International UV/Vis Limb Workshop Bremen, April 14-16 2003

1. International UV/Vis Limb WorkshopBremen, 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

2. 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.

3. Overview of OMPS • OMPS - Ozone Mapping and Profiler Suite • The primary ozone measuring component of NPOESS Nadir Mapper Nadir Profiler Limb Profiler • OMPS consists of three components: • 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

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

5. Primary Scattering & Absorption Features for OMPS

6. Optimal Estimation Routines • CPI/NRL algorithm uses optimal estimation routines which have been applied to a number of satellite data sets: POAM II1, POAM III2, MAS3. Features: - modular design - just define external forward model. - linear or nonlinear retrievals. - calculate kernel analytically or by finite difference. - returns important retrieval diagnostics: 1 Lumpe et al., JGR.,102, 1997; 2Lumpe et al., JGR,107, 2002, 3Hartmann et al., GRL, 23, 1996.

7. 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 *

8. Forward Model • 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) Herman et al., Appl. Optics, 33, 1994; Herman et al., Appl. Optics, 34, 1995.

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

10. 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.

11. 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.

12. Channel Selection used in OMPS Retrieval Simulations

13. Coupled O3/Aerosol Retrieval - High O3.

14. Coupled O3/Aerosol Retrieval - High O3.

15. Coupled O3/Aerosol Retrieval - High O3.

16. Coupled O3/Aerosol Retrieval - Low O3.

17. 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.

18. Retrieval Characterization Results

19. Future Work • Optimize aerosol retrievals. • Explore simultaneous retrieval of: NO2 • H2O • 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). NO2 H2O Total density * Lumpe et al., JGR, 107, 2002.

20. NO2 Retrieval • New, temperature-dependent NO2 cross sections * have been implemented. • NO2 has been integrated into the forward model. • NO2 retrieval tests should follow soon. *Harder et al., JGR, 1997

21. H2O Retrieval

22. Summary • 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.

23. 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:

24. 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:

25. OMPS FOV

26. O3 Retrieval only - Effect of Measurement Error