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Estimation of Cloud Droplet Number Concentration of Shallow - - PowerPoint PPT Presentation

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Estimation of Cloud Droplet Number Concentration of Shallow Trade-Wind Cumulus using Synergistic Airborne Remote Sensing Kevin Wolf [1] , Andr Ehrlich [1] , Susanne Crewell [2] , Marek Jacob [2] , Martin Wirth [3] and Manfred Wendisch [1] [1]

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Estimation of Cloud Droplet Number Concentration

  • f 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

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Trade-wind cumulus in Global Climate Models

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Trade-wind cumulus in Global Climate Models

The ‘too few, too bright‘ tropical low-cloud problem….

(Nam, C. et al., Geophys, Res. Lett. 2012 )

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Trade-wind cumulus in Global Climate Models

Poorly represented due to:

  • Sub-grid size
  • Structural variability
  • Boundary layer interactions

The ‘too few, too bright‘ tropical low-cloud problem….

(Nam, C. et al., Geophys, Res. Lett. 2012 )

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Sensitivity Studies

532 nm Liquid water path Cloud droplet number concentration Cloud top albedo / Cloud top reflectivity R = f(LWP,CDNC, T,p,q)

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Sensitivity Studies

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532 nm Liquid water path Cloud droplet number concentration Cloud top albedo / Cloud top reflectivity R = f(LWP,CDNC, T,p,q) I) CDNC driven: change of CDNC dominates

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Sensitivity Studies

II I

I) CDNC driven: change of CDNC dominates II) LWP driven: change of LWP dominates 532 nm Liquid water path Cloud droplet number concentration Cloud top albedo / Cloud top reflectivity R = f(LWP,CDNC, T,p,q)

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Sensitivity Studies

II I

532 nm How to separate the radiative effect from varying environmental conditions? Liquid water path Cloud droplet number concentration Cloud top albedo / Cloud top reflectivity R = f(LWP,CDNC, T,p,q)

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CDNC retrievals

τ and reff from bi-spectral retrieval (Nakajima and King, 1990) Cloud top T and p Assumptions: i) Adiabatic cloud profile ii) Vertically constant CDNC

T, p LWC

Common satellite

  • Brenguier, J.-L. et al., 2000
  • Grosvenor, D. P. et al., 2018
  • Wood, R. et al., 2006
  • Zheng, R. et al., 2008

Height

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

CDNC retrievals

τ and reff from bi-spectral retrieval (Nakajima and King, 1990) Cloud top T and p Assumptions: i) Adiabatic cloud profile ii) Vertically constant CDNC Shortcomings:

  • Large scale averaging (T and LWP)
  • Sub-pixel heterogeneity
  • Precipitation ?

T, p

?

Common satellite

  • Brenguier, J.-L. et al., 2000
  • Grosvenor, D. P. et al., 2018
  • Wood, R. et al., 2006
  • Zheng, R. et al., 2008

LWC Height

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

CDNC retrievals

τ and reff from bi-spectral retrieval (Nakajima and King, 1990) Cloud top T and p Assumptions: i) Adiabatic cloud profile ii) Vertically constant CDNC Shortcomings:

  • Large scale averaging (T and LWP)
  • Sub-pixel heterogeneity
  • Precipitation ?

T, p

Common satellite

Combined airborne passive and active remote sensing

?

LWC Height

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Campaign

  • Platform:

Area:

  • Time: 08. August – 31. August 2016
  • Objectives:
  • Trade-wind cumulus in the ITC region
  • Radiative effects
  • Structure
  • Evolution

Stevens, B. et al., 2018: (in prep.)

High Altitude and Long Range Research Aircraft (HALO)

Flight tracks of HALO during NARVAL-II 04

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Instrumentation of HALO

Combination of active and passive remote sensing instruments

WALES

  • Differential Absorption

and High Spectral Resolution Lidar

  • Cloud top height

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Instrumentation of HALO

Combination of active and passive remote sensing instruments

WALES

  • Differential Absorption

and High Spectral Resolution Lidar

  • Cloud top height

HAMP

  • Microwave radiometer
  • Cloud radar
  • Liquid water path
  • Radar reflectivity
  • Temperature +

humidity profiles

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Instrumentation of HALO

SMART

  • Passive cloud

spectrometer

Combination of active and passive remote sensing instruments

HAMP

  • Microwave radiometer
  • Cloud radar

WALES

  • Differential Absorption

and High Spectral Resolution Lidar

  • Cloud top height
  • Liquid water path
  • Radar reflectivity
  • Temperature +

humidity profiles

  • Irradiances
  • Optical thickness
  • Effective radius
  • Cloud top reflectivity

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Synergistic retrieval approach

SMART (Method A)

Cloud top reflectivity Effective Radius

dz LWC Height

γad

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Synergistic retrieval approach

