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Jens Redemann , M. Vaughan, Y. Shinozuka , P. Russell, J. Livingston,

The combined use of MODIS, CALIPSO and OMI level 2 aerosol products for calculating direct aerosol radiative effects. Jens Redemann , M. Vaughan, Y. Shinozuka , P. Russell, J. Livingston, A. Clarke, L. Remer, C. Hostetler, R. Ferrare , J. Hair, P. Pilewskie , S. Schmidt, E. Bierwirth

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Jens Redemann , M. Vaughan, Y. Shinozuka , P. Russell, J. Livingston,

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  1. The combined use of MODIS, CALIPSO and OMI level 2 aerosol products for calculating direct aerosol radiative effects Jens Redemann, M. Vaughan, Y. Shinozuka, P. Russell, J. Livingston, A. Clarke, L. Remer, C. Hostetler, R. Ferrare, J. Hair, P. Pilewskie, S. Schmidt, E. Bierwirth BAERI – NASA Langley - NASA Ames – SRI – UHawaii - NASA Goddard - UColorado http://geo.arc.nasa.gov/AATS-website/ email: Jens.Redemann-1@nasa.gov

  2. Outline • Goal: To devise a new, methodology to derive direct aerosol radiative effects - DFaerosol(z) based on CALIOP, OMI and MODIS • Checking consistency of input data • Comparison of MODIS and CALIOP-derived AOD • Differences in CALIOP V2 and V3 • Methodology for combining CALIOP, OMI and MODIS data • Sensitivity study using synthesized data • Horizontal variability considerations for comparing&combining satellite data sets and for extrapolating into data-sparse regions: see poster 190, Shinozuka • Aerosols above clouds: see poster 89, Kacenelenbogen • Proof of concept for actual data from October 2007 • Conclusions

  3. Mean of Observationally-based DF # of Observationally-based DF > Mean of Model-based DF < # of Model-based DF Motivation: Observation- and model-based estimates of direct aerosol radiatve forcing published in IPCC diverge Observationally-based Myhre, Science, July 10, 2009 Model-based • Myhre: • Observation-based methods too large • Models show great divergence in regional and vertical distribution of DARF. • “remaining uncertainty (in DARF) is probably related to the vertical profiles of the aerosols and their location in relation to clouds”.

  4. Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate DFaerosol(z) Constraints/Input: - MODIS AOD (7/2 l) + dAOD - OMI AAOD(388 nm) + dAAOD - CALIPSO ext (532, 1064 nm) + dext - CALIPSO back (532 , 1064 nm) + dback • Issues to consider • - Differences in data quality land/ocean • Impact of model assumptions • Spatial variability • Aerosols above & near clouds MODIS aerosol models: 810 fine and 510 coarse mode distribution models define size and refractive indices of bi-modal log-normal size distribution → 2080 100 combinations Free parameters: Nfine, Ncoarse Retrieval: ext (l, z) + dext ssa (l, z) + dssa g (l, z) + dg Target: DFaerosol(z) + d DFaerosol(z) Rtx code

  5. Methodology: Bridging the clear sky – all sky gap • Use suborbital observations to: • Test retrievals of aerosol radiative properties • Test calculated radiative fluxes • Study spatial variability = uncertainty involved in going to data sparse regions (e.g., above clouds) See poster 89, Kacenelenbogen See poster 150, Shinozuka

  6. October 2007 Poster 89, Kacenelenbogen CALIOP AOC AOD CALIOP AOC occurrence (%) MODIS Active fire detection 70 % of AOD in [0-0.1] 20 % of AOD in [0.1-0.2]

  7. Horizontal variability of aerosol optical properties observed during the ARCTAS airborne experiment Shinozuka et al. Poster C.S. McNaughton Asian outflow over Alaska Forest fire smoke over Canada stdrel= 19.4% stdrel= 2.1%

  8. Part 2: Retrieval of aerosol radiative properties from A-Train observations - Methodology Constraints/Input: - MODIS AOD (7/2 l) + dAOD - OMI AAOD(388 nm) + dAAOD - CALIPSO ext (532, 1064 nm) + dext - CALIPSO back (532 , 1064 nm) + dback MODIS aerosol models: 810 fine and 510 coarse mode distribution models define size and refractive indices of bi-modal log-normal size distribution → 2080 100 combinations Free parameters: Nfine, Ncoarse Retrieval: ext (l, z) + dext ssa (l, z) + dssa g (l, z) + dg Target: DFaerosol(z) + d DFaerosol(z) Rtx code

  9. Refractive Index of MODIS modes as a function of wavelength

  10. Step 1: Each observable (here AOD 550nm) is consistent with a range of fine/coarse mode particle concentrations for a given fine/coarse mode combination (here fine#1/coarse#5)

  11. Step 2: The totality of all observables is consistent with a smaller range of fine/coarse mode particle concentrations for a given fine/coarse mode combination (here fine#1/coarse#5)

  12. Step 3: For a different fine/coarse mode combination (here fine#3/coarse#6), the observables are consistent with a different range of fine/coarse mode particle concentrations

  13. Step 4: For all possible fine/coarse mode combinations, the observables are consistent with a different range of fine/coarse mode particle concentrations

  14. Step 5: The best 10% of possible fine/coarse mode combinations & concentrations, define a range of aerosol radiative properties.

