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Can AERONET help with monitoring clouds?

Can AERONET help with monitoring clouds?. Alexander Marshak NASA/GSFC Thanks to: Y. Knyazikhin, K. Evans, W. Wiscombe, I. Slutsker and B. Holben Supported by: NASA Radiation Science Program. AERONET Aerosol optical depth (from Cordoba-CETT, Argentina) 22 March, 2000. Clouds. Outline.

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Can AERONET help with monitoring clouds?

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  1. Can AERONET help with monitoring clouds? Alexander Marshak NASA/GSFC Thanks to: Y. Knyazikhin, K. Evans,W. Wiscombe, I. Slutsker and B. Holben Supported by: NASA Radiation Science Program Aeronet workshop

  2. AERONET Aerosol optical depth (from Cordoba-CETT, Argentina) 22 March, 2000 Clouds Aeronet workshop

  3. Outline • Overview of cloud optical depth retrievals from ground-based radiometers; • Main part: • Cloud optical depth retrievals from Cimel’s radiances, examples, comparison with other surface retrievals at the ARM site; • Surface inhomogeneity, sensitivity study; • Local climatology of several Aeronet sites • Testing with simulated clouds; • Summary/Conclusion Aeronet workshop

  4. Ground-based retrieval from Barker and Marshak, JAS 2001 upward radiance (flux) • Common approach is to use downward fluxes: • broadband pyranometers (Leontieva & Stamnes, 1994; Boers, 1997) • narrowband radiometers • (Min and Harrison, 1996, Min et al., 2003) downward radiance downward flux computed by DISORT: g=0.85, v0=1, rsurf=0.2 Aeronet workshop

  5. Clouds with Low Optical (Water) Depth“CLOWD” • Optical depth is the most fundamental cloud optical property • Solar radiation is most sensitive to changes in cloud optical depth at low optical depths • Over 50% of the warm liquid water clouds at the SGP site have LWP < 100 g m-2 • MWR’s uncertainty is 20-30 g m-2 (i.e., errors of 20% to over 100%) • Aerosol indirect effect research needs accurate measurements of LWP and effective radius ARM IS UNABLE TO ADEQUATELY OBSERVE OVER HALF OF THE CLOUDS OVERHEAD !! Courtesy of Dave Turner, PNNL Presentation at the ARM STM Aeronet workshop

  6. 20:44 Intercomparison between dif. retrievals for 14 March 2000 (Variable Thickness Stratus Case) Radar reflectivity Raman lidar backscatter comparisons among many volunteered methods for retrieving the low LWP Results from 14 Mar in the ARM 2000 Cloud IOP at the ARM SGP site, a day when the cloud was particularly stratiform and uniform courtesy of Dave Turner, PNNL Presentation at the ARM STM Aeronet workshop

  7. Ground-based retrieval;zenith radiance from simulated 3D clouds Aeronet workshop

  8. Pilewskie’s SSFR within 0.0001 rad (640 m) from the CART site flying at 300 m on April 5, 2000 X NIR X RED X BLUE from Michalski et al., 2002 Objectives to exploit the sharp spectral contrast in vegetated surface reflect. across 0.7 µm to retrieve cloud properties from the Cimel’s measurements Aeronet workshop

  9. Cimel radiance measurements (GSFC, Bld. 33): four channels (440, 670, 870, and 1020 nm) (a) Atmosphere dominates: I440 > I670 > I870 > I1020 Aerosol optical properties can be retrieved (b) Surface and Clouds dominate: I440 ≈ I670 < I870 ≈ I1020 Cloud optical properties can be retrieved • (c) Transition between (a) and (b): • sharp changes in Il around cloud edges; • the “order” of Il between all four channels rapidly changes from cloudy to clear and back. Aeronet workshop

  10. Surface retrieval t and Ac from Marshak et al., JAS 2004 points A & B are assumed to have the same cloud optical depth t but different cloud fraction Ac Two-channel cloud retrievals Satellite retrieval of t and re from Nakajima-King, JAS 1990 Aeronet workshop

  11. 2D Look-Up TablesNIR vs. RED plane cloudy clear IRED= IRED (t, Ac) INIR = INIR (t, Ac) t is cloud optical depth Ac is “effective” cloud fraction Aeronet workshop

  12. Where are Cimel data points? July 28, 2002 ARM CART site Cimel measurements are taken around 13:45, 13:58 and 14:11 UT TSI is from 14:00 UT Aeronet workshop

  13. Retrieval examples • Cloud optical depth retrieved from: • Cimel • MWR (Microwave Radiometer)assuming re = 7 mm • MFRSR (Multi-Filter Rotating Shadowband Radiometer) August 8, 2002;18:00 UT CART site Aeronet workshop

