CLARREO Visible and Near-Infrared Studies

1. CLARREO Visible and Near-Infrared Studies P. Pilewskie, G. Kopp, Y. Roberts, B. Kindel, N. Shanbhag University of Colorado, Laboratory for Atmospheric and Space Physics

2. LASP Science Studies

3. LASP Science Studies • Linked to IIP activities: determine requirements for CLARREO reflected-shortwave instruments. • Climate Benchmarking • Accuracy/Stability (needed to detect climate change signal) • Radiometric • Spectral • Spectral coverage and resolution • Spatial coverage and resolution • Temporal resolution • Cross-Calibration • Current and future instruments • Orbital requirements

4. Spectral Irradiance and Atmospheric Extinction

5. SCIAMACHY Nadir Radiance Spectra Sub-saturated water vapor bands Chlorophyll green peak Near-infrared jump

6. Nadir Radiance Spectra from Aircraft: Clouds Thick Cirrus Stratus:   40 Stratus:   10

7. Establishing a Benchmark Climate Data Record:Reflected Solar Spectral Radiance • Forcing and feedback not easily separable. • No direct signal related to climate response. • What is a suitable benchmark variable to monitor in the shortwave? • Ideally, albedo, but we don’t measure irradiance from LEO. • Other variables retrieved from reflected radiance: • What are climate trends? • Model assumptions  uncertainties • What trends are evident in directly measured, high accuracy, SI traceable radiance? • Understand variability in measured reflected radiance. • Use to constrain/test models.

8. Radiometric accuracy and stability • Study objective • Determine required instrument radiometric accuracy and stability levels for CLARREO in solar spectrum (Earth-reflected). • Approach • Using trend in water vapor feedback as “benchmark”, determine error in accuracy required for detection. • Examine other suitable variables.

9. Climate Fingerprinting: Water Vapor Feedback 0.4 kg/m2 per decade Santer et al., PNAS, 2007.

10. Sample CLARREO Slit Functions Spectral Radiance: Water Vapor Retrieval Define accuracy/stability requirements needed to detect trend in water vapor ~ 0.4 kg/m2 per decade.

11. Sensitivity of Earth-Reflected Solar Radiance to Water Vapor • MODTRAN simulations used to derive changes in outgoing top-of-atmosphere spectral radiance due to 0.4 kg/m2 per decade trend. • Largest absolute changes occur in the weak (sub-saturated) VNIR water band; largest fractional changes in the wings of the stronger SWIR bands.

12. Spectral/Spatial Range and Resolution • Study objective • Determine information content in hyperspectral (reflected solar) imagery. • Approach • Use SCIAMACHY (and other candidate data sources such as AVIRIS, Hyperion, etc.) to derive independent spectral modes of variability using PCA, SVD, EOF, etc. • Determine influence of spectral resolution and range on derived components. • Use SCIAMACHY to derive independent modes of variability at varying spatial scales. • Using broad-swath imagers (SCIAMACHY, potentially MODIS) to examine scales over which spectral variance is conserved.

13. SCIAMACHY SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY • Our study uses SCIAMACHY Data Products ‘SCI_NL__1P’ as these data closely match CLARREO goals for spectral and spatial range • Spatial Resolution (nadir) • 30 km (along-track) x 60 km (cross-track) • Spectral Range (used) • 240 - 1750 nm • Spectral Resolution • 0.24 - 1.5 nm • Radiometric Accuracy • Sun-normalized: 2-3% • Relative: 1%

14. Analyzed Orbital Data from SCIAMACHY Sun-synchronous polar orbit. Nominal reference orbit of mean altitude 800 km, 35 days repeat cycle, 10:00 AM MLST descending node, 98.55 deg. inclination.

15. Spectral Decomposition: Information Content http://www.silvereng.com/PDF/NEMO.pdf

16. Cumulative Variance

17. Principal Components for Single and Global Orbits

18. SCIAMACHY Degraded to 10 nm SCIAMACHY Native Resolution

19. Spectral resolution

20. 3-Day Full Global Coverage

21. Variance: 1-3 day comparisons

22. Variance: seasonal comparisons Vegetation Molecular scattering Clouds/water vapor

23. Seasonal Variability

24. Rotation of Principal Axes

25. PC5 Represents Vegetation

26. PC5 Scores

27. PC1 Scores

28. PC1 Scores

29. Science Studies Summary • Water vapor feedback provides a constraint to the required accuracy/stability. • PCA is useful in determining information content in a multidimensional dataset such as SCIAMACHY. • For both a full-global case and a subset single orbit, 99% of the variance is explained by 5-6 components. • Interpretation of physical causality is more difficult. • First component: clouds/water vapor; fourth: molecular scattering; fifth: vegetated surface albedo. • Spectral resolution makes little difference in distributed variance. • Recommendation: use cloud phase as threshold. • Seasonal variability is evident, but PC order is conserved.

30. Science Studies Outlook Future work: • SCIAMACHY PCA • Inter-annual variability • Quantify information loss using discrete bands versus full spectrum • Cross-calibration capabilities of CLARREO • Current and future instruments • Orbital requirements