Using Large-Eddy Simulations to
analyze microphysical behavior
in midlevel, mixed phase clouds
Master’s Thesis Defense
Adam J. Smith
The University of Wisconsin-Milwaukee
November 28, 2007
Outline
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Introduction
Numerical model description
Cloud cases
Budget analysis
Development of analytic equations
Verification of analytic equations
Conclusions / Future work
What are midlevel “alto” clouds?
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Thin clouds (less than 1 km thick)
Generally overcast
Often mixed phase
Occur in any climate region (Sassen and
Khvorostyanov, 2007)
 Cover up to 22% of the planet’s surface
(Warren et al., 1988)
The importance of cloud phase
 Climate models and general circulation models
(GCMs) have difficulty predicting cloud phase
(liquid, ice, or both)
 Significant effect on radiation budget
– Variations in glaciation temperature lead to
an 8 W m-2 difference in shortwave cloud radiative
forcing (Fowler et al., 1996)
 Ackerman et al. (2004): “One key area that
impacts cloud feedbacks to climate is the phase
[of clouds]”.
What about other effects?
 Icing threat to small aircraft
– During Operation ENDURING
FREEDOM, three Air Force
Predator aircraft crashed in
Afghanistan due to icing (Haulman,
2003)
– Unmanned aerial vehicles (UAVs)
often operate at altitudes where
mixed-phase alto clouds exist
– In a study of aircraft icing
environments, 48% of observed
environments in temperature range
of 0 to -30°C were mixed-phase
(Cober and Isaac, 2002)
The “forgotten clouds”
 Vonder Haar et al. (1997) call mid-level alto clouds
“the forgotten clouds” because they are understudied.
 Zhang et al. (2005) find that GCMs greatly
underpredict thin alto clouds while overpredicting
thicker clouds like nimbostratus
– Nimbostratus are primarily comprised of liquid, which
have different reflective properties than ice or mixedphase clouds
 Methods must be devised to predict cloud phase
and overcome prediction issues
“Can we predict phase in a
simple but informative way?”
 Simulate three mixed-phase alto clouds observed
by aircraft
 Simulations are high-resolution and threedimensional, with full microphysics
 Budget equations determine the important effects
– Analyze changes in liquid and snow mixing ratio
– What processes cause these changes?
 Develop analytic equations to predict phase
behavior
– Equations only require a few inputs
– Inputs can be estimated instead of directly measured
Outline
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Introduction / Rationale
Numerical model description
Cloud cases
Budget analysis
Development of analytic equations
Verification of analytic equations
Conclusions / Future work
Numerical model
 We select the Coupled Ocean/Atmospheric
Mesoscale Prediction System (COAMPS®)
Large Eddy Simulation (COAMPS-LES)
model (Golaz et al., 2005).
 Model was previously used to perform
detailed three-dimensional studies (e.g.
Larson et al., 2006; Falk and Larson, 2007).
General model settings
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Simulation length: 4 hours
Time step: 1 s
Vertical grid spacing: 25 m
Horizontal grid spacing: 75 m
Horizontal domain size: 4125 m x 4125 m
Vertical domain size: 4400 m – 4500 m (varies)
1-hour spinup period for turbulence
Microphysics activated at t = 61 min
Second 30 min spinup period for microphysics
Microphysics scheme
 Based on Rutledge & Hobbs (1983), subsequently referred
to as RH83
 Single-moment bulk microphysics equations
– Predicts mixing ratios, but uses diagnostic formulas to determine
ice mass, number concentration, diameter, fallspeed, etc.
– More advanced schemes actively predict these parameters, but at a
much greater computational cost
 Five hydrometeor species: cloud water (rc), rain (rr), cloud
ice (ri), snow (rS), graupel (rg)
 Microphysical processes: collection, depositional growth,
sublimation
 Aggregation is not used in this microphysical scheme
 Graupel and rain deactivated (not detected in
observations)
Ice particle number concentration
 Ice particle number concentration: greatest
of values calculated using Fletcher (1962)
and Cooper (1986) formulas.
 Concentration is a diagnostic function of
temperature; not directly affected by
microphysics calculations
 This method provides no sinks of ice nuclei
 Does not produce major errors in simulation
Outline
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Introduction / Rationale
Numerical model description
Cloud cases
Budget analysis
