Beyond number concentration:

Application of adiabatic cloud models to infer complete
vertical profiles of warm cloud microphysical properties
Matt Lebsock
Jet Propulsion Laboratory, California Institute of Technology
Rick SchulteColorado State University
John Haynes
Colorado State University

Existing Limitations

CWC-RVOD product:
Combined Radar + Visible Optical Depth
Leinonen et al., 2016

Missed CloudDrizzle

Key Points
1. Radar-based retrievals of low-cloud profiles have major deficiencies:• Missed detection• Precipitation contamination

2. (Sub)adiabatic theory in good agreement with radar observations fornon-precipitating clouds.
• Only requires Vis/NIR observations to derive cloud profiles

3. Machine Learning (non-linear regression) can be used to exploit lidar
observables and radar integral constraints to derive vertical profiles
within reasonable uncertainties.
• This works even when Vis/NIR observations are missing!

Problem #1
The CloudSat radar misses a large fraction of shallow warm clouds~45% missed

Christensen et al., (2013)https://doi.org/10.1002/2013JD020224

Problem #1
The CloudSat radar misses a large fraction of shallow warm clouds~45% missed

Solution #1:
Use Vis/NIR retrievals to infer vertical profiles

Christensen et al., (2013)https://doi.org/10.1002/2013JD020224

Adiabatic Cloud Model
Adiabatic theory is widely used totranslate:
𝜏𝐿𝑊𝑃
→
𝑟𝑒𝑁𝑑

What’s new here?
1. Use adiabatic theory to derive thecomplete profile - Not just
number concentration.
2. Sub-adiabatic factor is a functionof height above cloud base:ℎ𝑜𝑓𝑎𝑑 (ℎ) =ℎ𝑜 + ℎAdiabatic Cloud Model
Adiabatic Theory is widely used totranslate:
𝜏𝐿𝑊𝑃
→𝑟𝑒𝑁𝑑

What’s New here?
1. Use adiabatic theory to derive thecomplete profile. Not just numberconcentration.
2. Sub-adiabatic factor is a functionof height above cloud base:
Schulte et al., (2023)
https://doi.org/10.5194/amt-16-3531-2023ℎ𝑜𝑓𝑎𝑑 (ℎ) =ℎ𝑜 + ℎAdiabatic Cloud Model
What’s New here?

1. Use adiabatic theory to derive
the complete profile. Not just
• Use CALIOP to define cloud top height.number concentration.
Implementation:• Use MODIS optical depth and effective
radius
2. Sub-adiabaticfactor is ato define microphysical vertical profilefunction of height above cloudbase :Schulte et al., (2023)
https://doi.org/10.5194/amt-16-3531-2023ℎ𝑜𝑓𝑎𝑑 (ℎ) =ℎ𝑜 + ℎValidation of adiabatic ModelExample Solution #1 (missed clouds)

Example Solution #1 (missed clouds)

Example Solution #1 (missed clouds)

Example Solution #1 (missed clouds)

Example Solution #1 (missed clouds)

Problem #2
Radar retrievals misdiagnose precipitation as cloud water
Overestimated
LWP
Drizzle Cells

Problem #2
Radar retrievals misdiagnose precipitation as cloud water
Overestimated
LWP
Drizzle Cells

Solution #2:
Use Vis/NIR retrievals to infer vertical profiles

Example Solution #2 (drizzle clouds)

Problem #3
We have no Vis/NIR retrievals at night
ORVis/NIR retrievals failProblem #3
We have no Vis/NIR retrievals at night
ORVis/NIR retrievals failr = 0.60

Problem #3

r = 0.60

W-band PIA (dB)

We have no Vis/NIR retrievals at nightOR
Vis/NIR retrievals failr = -0.51

Problem #3

r = 0.60

W-band PIA (dB)

We have no Vis/NIR retrievals at nightOR
Vis/NIR retrievals failr = -0.51
r = 0.77

Problem #3
We have no Vis/NIR retrievals at night
ORVis/NIR retrievals failr = 0.60

W-band PIA (dB)

Solution #3:
Use machine learning to exploit available observables to infer opticaldepth and effective radiusr = -0.51
r = 0.77

Machine Learning Approach• Random forest regression model details:• Trained on Jan 2008, tested on February 2008
• 50 trees• Ocean pixels only
• Used Python’s scikit-learn package• Model inputs:• CPR surface return (𝜎0 ) and 94 GHz brightness
temperature (TB94)• ECMWF environmental data: total column watervapor (TCWV), SST, and surface wind speed• CALIOP 532 nm column integrated attenuatedbackscatter (CIAB) and ODCOD optical depth• CALIPSO-based estimates of cloud top LWC and𝑟𝑒 from (Hu et al. 2021)

CPR inputs(𝜎0 and TB94)

Lidar inputs(CIAB, ODCOD 𝜏,cloudtop LWCand 𝑟𝑒 )Aux inputs(TCWV, SST,wind)

Random Forest
Model

Cloud opticaldepth (𝜏)Cloud-top 𝑟𝑒

Subadiabatic
model
Profile of cloud
microphysics

23

ML Model Performancer = 0.77
MAE = 3.41
Bias = +0.22r = 0.76
MAE = 2.22 𝜇m
Bias = +0.05 𝜇m

r = 0.81
MAE = 27.6 g/m2
Bias = +2.1 g/m2

ML Case Study25

Key Points
1. Radar-based retrievals of low-cloud profiles have major deficiencies:• Missed detection• Precipitation contamination

2. (Sub)adiabatic theory in good agreement with radar observations for
non-precipitating clouds.
3. Machine Learning (non-linear regression) can be used to exploit lidar
observables and radar integral constraints to derive vertical profiles
within reasonable uncertainties.
• This works even when Vis/NIR observations are missing!