CPI International UV/Vis Limb Workshop Bremen, April Development of Generalized Limb Scattering Retrieval Algorithms Jerry Lumpe & Ed Cólon Computational Physics, Inc. John Hornstein, Eric Shettle, Richard Bevilacqua Naval Research Laboratory

CPI 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.

CPI Overview of OMPS OMPS - Ozone Mapping and Profiler Suite The primary ozone measuring component of NPOESS Limb Profiler - Measures limb scattered sunlight (dayside O 3 profiles) - Spectral range : nm - Spectral resolution : nm - Vertical resolution : km OMPS consists of three components: Nadir Mapper Nadir Profiler Limb Profiler

CPI OMPS Spectral Sampling Channels are obtained by binning spectral pixels. Nominal spectral binning: 4 pixels/channel; < 400 nm 2 pixels/channel; > 400 nm

CPI Primary Scattering & Absorption Features for OMPS

CPI 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 II 1, POAM III 2, MAS 3. 1 Lumpe et al., JGR.,102, 1997; 2 Lumpe et al., JGR,107, 2002, 3 Hartmann et al., GRL, 23, 1996.

CPI Application to Limb Scattering Problem The data space consists of normalized limb radiance versus tangent altitude in N spectral channels: The retrieval space consists of gas density and aerosol extinction profiles versus geometric altitude: * Fully coupled, simultaneous retrieval of all species *

CPI Forward Model Herman et al., Appl. Optics, 33, 1994; Herman et al., Appl. Optics, 34, We use the same forward model as the operational OMPS codes [Herman et al., 1994;1995]. Minor modifications made to the model include: - updated O 3 and NO 2 spectroscopy - more realistic aerosol models (in situ stratospheric size distributions and polar stratospheric cloud models)

CPI Treatment of Aerosols We parameterize the aerosol spectral dependence globally: 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.

CPI Retrieval Simulations Retrievals are tested using simulated data from the OMPS forward model with different O 3 /aerosol profiles. A priori profiles: O 3 - mid-latitude profile (300 DU). aerosol - MODTRAN background model. “Truth” profiles: - high O 3 high-latitude profile (575 DU) - low O 3 SH vortex, ozone hole (175 DU). - aerosol MODTRAN moderate volcanic model.

CPI Retrieval Simulations For the coupled O 3 /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.

CPI Channel Selection used in OMPS Retrieval Simulations

CPI Coupled O 3 /Aerosol Retrieval - High O 3.

CPI Coupled O 3 /Aerosol Retrieval - High O 3.

CPI Coupled O 3 /Aerosol Retrieval - High O 3.

CPI Coupled O 3 /Aerosol Retrieval - Low O 3.

CPI Retrieval Characterization The retrieval system is best characterized by studying the averaging kernel matrix: describes response of the retrieved atmospheric state vector, to variations in the true atmospheric state. We define the retrieval vertical resolution as the FWHM of the averaging kernels.

CPI Retrieval Characterization Results

CPI Future Work Optimize aerosol retrievals. Explore simultaneous retrieval of: NO 2 H 2 O Total 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, NO 2 H 2 O Total density

CPI NO 2 Retrieval * Harder et al., JGR, 1997 New, temperature- dependent NO 2 cross sections * have been implemented. NO 2 has been integrated into the forward model. NO 2 retrieval tests should follow soon.

CPI H 2 O Retrieval

CPI 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 (NO 2 ). Although the initial emphasis is on OMPS, the algorithm design is general. We intend to apply it to other limb scattering data sets. Summary

CPI Fundamentals 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:

CPI Fundamentals 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:

CPI OMPS FOV

CPI O 3 Retrieval only - Effect of Measurement Error