  15. Current choices in retrieval method: • Metric / error / cost function • Observables • xi = AOD 550nm, AOD 1240 nm (±0.03±5%) - MODIS • AAOD 388 nm (±0.03±5%) - OMI • b532 (±10Mm-1±10%) - CALIOP • Use 10% best solutions in context of metric above : retrieved parameters : observables

  16. Sensitivity study Consider 9 cases: Metric: allow 50% deviation in C Case Number

  17. Sensitivity study Consider 9 cases: Metric: allow 10% deviation in C Case Number

  18. Solution space: expansion from over-ocean MODIS models

  19. Solution space: expansion from over-ocean MODIS models

  20. Solution space: expansion from over-ocean MODIS models

  21. Solution space: expansion from over-ocean MODIS models

  22. Aerosol Optical Depth comparisons (CALIOP V2/V3) • Eight months of data: January, April, July and October 2007 and 2009 • Use CALIOP 5/40km-avg. (V3/V2) aerosol extinction profiles, and 5km aerosol and cloud layer products • Find all instantaneously collocated, MODIS MYD04_L2 (10x10km) aerosol retrievals traversed by 40km CALIPSO track • For V2, apply three CALIPSO profile quality criteria: • Alt_top_aerosol> Alt_top_cloud • EQC532_flag = 0 or 1 • Integrated attenuated backscatter @ 532 <=0.011 • Stratify by MODIS cloud fraction and FMF • Break down geographically → zonal mean AOD comparisons and repre-sentativeness of MODIS obs. along CALIPSO track for ALL MODIS obs. • Compare zonal means

  23. 1) Alt_top_aerosol> Alt_top_cloud 2) EQC532_flag = 0 or 1 3) Integrated attenuated backscatter @ 532 <=0.011

  24. CALIPSO AOD > MODIS AOD + 0.2 MODIS AOD > CALIPSO AOD + 0.2 FOC<0.01

  25. Zonal mean differences in AOD (550nm) from MODIS and CALIPSO over landand ocean during 4 months in 2007

  26. Zonal mean differences in AOD (550nm) from MODIS and CALIPSO over landand ocean during 4 months in 2009

  27. Proof of concept: Data sources

  28. Proof of concept: Data sources CALIOP ext.

  29. Proof of concept: Data sources CALIOP ext., MODIS AOD

  30. Proof of concept: Data sources CALIOP ext., MODIS AOD, OMI AAOD

  31. Proof of concept: Data sources CALIOP ext., MODIS AOD, OMI AAOD, ALL

  32. Example of successful retrieval from actual collocated OMI, MODIS, CALIOP (V3) data: Oct. 23, 2007

  33. Proof of concept: Instantaneous aerosol radiative forcing calculated from combined CALIOP, MODIS, OMI observations for October 23, 2007

  34. Validating/Testing the retrievals and calculated aerosol radiative effects:1) Input/output consistency checks2) Field observations of aerosol radiative properties: Using HSRL, AATS, and in situ data as test bed

  35. Conclusions • Instantaneous MODIS-CALIOP AOD comparisons show decent agreement after severe cloud clearing, and regional and zonal averaging. Zonal mean MODIS over ocean AOD is greater than CALIOP AOD by 0.05 - 0.1. Increased data density in V3 comes without accuracy loss by comparison to V2. • A methodology for the retrieval of aerosol radiative properties from MODIS AOD, OMI AAOD and CALIPSO b532&b1064 has been devised. • A sensitivity study of current method shows good retrievals for almost all AOD/ssa combinations with AOD greater or equal to 0.2. Solution space issues remain to be resolved. • Proof of concept study complete for one day of data in October 2007. • Next steps: • Continue test of retrieval assumptions (metric, solution space, etc.) and output against suborbital data • Constrain OMI AOD retrievals with CALIOP height input • Test CALIOP aerosol above cloud measurements • Extrapolation of radiative properties to data-sparse regions • Testing additional constraints afforded by APS • Comparisons to CERES results

  36. Validating/Testing the retrievals and calculated aerosol radiative effects:1) Input/output consistency checks2) Field observations of aerosol radiative properties3) AERONET retrievals

  37. CALIPSO AOD > MODIS AOD + 0.2 MODIS AOD > CALIPSO AOD + 0.2

  38. Validating/Testing the retrievals and calculated aerosol radiative effects:1) Input/output consistency checks2) Field observations of aerosol radiative properties3) AERONET retrievals

  39. Proof of concept: Available data CALIOP ext., MODIS AOD, OMI AAOD, ALL

  40. Importance (IPCC uncertainty) • Is the thickness of the aerosol layers overlying clouds significant enough to even consider retrieving aerosol radiative properties? • Where and when are there aerosols over clouds on the globe? • Devise a method of retrieving aerosol radiative properties above clouds using CALIOP and OMI (no MODIS data) Retrieving aerosol radiative properties above clouds Preliminary results using CALIPSO level 2-5km layer product data in 2007…

  41. Constraints afforded by lidar backscatter retrieval - 1 MODIS AOD (±0.03±5%) OMI AAOD (±0.03±5%) No CALIOP b532

  42. Constraints afforded by lidar backscatter retrieval - 3 MODIS AOD (±0.03±5%) OMI AAOD (±0.03±5%) CALIOP b532 (±10Mm-1±10%)

  43. OLD Sensitivity: MODIS AOD (±0.03+5%), OMI AAOD (±0.03+5%), CALIOP b532 (±10Mm-1±10%)

  44. Horizontal variability of aerosol optical properties observed during the ARCTAS airborne experiment Shinozuka et al. Poster C.S. McNaughton Asian outflow over Alaska Forest fire smoke over Canada r = 0.95 r= 0.37

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