Estimation of Cloud Droplet Number Concentration of Shallow Trade-Wind Cumulus using Synergistic Airborne Remote Sensing Kevin Wolf [1] , André Ehrlich [1] , Susanne Crewell [2] , Marek Jacob [2] , Martin Wirth [3] and Manfred Wendisch [1] [1] Institute for Meteorology, University of Leipzig, Leipzig, Germany [2] Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany [3] Institute for Atmospheric Physics, German Aerospace Center, Oberpfaffenhofen, Germany AMS, Vancouver, 12th July 2018 1
Trade-wind cumulus in Global Climate Models 01 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Trade-wind cumulus in Global Climate Models The ‘ too few, too bright ‘ tropical low-cloud problem …. (Nam, C. et al., Geophys, Res. Lett. 2012 ) 01 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Trade-wind cumulus in Global Climate Models The ‘ too few, too bright ‘ tropical low-cloud problem …. (Nam, C. et al., Geophys, Res. Lett. 2012 ) Poorly represented due to: • Sub-grid size • Structural variability • Boundary layer interactions 01 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Sensitivity Studies 532 nm Liquid water path R = f( LWP,CDNC , T,p,q) Cloud top albedo / Cloud droplet Cloud top reflectivity number concentration 02 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Sensitivity Studies 532 nm I Liquid water path I) CDNC driven: change of CDNC dominates R = f( LWP,CDNC , T,p,q) Cloud top albedo / Cloud droplet Cloud top reflectivity number concentration 02 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Sensitivity Studies 532 nm I II Liquid water path I) CDNC driven: change of CDNC dominates II) LWP driven: change of LWP dominates R = f( LWP,CDNC , T,p,q) Cloud top albedo / Cloud droplet Cloud top reflectivity number concentration 02 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Sensitivity Studies 532 nm I II Liquid water path How to separate the radiative effect from varying environmental conditions? R = f( LWP,CDNC , T,p,q) Cloud top albedo / Cloud droplet Cloud top reflectivity number concentration 02 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
CDNC retrievals Common satellite τ and r eff Cloud top T from bi-spectral retrieval and (Nakajima and King, 1990) p Assumptions: i) Adiabatic cloud profile T, p ii) Vertically constant CDNC Height LWC • Brenguier, J.-L. et al., 2000 • Grosvenor, D. P. et al., 2018 • Wood, R. et al., 2006 • Zheng, R. et al., 2008 03 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
CDNC retrievals Common satellite τ and r eff Cloud top T from bi-spectral retrieval and (Nakajima and King, 1990) p Assumptions: i) Adiabatic cloud profile T, p ii) Vertically constant CDNC Height ? Shortcomings: LWC • Large scale averaging (T and LWP) • Sub-pixel heterogeneity • Precipitation ? • Brenguier, J.-L. et al., 2000 • Grosvenor, D. P. et al., 2018 • Wood, R. et al., 2006 • Zheng, R. et al., 2008 03 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
CDNC retrievals Common satellite τ and r eff Cloud top T from bi-spectral retrieval and (Nakajima and King, 1990) p Assumptions: i) Adiabatic cloud profile T, p ii) Vertically constant CDNC Height ? Shortcomings: LWC • Large scale averaging (T and LWP) • Sub-pixel heterogeneity • Precipitation ? Combined airborne passive and active remote sensing 03 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Campaign • Platform : A rea : High Altitude and Long Range Research Aircraft (HALO) • Time : 08. August – 31. August 2016 Flight tracks of HALO during NARVAL-II • Objectives : • Trade-wind cumulus in the ITC region • Radiative effects • Structure • Evolution 04 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de Stevens, B. et al. , 2018: (in prep.)
Instrumentation of HALO Combination of active and passive remote sensing instruments WALES • Differential Absorption and High Spectral Resolution Lidar • Cloud top height 05 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Instrumentation of HALO Combination of active and passive remote sensing instruments WALES HAMP • Differential Absorption • Microwave radiometer • Cloud radar and High Spectral Resolution Lidar • • Liquid water path Cloud top height • Radar reflectivity • Temperature + humidity profiles 05 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Instrumentation of HALO Combination of active and passive remote sensing instruments WALES HAMP SMART • Differential Absorption • Microwave radiometer • Passive cloud • Cloud radar and High Spectral spectrometer Resolution Lidar • • • Liquid water path Cloud top height Irradiances • • Radar reflectivity Optical thickness • • Temperature + Effective radius • humidity profiles Cloud top reflectivity 05 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synergistic retrieval approach SMART (Method A) Cloud top reflectivity Effective Radius Height dz γ ad LWC 06 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synergistic retrieval approach HAMP (Method B) Liquid Water Path SMART (Method A) WALES (Method C) Effective Radius Cloud top reflectivity Cloud base height Cloud top height Effective Radius γ meas Height dz γ ad LWC 06 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synergistic retrieval approach HAMP (Method B) Liquid Water Path SMART (Method A) WALES (Method C) Effective Radius Cloud top reflectivity Cloud base height Cloud top height Effective Radius Radar reflectivity Precipitation flag γ meas Height dz γ ad LWC 06 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synthetic measurements 07 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synthetic measurements 07 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synthetic measurements 07 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synthetic measurements uncorrected adiabaticity 07 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synthetic measurements corrected adiabaticity 07 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Measurement Example Date : 19.08.2016 Time : 12:29 – 20:52 UTC Duration : 8h 23 min Weather situation : • moderate convection • larger fields of shallow trade-wind cumulus • zonaly winds Meteosat satellite image of 19:30 UTC 08 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Correlation Reflectivity - CDNC Simulated Cloud top reflectivity 532 nm 532 nm uncorrected adiabaticity 08 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Correlation Reflectivity - CDNC Simulated Cloud top reflectivity 532 nm 532 nm corrected adiabaticity 09 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Correlation Reflectivity - CDNC Simulated Cloud top reflectivity 532 nm 532 nm 532 nm b) a) 09 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Conclusion Simulated Cloud top reflectivity 532 nm 532 nm How to separate the radiative effect from varying environmental conditions? • Reflectance measurements • Independent Cloud Droplet Number Concentration • Separated for Liquid water path and droplet size • Correct adiabatic assumption (calc. adiabaticity) • Precipitation flag 10 AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Additional slides AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
References AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Measurement Example AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Measurement Example AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Measurement Example AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Measurement Example AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Measurement Example AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Measurement Example AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Measurement Example AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Cloud top height AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Propability function of CDNC AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Liquid water path AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Effective radius AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Correlation Reflectivity - CDNC Simulated Cloud top reflectivity 532 nm 532 nm Why: • Sub-adiabaticity • 3D radiative effects • Cloud heterogenieties • Cloud size • Surface albedo • Droplet size distribution AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
Synergistic retrieval approach Cloud optical thickness SMART Effective radius HAMP (Microwave profiler Liquid Water Path + radar WALES Radar reflectivity Dropsonde Cloud top height • Cloud geometric Cloud base height thickness AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de
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