  14. Retrievals SZA=52.3 Time: 14:36 SZA=16.3 Time: 17:50 MFRSR data is courtesy of Q. Min Aeronet workshop

  15. Independent retrieval from BLUE and RED SZA: 29 Time: 20:36 Scatter-plot of RED_vs_NIR against BLUE_vs_NIR retrievals SZA: 19 Time: 19:32 SZA: 30 Time: 16:24 SZA: 47 Time: 15:17 SZA: 64 Time: 14:00 MODIS surface albedo: BLUE: 0.044 RED: 0.092 NIR: 0.289 Aeronet workshop

  16. Local Climatology(broadleaf cropland: Bondville, IL) MODIS srf. albedo 670 nm MODIS srf. albedo 870 nm Aeronet workshop

  17. Local Climatology(Santa Barbara, CA: 2003)Version 0 cloud optical depth product RED&NIR vs. BLUE&NIR retrievals Cloud optical depth “Effective”cloud fraction Aeronet workshop

  18. Local climatology ARM CART cite Version 0 optical depth product RED&NIR vs. BLUE&NIR retrievals Aeronet workshop

  19. Seasonal applicability Bondville, IL ARM CART site, OK Surface albedo NDVI Aeronet workshop

  20. How good is the retrieval? Sensitivity to Surface Albedo July 3, 2002, ARM CART site from LANDSAT courtesy of A. Trishchenko <t-t*> <(t-t*)/t*> in % + + • If the uncertainties in surface albedo have the same sign, the algorithm performs well. • If the NIR albedo is overestim. but the RED albedo is underestim., errors in the retrieved t are not severe. • In the opp. case, the algorithm underestim. multiple refl. in the bright band and greatly overestim. t. Aeronet workshop

  21. How good is the retrieval?Simulations using stochastic cloud models Aeronet workshop

  22. Resultsscatter plots of “retr.” vs. “true” t Total STATISTICS (Ac=81%) t(true) t(retr) mean 13.0 13.1 std 10.5 10.8 pixel-by-pixel error: 3.0 Aeronet workshop

  23. 75% pixels have errors < 3 Cumulative error histograms 3 Aeronet workshop

  24. The Main Ideas Cimel measures zenith radiance when the Sun is blocked by clouds; use two wavelengths: one in RED (670 nm) [or in BLUE (440 nm)], where vegetation albedo is low, and one in NIR (870 nm), where vegetation albedo is high retrieve cloud optical depth (and cloud fraction) using the NIR vs. RED plane The Results (so far) looks promising; it largely removes 3D effects; it is not the final answer but a big improvement against single-wavelength retrievals; can fill (cloud) gaps in AERONET aerosol optical depth retrievals and estimate (effective) cloud fraction; version 0 cloud optical depth product will be soon available for distribution Summary Aeronet workshop

  25. Can AERONET help with monitoring clouds? Alexander Marshak NASA/GSFC Thanks to: Y. Knyazikhin, K. Evans,W. Wiscombe, I. Slutsker and B. Holben Supported by: NASA Radiation Science Program Aeronet workshop

  26. Dates with the cloud mode data Bondville: July 2001 - present Cartel: July 2001 - May 2002 Egbert: July 2001 - Jan. 2002 GSFC: Dec. 2001 - Dec. 2002 Harvard Forest: July 2001 - Dec. 2001 SGP CART: Oct. 2001 - March 2004 Shirahama: Oct. 2001 - May 2002 UCSB: May 2003 - present Aeronet workshop

  27. NDCINormalized Difference Cloud Index Aeronet workshop

  28. 1D Calculations Aeronet workshop

  29. zoom Cloud observations Normalized radiance, NDCI and cloud optical depth 1D retrieval Aeronet workshop

  30. How good is the retrieval? Surface Albedo at the ARM SGP site from Michalski et al., 2002 Pilewskie’s SSFR within 0.0001 rad (640 m) from the CART site flying at 300 m on April 5, 2000 from LANDSAT + courtesy of A. Trishchenko from Li et al., JGR 2002 Aeronet workshop

  31. (effective) cloud fraction Local Climatology( a few other locations) optical depth Egbert (2001) Cartel (2001-2002) Aeronet workshop

  32. 20:44 LWP comparisons for March 14th and 15th Encouraging? Comparison of the Miller microbase algorithm for LWP (based on combining microwave and radar data) against all other volunteered results, for two days in the ARM 2000 Cloud IOP at the ARM SGP site. The cloud was very uniform and stratiform on both days. Aeronet workshop courtesy of Dave Turner, PNNL

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