Assessing spaceborne’lidar’detec1on’of’aerosol’above’clouds...May 09, 2011  ·...

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;    meloe.s.kacenelenbogen@nasa.gov

[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

€

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)