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Assessing spaceborne lidar detec1on of aerosol above clouds
M. Kacenelenbogen1, J. Redemann1, P. B. Russell2, M. A. Vaughan3, Omar A.3, S. Burton3, R. R. Rogers3, R. A. Ferrare3, C. A. Hostetler3, J.W. Hair3 1Bay Area Environmental Research Ins1tute, Sonoma, CA, USA; 2NASA Ames Research Center, MoffeP Field, CA, USA; 3NASA 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 Sa, 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]
€
β1064, Totalatt
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β532,⊥att€
β532,Totalatt
CALIOP
Level 1 produ
cts
• 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 B-‐200
AATS on P3-‐B 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 Sa 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
AODCALIOP (B) >0 AODCALIOP (B) >0 AODCALIOP (B) >0
CloudCALIOP*
CloudCALIOP* AODAOC_CALIOP>0
HSR
L
AODHSRL(B) >0 2060 15% (314/2060)
68% (215/314)
AODHSRL (B) >0 24% (505/2060)
44% of HSRL (224/505)
66% (149/224) CloudHSRL
*
AODHSRL (B) >0 81%
(407/505) 76%
(170/224) 76% of HSRL
(129/170) CloudHSRL*
AODAOC_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
Clou
d free
Clou
d free
Co-‐loca*on Closest HSRL profile to CALIOP profile in 5km-‐30mn 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
Upper most cloud top height below plane CAL_LID_L2_333mCLay: median 333m-‐profile in each 5km-‐segment; 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 2-‐5km • 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 CALIOP-‐HSRL AOD (B) correlaCon more saCsfying by night (R2=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 (R2=0.94 vs 0.67)