1. Passive microwave and visible/near infrared retrievals of boundary layer cloud physical properties
Ralf Bennartz1,2, John Rausch1,Tom Greenwald2,
Greg Elsaesser3,4, Chris O’Dell4, Matt Lebsock5
1: EES, Vanderbilt University
2: SSEC, University of Wisconsin
3: NASA-GISS/Columbia University
4: CIRA, Colorado State University
5. JPL

2. Outline Cloud Droplet Number Concentration from 13 years of MODIS data
Long-term climatology
Error analysis
Microwave-based LWP climatology from 28 years of passive microwave observations
Error analysis using combined MODIS/CloudSat/AMSR-E
Outlook

3. Cloud Droplet Number Concentration from MODIS

4. Cloud Droplet Number Concentration from MODIS
LWC

5. Cloud Droplet Number Concentration from MODIS
Updated climatology based on 13 years of full Level 2 data (Bennartz and Rausch 2017, ACP)
Fixed errors in condensation rate parameterization
Used full Collection 6 data
Limited evaluation to cases consistent with adiabatic assumption (r16 < r21 < r37)
Investigated differences C5 versus C6. Paper submitted with Kerry Meyer/Steve Platnick
Initial results using updated climatology, DOI: /vudata.ees.1

6. Climatology results
CDNC over global oceans (13 yrs average)

7. Validation against aircraft observations
Our retrieval
Aircraft observation reported by
Painemal and Zuidema (2011)

8. Climatology results
CDNC over global oceans (13 yrs average)

9. Climatology results

10. See ‘plumes’ of high retrieved CDNC around various volcanic islands
Climatology results
See ‘plumes’ of high retrieved CDNC around various volcanic islands

11. See ‘plumes’ of high retrieved CDNC around various volcanic islands
Climatology results
See ‘plumes’ of high retrieved CDNC around various volcanic islands

12. Climatology results
CDNC over global oceans (13 yrs average)

13. Climatology results
East China Sea

14. Climatology results
Consistent annual cycle with earlier studies using AVHRR (PATMOS-x) (Bennartz et al. 2011; Rausch et al., 2010)

15. Uncertainty estimates
How to calculate uncertainty estimates:
Propagate errors in tau/reff forward and average?
Use standard deviation over 1x1 degree box?

16. Uncertainty estimates

17. MW cloud liquid water path climatology
Based on RSS V7 retrievals, SSM/I since 1987, AMSR-E, and TMI
Monthly diurnal mean liquid water path
Climatological diurnal cycle
Elsaesser et al. JCLIM, submitted. (O’Dell, Wentz, and Bennartz, JCLIM, 2008)
Ongoing NASA Measures project ( )

18. MW: Principle of retrieval
Use 2 channels and polarization difference to estimate WVP, LWP
Also affected by rain water
Separation of RWP/LWP critical.

19. Data Record SSM/I, SSMIS Morning/Evening Coverage since 1987
TRMM/GPM crisscrossing in LEXT since 1997 resp 2014
AMSR-E/AMSR-2 13:30 LEXT
MWI on EUMETSAT/ EPS-SG early afternoon orbit

20. The diurnal cycle of LWP
Long-term satellite studies of LWP must account for the diurnal cycle. Otherwise, satellite drifts will lead to an aliasing of the diurnal cycle onto trends of LWP.

21. Time Series: California
MAC is higher than MODIS by about 20 gm-2
Terra/Aqua bias

22. Uncertainty analysis – Clear-sky bias
Clear-sky bias a function of wind speed and TPW
V7 retrievals are improved over older versions.
Data used for correction of climatology

23. Uncertainty analysis – Cloud/Rain partitioning
Developed new cloud/rain partitioning based on AMSR-E/Cloudsat collocation
Significantly improved agreement with Cloudsat and MODIS.

24. Conclusions CDNC Climatology published: MAC-LWP Climatology:
Bennartz and Rausch (2017, ACP). Dataset published: DOI: /vudata.ees.1
MAC-LWP Climatology:
Elsaesser et al. JCLIM under review. Supported by the NASA MEaSUREs program
To be hosted at GES-DISC (Goddard Earth Science Data and Information Services Center)
Particularly important for global climate modeling validation as well as aerosol-cloud interaction process studies.
Would be highly beneficial to tie MODIS dataset backward in time to include Patmos (AVHRR) and forward to VIIRS.