Emissivity and reflection model for calculating water surface-leaving infrared radiance

1. 2007 Joint EUMETSAT/AMS Satellite Conference 26 September 2007 Emissivity and reflection model for calculating water surface-leaving infrared radiance Nicholas R. Nalli 1,4 and P. J. Minnett2,P. van Delst3, E. Maddy1,4, W. W. McMillan4 , M. D. Goldberg5 1Perot Systems Corporation, Washington, DC, USA 2University of Miami/RSMAS, Miami, USA 3 University of Wisconsin-Madison/CIMSS, NOAA/NWS/NCEP, USA 4 University of Maryland, Baltimore County, USA 5 NOAA/NESDIS/STAR, Washington, DC, USA

2. Outline • Introduction • Conventional Emissivity Models • Discrepancies against FTS observations • Radiative Transfer Based Effective Emissivity • Quasi-Specular Surface-Leaving Radiance Model • Validation • Summary and Future Work N. R. Nalli – 2007 EUMETSAT/AMS

3. Introduction • For ocean remote sensing applications requiring high accuracy (e.g., skin SST), the surface emissivity and reflectance must be determined accurately • 0.5% departure from “truth” results in significant errors (~0.3–0.4 K) in hyperspectral LWIR window channels • To this end, several published emissivity models have gained widespread acceptance (e.g., Masuda et al. 1988; Watts et al. 1996; Wu and Smith 1997; Henderson et al. 2003) • Emissivity is calculated as the ensemble-mean of one minus the Fresnel reflectivity of visible surface waves: • However, discrepancies have been identified against field measurements obtained from IR Fourier transform spectrometers (FTS) at higher wind speeds and view angles ≥40° (Hanafin and Minnett 2005) N. R. Nalli – 2007 EUMETSAT/AMS

4. Calculated Emissivity (after Wu and Smith, 1997) vs. M-AERI Observation (Hanafin and Minnett, 2005) N. R. Nalli – 2007 EUMETSAT/AMS

5. These models are theoretically sound, so where’s the culprit? • Approximation of multiple reflections • Enhancement of emissivity in analytical models includes only SESR radiation • Rigorously accounted for in Monte Carlo models (e.g., Henderson et al. 2003), but less convenient to implement • Although this is second order, it is in the right direction • Incorrect specification of reflected atmospheric radiation • Ocean reflected downwelling radiance is quasi-specular , i.e., diffuse with a large specular component (Nalli et al. 2001) • However, because of the computational burden associated with a hemispheric double integral, radiative transfer models typically treat the reflectance as either specular or Lambertian N. R. Nalli – 2007 EUMETSAT/AMS

6. Radiative Transfer-Based Effective Emissivity • Using LBLRTM downwelling calculations, we derive the effective incidence angle, Θie, from spectral variance minimization of the following RTE where the surface incident radiance is modeled by • Defining an effective emissivity to be the full quasi-specular surface-leaving radiance may then be modeled simply as N. R. Nalli – 2007 EUMETSAT/AMS

7. Validation

8. Marine Atmospheric Emitted Radiance Interferometer (M-AERI) • Ship-based FTS designed to sample downwelling and upwelling IR high-resolution spectra near the surface (Minnett et al. 2001) • High accuracy calibration (e.g., Revercomb et al. 1988)is achievedusing 2NIST-traceable blackbodies • Derived products of interest include • Radiometric skin SST (0.1 K accuracy) derived from semi-opaque spectral region (~7.7 µm) (Smith et al. 1996) • Spectral emissivity: the effective incidence angle is iterated until the TB spectral variance is minimized (Smith et al. 1996; Hanafin and Minnett 2005). • The Baltimore-Bomem AERI (BBAERI) is essentially the same as M-AERI except it is deployed on a stationary platform (the COVE site). N. R. Nalli – 2007 EUMETSAT/AMS

9. Calculated Emissivity (after Wu and Smith, 1997) vs. M-AERI Observation (Hanafin and Minnett, 2005) N. R. Nalli – 2007 EUMETSAT/AMS

