Assessing spaceborne lidar detec1on of aerosol above clouds M. Kacenelenbogen 1 , J. Redemann 1 , P. B. Russell 2 , M. A. Vaughan 3 , Omar A. 3 , S. Burton 3 , R. R. Rogers 3 , R. A. Ferrare 3 , C. A. Hostetler 3 , J.W. Hair 3 1 Bay Area Environmental Research Ins1tute, Sonoma, CA, USA; 2 NASA Ames Research Center, MoffeP Field, CA, USA; 3 NASA Langley Research Center, Hampton, VA, USA; [email protected] [1] Winker et al., J. Atmos. Ocean. Tech., 26, p 2310–2323, 2009. [2] Vaughan et al., J. Atmos. Ocean. Tech., 26, p 2034–2050, 2009. [3] Hair et al., Appl. Optics, 40, p 5280–5294, 2001. [4] Hair et al., Appl. Optics, 47, p 6734–6752, 2008. [5] Burton et al., Atmos. Meas. Tech. Discuss., 4, p. 5631-5688, 2011. [6] Kacenelenbogen et al., Atmos. Chem. Phys., 11, p 3981-4000, 2011. Acknowledgements: The Sun/Sat group at NASA AMES (especially Jens Redemann), the HSRL (especially Sharon Burton) and the CALIPSO team at NASA Langley (especially Mark Vaughan). This research was supported by the Bay Area Environmental Research Institute (BAER) Related to a project that combines aerosol observations from CALIOP and other A-Train sensors to characterize the aerosol direct HSRL Airborne High Spectral Resolution Lidar [4] Aerosol Over Cloud (C) •Measures directly aerosol extinction and S a , without ancillary aerosol measurements or assumptions on aerosol type [3] •Systematic error on 532 nm extinction < 0.01 km −1 for typical aerosol loading [4] β 1064, Total att β 532, ⊥ att β 532, Total att CALIOP Level 1 products • Active downward pointing elastic lidar • Flies at ~7km/s at an altitude of 705 km HSRL A B CALIOP C HSRL Over all CALIOP versus HSRL (B) Poten*al bias #2 Strong aerosol events Example: 06/30/2008 HSRL on B200 AATS on P3B CALIOP Smoke scattering characteristics can be nearly identical to those of optically thin clouds (ITCR-MAB and IVDR-MAB space): CALIOP aerosol-cloud misclassification AATS time AATS AOD HSRL AOD CALIOP AOD 19.84 2.07 0.82 N/A Poten*al bias #1 Above plane: 1. Clouds aEenuate signal above plane 2. Retrieval errors propagate downward in the CALIPSO algorithm Poten*al bias #3 CALIOP’s low SNR CALIOP wrong S a Other poten*al bias • Tenuous aerosol under the detection threshold • CALIOP cloud contamination • CALIOP calibration • HSRL-CALIOP time and distance co-location #2: Dusty Mix #3: Mari1me #4: Urban #5: Smoke #6: Fresh Smoke #7: Polluted Mari1me #8: Pure Dust [5] 5km-30mn *Uppermost cloud below plane CALIOP AOD CALIOP (B) >0 AOD CALIOP (B) >0 AOD CALIOP (B) >0 Cloud CALIOP * Cloud CALIOP * AOD AOC_CALIOP >0 HSRL AOD HSRL (B) >0 2060 15% (314/2060) 68% (215/314) AOD HSRL (B) >0 24% (505/2060) 44% of HSRL (224/505) 66% (149/224) Cloud HSRL * AOD HSRL (B) >0 81% (407/505) 76% (170/224) 76% of HSRL (129/170) Cloud HSRL * AOD AOC_HSRL >0.01 • CALIOP shows 56% less clouds than HSRL (possibly due to distance and time difference between two lidars) • CALIOP shows 24% less aerosol-over-cloud cases than HSRL Cloud free Cloud free Coloca*on Closest HSRL profile to CALIOP profile in 5km30mn range (B) and (C) calcula*ons . CAL_LID_L2_05kmAPro: valid range of parameters (data catalog) and QC (0,1,2,16,18 ) . HSRL 4/3km *_sub.hdf and *_sub_aeroID.hdf [5] CALIOP cloud detec*on above plane CAL_LID_L2_05kmAPro: “Atm._Vol._Descrip1on” parameter U pper most cloud top height below plane CAL_LID_L2_333mCLay: median 333mprofile in each 5kmsegment; HSRL “cloud_top_height” • 90 m diameter foot print every 333m • No daily global coverage (same region, 16 days) Goal Our goal is to help extend this study near-cloud and above clouds. For example, over cloud, biomass burning aerosols usually strongly absorbing, may cause local positive radiative forcing radiative effects in clear skies (see oral presentation by Jens Redemann in session A52D, room 3002, 12/09, 11:16). CALIOP can be used globally to detect aerosol over clouds (AOC) but how accurate is it’s detection? Example: 08/04/2007 [6] • Aerosol type is mostly dusty mix, then smoke, then urban • Aerosol alCtude between 25km • Aerosol thickness around 500m • Distance aerosol to cloud max 2.5km Leads to 1. aerosol misclassification (wrong lidar ratio) and/or 2. lack of aerosol layer identification, especially for daytime measurements and/ or close to the ground Night CALIOPHSRL AOD (B) correlaCon more saCsfying by night (R 2 =0.64 vs. 0.22) CALIOP int. Sa less variable and smaller range Day/Night Night Other poten*al bias: Dense smoke plumes with broken clouds oVen classified as clouds [1] and CALIOP/ HSRL cloud detec*on: Day [1, 2] Cloud height agreement more saCsfying by night (R 2 =0.94 vs 0.67)