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

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.

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

CPICPICPI OMPS Spectral Sampling

Channels are obtained by binning spectral pixels.

Nominal spectral binning:

4 pixels/channel; < 400 nm2 pixels/channel; > 400 nm

CPICPICPIPrimary Scattering &

Absorption Features for OMPS

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.

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 *

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)

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.

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

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

CPICPICPIChannel Selection used in

OMPS Retrieval Simulations

CPICPICPI Coupled O3/Aerosol Retrieval - High O3.

CPICPICPI Coupled O3/Aerosol Retrieval - High O3.

CPICPICPI Coupled O3/Aerosol Retrieval - High O3.

CPICPICPI Coupled O3/Aerosol Retrieval - Low O3.

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.

CPICPICPI Retrieval Characterization Results

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

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.

CPICPICPI H2O Retrieval

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

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

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

CPICPICPI OMPS FOV

CPICPICPI O3 Retrieval only - Effect of Measurement Error