Development of analytic equations
Verification of analytic equations
Conclusions / Future work
Cloud cases
 Three mixed-phase cloud cases:
– 11 November 1999 (denoted Nov.11 case)
– 14 October 2001 (denoted Oct.14 case)
– 02 November 2001 (denoted Nov.02 case)
 All cases were observed by aircraft during the Complex
Layered Cloud Experiments (CLEX)
 All are “altostratocumulus” (Larson et al., 2006)
– Overcast (like “stratocumulus”)
– Isolated from boundary layer (hence “alto”)
– “Altocumulus” consist of “distinct elements”, while our cases are
stratiform
 Peak liquid at cloud top, peak snow near cloud base
Nov. 11 case (11 November 1999)
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Sampled during CLEX-5 over central Montana
Studied previously in Larson et al. (2006)
Sampling occurred from 1224 – 1336 local time
Cloud region dissipated during sampling (Fleishauer et al.,
2002)
 Liquid layer: 500 m thick
 Large scale ascent: -3 cm s-1
 Constant solar zenith angle (observed near midday)
 No induced vertical wind profile in simulation (lack of
vertical wind shear)
Oct. 14 case (14 October 2001)
 Observed during CLEX-9 over central Nebraska
 Sampled from 0610 – 1000 and 1115 – 1300 local time
(sunrise through midday)
 Satellite observations show cloud region persists through
sampling periods (not shown)
 Liquid layer: 800 m thick
 Ice layer: extends 2000 m below liquid
 Above- and below- cloud data from supplemental sounding
– Launched at NWS site on Lee Bird Field (LBF), North Platte, NE
– 45 miles away from aircraft observation location
 Varied solar zenith angle using Liou (2002)
 Ascent of 1.4 cm s-1 (obtained from NCEP North American
Regional Reanalysis)
Nov. 02 case (02 November 2001)
 Also observed during CLEX-9 over central Nebraska
 Sampled from 0620 – 1020 local time (sunrise through
mid-morning)
 Satellite images indicate cloud region dissipated by 1230
local time (not shown)
 Warmer temperatures than Oct.14 case
 Liquid layer: only 400 m thick
 Ice layer: extends 1500 m below liquid
 Again, supplemental sounding launched at LBF
 Varied solar zenith angle
 Ascent of 0.7 cm s-1
Verification methods
1. Comparisons of observed versus simulated
profiles at end of spinup (t = 61 min)
– Simulated profiles tuned to match observations
2. Comparisons of observed versus simulated snow
mixing ratio at t = 90 min
– Snow profiles NOT tuned to match observations
– t = 90 min is selected to account for microphysical
spinup
3. Examination of time series evolution for liquid and
snow
Simulation
already
saturated
Simulation
already
saturated
Outline
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Introduction / Rationale
Numerical model description
Cloud cases
Budget analysis
Development of analytic equations
Verification of analytic equations
Conclusions / Future work
Budget analysis
 Which model processes are important?
 We evaluate budget equations
 Large scale and microphysical processes included
– We focus primarily on microphysics
 Small or negligible contributions neglected
 Individual budget terms (including negligible
terms) add up to equal total tendency
 Cloud water and snow budgets are examined
 We observe from t = 91 min to t = 150 min, to
account for microphysical spinup
Budget equations
rc
 Mix rc  Ascent rc  Rad rc  PSACWrc  PSDEP rc  PDEPI rc
t
rs
 Mix rS  Sediment
t
rS
 PSACWrS  PSDEP rS  PCONVrS
where:
Mix = change due to turbulent mixing
Ascent = change due to large-scale ascent
Rad = change due to radiative forcing
Sediment = change due to the motion of falling snow (“sedimentation”)
PSACW = change due to snow collecting cloud water
PSDEP = change due to depositional growth of snow
PDEPI = change due to depositional growth of cloud ice
PCONV = conversion of cloud ice to snow
Major observations from budgets
 Most important microphysical process:
Depositional growth of snow
 Other microphysical processes generate
smaller effects
 Balance between depositional growth of
snow and sedimentation in Oct.14 and
Nov.02 cases
 Time tendency of snow is relatively small,
except with strong descent in Nov.11 case
Outline
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Introduction / Rationale
Numerical model description
Cloud cases
Budget analysis
Development of analytic equations
Verification of analytic equations
Conclusions / Future work
Analytic equations
 Useful to predict snow mixing ratio and
precipitation flux
 Analytic equations allow for simple
predictions without a lot of information
 Formulas are derived from RH83
Simplified snow budget