SMART (Method A)

Cloud top reflectivity Effective Radius

HAMP (Method B)

Liquid Water Path Effective Radius Cloud base height

WALES (Method C)

Cloud top height

dz LWC Height

γad γmeas

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Synergistic retrieval approach

SMART (Method A)

Cloud top reflectivity Effective Radius

HAMP (Method B)

Liquid Water Path Effective Radius Cloud base height Radar reflectivity Precipitation flag

WALES (Method C)

Cloud top height

dz LWC Height

γad γmeas

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Synthetic measurements

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Synthetic measurements

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Synthetic measurements

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Synthetic measurements

uncorrected adiabaticity

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Synthetic measurements

corrected adiabaticity

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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

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Correlation Reflectivity - CDNC

Simulated Cloud top reflectivity

532 nm 532 nm

uncorrected adiabaticity

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Correlation Reflectivity - CDNC

Simulated Cloud top reflectivity

532 nm 532 nm

corrected adiabaticity

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Correlation Reflectivity - CDNC

Simulated Cloud top reflectivity

532 nm 532 nm 532 nm

b) a)

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Simulated Cloud top reflectivity

532 nm 532 nm

Conclusion

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

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Additional slides

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References

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Measurement Example

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Measurement Example

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Measurement Example

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Measurement Example

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Measurement Example

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Measurement Example

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Measurement Example

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Cloud top height

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Propability function of CDNC

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Liquid water path

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Effective radius

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Simulated Cloud top reflectivity Why:

  • Sub-adiabaticity
  • 3D radiative effects
  • Cloud heterogenieties
  • Cloud size
  • Surface albedo
  • Droplet size distribution

Correlation Reflectivity - CDNC

532 nm 532 nm

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Synergistic retrieval approach

SMART HAMP (Microwave profiler + radar WALES Dropsonde Cloud optical thickness Effective radius Liquid Water Path Cloud top height Cloud base height Radar reflectivity

  • Cloud geometric

thickness

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Common CDNC satellite retrieval

τ and reff from bi-spectral retrieval (Nakajima and King) Cloud top T and p Assumptions: i) Adiabatic cloud profile ii) Vertically constant DNC

T, p LWC

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AMS, Vancouver, 12th July 2018, kevin.wolf@uni-leipzig.de

Common CDNC satellite retrieval

τ and reff from bi-spectral retrieval (Nakajima and King) Cloud top T and p Assumptions: i) Adiabatic cloud profile ii) Vertically constant DNC Disadvantage: Large scale averaging (T and LWP)

T, p LWC

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Spectral Modular Airborne Radiation measurement sysTem

Wendisch, M. et al., 2001: An airborne spectral albedometer with active horizontal stabilization, J. Atmos. Oceanic Technol., 18, 1856-1866, doi: 10.1175/1520- 0426(2001)018<1856:AASAWA>2.0.CO;2

Cloud radiative forcing Retrieval of:

  • Optical thickness
  • Effective radius
  • Cloud top phase
  • Zeiss grating spectrometers
  • Temporal resolution: 2 – 5 Hz
  • Spatial resolution: 2°FOV 120mx110 m (@ 220 m s-1 10 000m)
  • Wavelength range: 300 – 2200 nm
  • Spectral resolution: 2 – 16 nm FWHM

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HALO Microwave Package

Cloud Radar MIRA-36 Frequency: 35.563 GHz Temporal resolution: 1 a Range resolution: 30 m Footprint: 130 m Parameters: Radar reflectivity, Doppler velocity, spectral width, Depolarization ratio Microwave Profiler Frequency: 26 Frequencies [22.24 – 183.31 ±12.5 GHz] Temporal resolution: 1 a Footrint: 130 m Parameters: Brightness temperature and humidity profiles

Mech, M. et al., 2014: HAMP – the microwave package on the High Altitude and Long range research aircraft (HALO), J. Atmos. Meas Tech., 7, 4539-4553, doi: 10.5194/amt-7-4539-2014

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Instrumentation Overview

48 Passive Instruments Active Instruments SMART Albedometer

  • Spectral solar Fup, Fdw, Iup (220-2200

nm)

  • Radiation budget
  • Cloud microphysics

Cloud Radar

  • Vertical radar reflectivity
  • Doppler reflectivity

Microwave Radiometer

  • Vertical temperature and humidity

profiles

  • Column integrated properties (LWP)
  • Rain rate

Lidar

  • Backscatter ratio @ 532nm
  • Depol. ratio
  • Cloud top height
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Multi-wavelength water vapor differential absorption Lidar WALES

Wirth, M. et al., 2009: The airborne multi-wavelength water vapor differential absorption lidar WALES: system design and performance, Appl. Phys. B., 96, 201-213, doi: 10.1007/s00340-009-3365-7