sets.
Summary
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CPICPICPIFundamentals of Retrieval
Technique(Optimal Estimation)
Let:
= measurement vector, with corresponding covariance
matrix .
= true distribution of geophysical parameter to be
retrieved.
= a priori distribution of , with covariance .
= retrieved distribution.
If measurement and a priori errors are normally distributed, the maximum likelihood estimate of the true distribution, , is obtained by minimization of the cost function
Where is the forward model operator:
y
x
ax
x̂aS
yS
x
x̂
1 1ˆ ˆ ˆ ˆ( ) ( )T T
a a a yx x S x x y F x S y F x
F ( )y F x
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CPICPICPIFundamentals of Retrieval
Technique(Optimal Estimation)
For a linear problem and the functional is minimized if
For a nonlinear problem, linearize about the current best estimate, :
where
The final solution is iterative:
nx
1ˆ T T
o a a y ax x S K K S K S y K x
y K x
( ) n n ny F x y K x x n
x xn
FK
x
1
1T T
n o a n n a n y n n a nx x S K K S K S y y K x x
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CPICPICPI OMPS FOV
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CPICPICPI O3 Retrieval only - Effect of Measurement Error