Retrieval of thermal infrared cooling rates from EOS instruments

1. Retrieval of thermal infrared cooling rates from EOS instruments Daniel Feldman Thursday IR meeting January 13, 2005

2. Outline • Introduction • Methodology • Clear vs. Scattering • Instrumentation questions • Representative scenarios

3. Introduction • State vector components are frequently retrieved to derive standard products • We intend to explore in detail infrared cooling rate retrievals in clear and scattering atmospheres using EOS instruments: • AIRS • TES • MODIS/MISR

4. Motivation • Closure of infrared radiation balance for input to regional-scale models • Evaluate the direct forcing of mineral dust in the infrared via direct measurement. • Ultimately improve parameterizations of treatment of radiation in regional-scale models.

5. Previous work: • Cooling rate retrieval: • Liou and Xue (1988) • Liou (2002) • AIRS dust: • X. Huang (JGR 2004) • Thomas (AGU) • Pierangelo (ACP 2004)

6. Liou and Xue (1988 & 2002) • Analytic expression derives spectral and band radiance as a Fredholm integral of cooling rate profile and kernel transmittance function. • Assumptions: • Utilize either Goody random model or correlated-k • Transmittance function assumes constant form over spectral and band regions • Planck function for band equals Planck function for spectral channel. • Limitations: • Clear-sky calculations only, transmission function takes simple form

7. Project Flow Chart

8. Methodology • Heating/cooling rate profile retrieval methods show distinct differences compared to standard retrievals • Standard retrieval performs an inversion of the forward model mapping state vector to radiances. • Given full radiance field, heating rate calculation is trivial • Challenge of heating/cooling rate retrieval involves determining spectral and channel information to perform forward model heating/cooling rate calculation.

9. Clear-sky Roadmap • Utilize LBRTM with RADSUM • For faster calculations, use Modtran 5 • Develop framework for cooling rate retrieval • Test cooling rate retrieval algorithm for H2O (800-960) using AIRS scan pattern • Perform retrieval test by first deriving a state vector and then deriving the cooling rate.

10. Clear-Sky Verification

11. Presence of Mineral Dust • Included Volz description of dust indices of refraction and tri-model log-normal distribution of aerosols per Seinfeld and Pandis (AOD ~ 1)

12. Cooling rate profile difference with dust

13. Cooling rate retrieval with scattering in source function • Doubling-adding module on top of LBLRTM called CHARTS • User-supplied spectral functions for Modtran 5 • Derivation by Liou and Xue no longer valid because source function is not Planck function. • What are valid assumptions that can be made about source function?

14. Current foci of IR mineral dust research • Composition • Sokolik et al. • Phase function/sphericity • Spatial/height distribution • Pierangelo et al. • Mahowald • Particle Size Distribution • MODIS/MISR products • AERONET validation • Thomas

15. Cooling Rate Retrieval Road Map • Use Modtran 5 to develop a cooling rate retrieval program similar to that described by Liou. • Need validation with AIRS spectra • Use of DISORT option • Problems with sertran parameters • Test out program sensitivity to dust layer using range of dust fields provided by Mahowald.

16. Numerical methods for cooling rate retrieval • Create cooling rate jacobians with respect to standard state vector • Look at variation in band radiance with respect to view angle • Explore band radiance variations with respect to state components • Effect of uncertainty in measurements and state components (chain rule) I = radiance x = state vector T = heating/cooling (h/c) rate z = height coordinate k = state vector component index j = channel index n = matrix index for h/c rate designation

17. Questions for future: • AIRS vs. TES • TES has coverage over bright surfaces • AIRS radiances are better validated • Surface emissivity • MODIS 5km land emissivity map? • Role of AERONET for validation