d  ws rs
PSDEP

dt
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 Presumptions:
– Sedimentation balances depositional growth
exactly
– Steady state processes (no time tendency)
– Other microphysical terms are negligible
Analytic formulas
 1
 c    b 
b   
 b   b  
rs   3
N 0 S  1 ztop  z   rs ,top  1 
 c2   b  1

A 

Lv  Lv

 1
K aT  RvT 
B 
RvT
esi
FPSDEP  ws rs  N 0 S c2
 1
b   
    b 



c
z

z
 3   b  1 top



  b 1
b   
 1 4 Si  1  1 
c1 
F    1




 A B
 D0 
p 
c2  a 0 
 p
0.4

  b  1  S   1

D0 3  
  1  
6

b
 1
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Unknown variables:
– p (pressure)
 1
  1
– ρ (air density)

  s   1
3  
c3  c1 
D0 

6


– T (temperature)
– ztop (liquid cloud top altitude)
– z (liquid cloud base altitude)
– Si (fraction of saturation with respect to ice)
– esi (saturation vapor pressure)
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By using a reasonable estimate for each unknown variable, we can explicitly
solve these equations.
Outline






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Introduction / Rationale
Numerical model description
Cloud cases
Budget analysis
Development of analytic equations
Verification of analytic equations
Conclusions / Future work
Methods for verifying analytic
formula
 Completed a series of sensitivity simulations
 No collection processes used in study
 Adjusted variables:
– Large scale ascent
– Variable a’’ (affects snow fall velocity)
– Variable N0S (affects snow particle number
concentration)
 Total number of sensitivity simulations:
17 different settings x 3 cloud cases = 51 total
Formula verification
 For each simulation, observation time is
based on when peak snow mixing ratio
occurs
 Diagnosed value of snow mixing ratio and
snow precipitation flux obtained directly from
simulation results
 Analytic results also calculated with inputs
from simulation
 Results plotted using scatter plot
Formula underpredicts
mixing ratio
Formula overpredicts
mixing ratio
Line
indicates
equality
Formula underpredicts
mixing ratio
Formula overpredicts
mixing ratio
Line
indicates
equality
Formula underpredicts
mixing ratio
Formula overpredicts
mixing ratio
Line
indicates
equality
Formula underpredicts
mixing ratio
Formula overpredicts
mixing ratio
Line
indicates
equality
Results from our verification
 Formulas consistently underpredict snow mixing
ratio and precipitation flux
 Underprediction is likely due to neglected terms,
especially time tendency
 We still need to evaluate tendencies of the
individual results
 A multiplicative or additive factor could be useful
Outline