10. Calculated Effective Emissivity vs. M-AERI Observation (Hanafin and Minnett, 2005) N. R. Nalli – 2007 EUMETSAT/AMS

11. Surface-Leaving Radiance Measurements • For model validation, we make use an exhaustive sample of M-AERI and BBAERI radiances obtained from several intensive field campaigns: • 1996 Combined Sensor Program (CSP) (Post et al. 1997) • 1999 East Atlantic Transect (EAT) • 2001 Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) • 2002 and 2003 AIRS BBAERI Ocean Validation Experiment (ABOVE) • 2003 Canadian Arctic Shelf Exchange Study (CASES) • 2004 Aerosol and Ocean Science Expedition (AEROSE-I) (Nalli et al. 2006) • 2004 Surface-Ocean Lower-Atmosphere Studies Air-sea Gas Experiment (SAGE) • 2006 African Monsoon Multidisciplinary Analysis (AMMA) AEROSE-II (Morris et al. 2006) • Unlike prior limited studies, these data include varying all-sky atmospheric conditions (clear, cloudy and dusty), with regional samples from the tropics, midlatitudes and high latitudes. N. R. Nalli – 2007 EUMETSAT/AMS

12. Selected Field Campaigns N. R. Nalli – 2007 EUMETSAT/AMS

13. AEROSE 2004 – LWIR 55° N = 22 days n = 2397 spectra N. R. Nalli – 2007 EUMETSAT/AMS

14. AEROSE 2004 – SWIR 55° N = 22 days n = 1080 spectra N. R. Nalli – 2007 EUMETSAT/AMS

15. ACE-Asia 2001 – LWIR 55° N = 38 days n = 4434 spectra N. R. Nalli – 2007 EUMETSAT/AMS

16. SAGE 2004 – LWIR 55° N = 25 days n = 2293 spectra N. R. Nalli – 2007 EUMETSAT/AMS

17. CASES 2003 – LWIR 55° N = 8 days n = 643 spectra N. R. Nalli – 2007 EUMETSAT/AMS

18. CSP 1996 – LWIR 65° N = 28 days n = 715 spectra N. R. Nalli – 2007 EUMETSAT/AMS

19. CSP 1996 – SWIR 65° N = 28 days n = 313 spectra N. R. Nalli – 2007 EUMETSAT/AMS

20. ABOVE 2002 – SWIR 45° N = 15 days n = 401 spectra N. R. Nalli – 2007 EUMETSAT/AMS

21. ABOVE 2003 – SWIR 45° N = 9 days n = 241 spectra N. R. Nalli – 2007 EUMETSAT/AMS

22. Summary and Future Work • Developed a new practical model for calculating IR ocean surface-leaving radiance • Unlike previous models, which focused exclusively on emissivity, the new approach presented here attempts to account consistently for quasi-specular emissivity and reflection by introducing an effective emissivity through RT calculations. • An extensive sample of FTS measurements (M-AERI and BBAERI) has provided a dataset for statistical validation • Reduction of bias of ~0.05–0.2 K in microwindows; ~0.15–0.4% correction in emissivity for observing angles 45–65° • Amounts to a significant improvement in the context of the complete forward model • The large number of global observations establishes statistical significance in the results • Angular dependence particularly germane for geosynchronous satellites • Work to be published as 2 companion papers in Applied Optics, including the base lookup tables for calculating effective emissivity • Model to be implemented within the NOAA CRTM • This work has also provided an independent evaluation of 7 published sets of measured IR refractive indices. • In agreement with other recent work, we found a significant temperature dependence, which, if unaccounted for, can lead to non-negligible spectral errors. • Therefore, additional work is desirable to derive an optimal seawater Nν dataset. N. R. Nalli – 2007 EUMETSAT/AMS

23. Acknowledgements • This work is funded by the Joint Center for Satellite Data Assimilation (JCSDA) Science Development and Implementation Task (JSDI) (J. Le Marshall and J. Yoe) • C. Barnet (NESDIS/STAR Task Monitor) and C. Dean (PSGS Program Manager) for their support of this project. • D. Tobin and B. Howell (UW/CIMSS) for providing us with AERI software utilities • N. Ebuchi (Hokkaido U., Japan) for correspondence pertaining to the Ebuchi and Kizu (2002) paper • M. Szczodrak, M. Izaguirre (UM/RSMAS), R. Knuteson, B. Osborne (UW/CIMSS), along with others too numerous to mention here, who contributed to success of the many FTS field campaigns conducted over the last decade N. R. Nalli – 2007 EUMETSAT/AMS