Ralf Bennartz1,2, John Rausch1,Tom Greenwald2,

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Presentation transcript:

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

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

Cloud Droplet Number Concentration from MODIS

Cloud Droplet Number Concentration from MODIS LWC

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: 10.15695/vudata.ees.1

Climatology results CDNC over global oceans (13 yrs average)

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

Climatology results CDNC over global oceans (13 yrs average)

Climatology results

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

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

Climatology results CDNC over global oceans (13 yrs average)

Climatology results East China Sea

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

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

Uncertainty estimates

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 (2013-2018)

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

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

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.

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

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

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

Conclusions CDNC Climatology published: MAC-LWP Climatology: Bennartz and Rausch (2017, ACP). Dataset published: DOI: 10.15695/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) http://disc.sci.gsfc.nasa.gov/measures 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.