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Introduction / Rationale
Numerical model description
Cloud cases
Budget analysis
Development of analytic equations
Verification of analytic equations
Conclusions / Future work
Conclusions
 For this study, we simulate three mixed-phase alto clouds
 Depositional growth is the strongest microphysical process
affecting liquid and snow
 Sedimentation of snow nearly balances depositional
growth
 Time tendency of snow mixing ratio is nearly zero in two
simulations
 A series of simple analytic equations was derived from
budget observations.
 Analytic equations require inputs that can easily be
estimated
 Equations consistently underestimate snow properties, but
they still provide accurate and useful information.
Future work
 Examine log-log plot results more closely
– Why does the analytic formula produce different results
when the simulation predicts almost exactly the same
value?
 Can we factor time tendency into our equations?
 What about other neglected terms?
 How do equations perform when analyzing
different snow habits?
 Test equations versus a more robust microphysics
scheme.
Acknowledgements
 Professor Vince Larson for advising my research
 My research associates: Michael Falk, Dave Schanen,
Brian Griffin, Brandon Nielsen, and Joshua Fasching for
working with me on various computer and scientific issues
 Dr. Jean-Christophe Golaz (NOAA / GFDL) for providing
technical assistance with COAMPS-LES
 Dr. Larry Carey (ESSC / Univ. of Alabama Huntsville), Dr.
Jingguo Niu (Texas A&M Univ.), and Dr. J. Adam
Kankiewicz for providing vital aircraft and rawinsonde data
 My fellow graduate students, family, friends, and my
fianceé Apryle
 Viewers Like You
 COAMPS® is a registered trademark of the Naval
Research Laboratory
Any questions?
References
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Ackerman, T. P. et al., 2004: Atmospheric Radiation Measurement program science plan.
http://www.arm.gov/science
Cober, S. .G. and G. A. Isaac, 2002: Aircraft icing environments observed in mixedphase clouds. Preprints, 40th Aerospace Sciences Meeting & Exhibit, Reno, NV,
American Institute of Aeronautics and Astronautics
Cooper, W. A., 1986: Ice initiation in natural clouds. Precipitation Enhancement – A
Scientific Challenge, Meteor. Monogr., No. 43, Amer. Meteor. Soc., 29-32.
Falk, M. J. and V. E. Larson, 2007: What causes partial cloudiness to form in
multilayered midlevel clouds? A simulated case study. J. Geophys. Res., 112 (D12206),
doi:10.1029/2006JD007666.
Fleishauer, R. P., V. E. Larson, and T. H. Vonder Haar, 2002: Observed microphysical
structure of midlevel, mixed-phase clouds. J. Atmos. Sci., 59, 1779-1804.
Fletcher, N. H., 1962: The Physics of Rainclouds. Cambridge University Press, 386 pp.
Fowler, L. D., D. A. Randall, and S. A. Rutledge, 1996: Liquid and ice cloud microphysics
in the CSU general circulation model. Part I: Model description and simulated
microphysical processes. J. Climate, 9, 489-529.
Golaz, J.-C., S. Wang, J. D. Doyle, and J. M. Schmidt, 2005: Second and third moment
vertical velocity budgets derived from COAMPS-LES. Bound.-Layer Meteor., 116, 487517.
References
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Haulman, D. L. 2003: U. S. unmanned aerial vehicles in combat, 1991-2003. Report, Air
Force Historical Research Agency, 19 pp., Maxwell AFB, AL.
Larson, V. E., A. J. Smith, M. J. Falk, K. E. Kotenberg, and J.-C. Golaz, 2006: What
determines altocumulus dissipation time? J. Geophys. Res., 111 (D19207),
doi:10.1029/2005JD007002.
Liou, K. N., 2002: An Introduction to Atmospheric Radiation. 2nd ed., Academic Press,
583 pp.
Rutledge, S. A. and P. V. Hobbs, 1983: The mesoscale and microscale structure and
organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the
seeder-feeder process in warm-frontal rainbands. J. Atmos. Sci., 40, 1185-1206.
Sassen, K. and V. I. Khvorostyanov, 2007: Microphysical and radiative properties of
mixed-phase altocumulus: A model evaluation of glaciation effects. Atm. Res., 84, 390398
Vonder Haar, T. H. et al., 1997: Overview and objectives of the DoD Center for
Geosciences sponsored “Complex Layered-Cloud Experiment”. Preprints, Cloud
Impacts on DoD Operations and Systems Conf., Newport, RI, Phillips Laboratories, 163169.
Warren, S. G., C. J. Hahn, J. London, R. M. Chervin, and R. Jenne, 1988: Global
distribution of total cloud cover and cloud type amount over land. Tech. Report NCAR
TN-317 STR, Natl. Cent. for Atmos. Res., Boulder, CO.
Zhang, M. H. et al., 2005: Comparing clouds and their seasonal variations in 10
atmospheric general circulation models with satellite measurement. J. Geophys. Res.,
110 (D15S02), doi:10.1029/2004JD005021.