using large-eddy simulations to analyze microphysical behavior in midlevel, mixed phase clouds...

Using Large-Eddy Simulations to Using Large-Eddy Simulations to analyze microphysical behavior in analyze microphysical behavior in

midlevel, mixed phase cloudsmidlevel, mixed phase clouds

Master’s Thesis DefenseMaster’s Thesis Defense

Adam J. SmithAdam J. Smith

The University of Wisconsin-MilwaukeeThe University of Wisconsin-Milwaukee

November 28, 2007November 28, 2007

OutlineOutline

IntroductionIntroduction Numerical model descriptionNumerical model description Cloud casesCloud cases Budget analysisBudget analysis Development of analytic equationsDevelopment of analytic equations Verification of analytic equationsVerification of analytic equations Conclusions / Future workConclusions / Future work

What are midlevel “alto” clouds?What are midlevel “alto” clouds?

Thin clouds (less than 1 km thick)Thin clouds (less than 1 km thick) Generally overcastGenerally overcast Often mixed phaseOften mixed phase Occur in any climate region (Occur in any climate region (Sassen and

Khvorostyanov, 2007)) Cover up to 22% of the planet’s surface Cover up to 22% of the planet’s surface

(Warren et al., 1988)(Warren et al., 1988)

The importance of cloud phaseThe importance of cloud phase

Climate models and general circulation models Climate models and general circulation models (GCMs) have difficulty predicting cloud phase (GCMs) have difficulty predicting cloud phase (liquid, ice, or both)(liquid, ice, or both)

Significant effect on radiation budgetSignificant effect on radiation budget– Variations in glaciation temperature lead toVariations in glaciation temperature lead to an 8 W man 8 W m-2-2 difference in shortwave cloud radiative difference in shortwave cloud radiative

forcing (Fowler et al., 1996)forcing (Fowler et al., 1996) Ackerman et al. (2004): “One key area that Ackerman et al. (2004): “One key area that

impacts cloud feedbacks to climate is the phase impacts cloud feedbacks to climate is the phase [of clouds]”.[of clouds]”.

What about other effects?What about other effects?

Icing threat to small aircraftIcing threat to small aircraft– During Operation ENDURING During Operation ENDURING

FREEDOM, three Air Force FREEDOM, three Air Force Predator aircraft crashed in Predator aircraft crashed in Afghanistan due to icing (Haulman, Afghanistan due to icing (Haulman, 2003)2003)

– Unmanned aerial vehicles (UAVs) Unmanned aerial vehicles (UAVs) often operate at altitudes where often operate at altitudes where mixed-phase alto clouds existmixed-phase alto clouds exist

– In a study of aircraft icing In a study of aircraft icing environments, 48% of observed environments, 48% of observed environments in temperature range environments in temperature range of 0 to -30of 0 to -30°C°C were mixed-phase were mixed-phase (Cober and Isaac, 2002)(Cober and Isaac, 2002)

The “forgotten clouds”The “forgotten clouds”

Vonder Haar et al. (1997) call mid-level alto clouds Vonder Haar et al. (1997) call mid-level alto clouds “the forgotten clouds” because they are under-“the forgotten clouds” because they are under-studied.studied.

Zhang et al. (2005) find that GCMs greatly Zhang et al. (2005) find that GCMs greatly underpredict thin alto clouds while overpredicting underpredict thin alto clouds while overpredicting thicker clouds like nimbostratusthicker clouds like nimbostratus– Nimbostratus are primarily comprised of liquid, which Nimbostratus are primarily comprised of liquid, which

have different reflective properties than ice or mixed-have different reflective properties than ice or mixed-phase cloudsphase clouds

Methods must be devised to predict cloud phase Methods must be devised to predict cloud phase and overcome prediction issuesand overcome prediction issues

““Can we predict phase in aCan we predict phase in asimple but informative way?”simple but informative way?”

Simulate three mixed-phase alto clouds observed Simulate three mixed-phase alto clouds observed by aircraftby aircraft

Simulations are high-resolution and three-Simulations are high-resolution and three-dimensional, with full microphysicsdimensional, with full microphysics

Budget equations determine the important effectsBudget equations determine the important effects– Analyze changes in liquid and snow mixing ratioAnalyze changes in liquid and snow mixing ratio– What processes cause these changes?What processes cause these changes?

Develop analytic equations to predict phase Develop analytic equations to predict phase behaviorbehavior– Equations only require a few inputsEquations only require a few inputs– Inputs can be estimated instead of directly measuredInputs can be estimated instead of directly measured

OutlineOutline

Introduction / RationaleIntroduction / Rationale Numerical model descriptionNumerical model description Cloud casesCloud cases Budget analysisBudget analysis Development of analytic equationsDevelopment of analytic equations Verification of analytic equationsVerification of analytic equations Conclusions / Future workConclusions / Future work

Numerical modelNumerical model

We select the Coupled Ocean/Atmospheric We select the Coupled Ocean/Atmospheric Mesoscale Prediction System Mesoscale Prediction System (COAMPS(COAMPS®) ) Large Eddy Simulation (COAMPS-LES) Large Eddy Simulation (COAMPS-LES) model (model (Golaz et al., 2005)Golaz et al., 2005)..

Model was previously used to perform Model was previously used to perform detailed three-dimensional studies (e.g. detailed three-dimensional studies (e.g. Larson et al., 2006; Falk and Larson, 2007).Larson et al., 2006; Falk and Larson, 2007).

General model settingsGeneral model settings

Simulation length: 4 hoursSimulation length: 4 hours Time step: 1 sTime step: 1 s Vertical grid spacing: 25 mVertical grid spacing: 25 m Horizontal grid spacing: 75 mHorizontal grid spacing: 75 m Horizontal domain size: 4125 m x 4125 mHorizontal domain size: 4125 m x 4125 m Vertical domain size: 4400 m – 4500 m (varies)Vertical domain size: 4400 m – 4500 m (varies) 1-hour spinup period for turbulence1-hour spinup period for turbulence Microphysics activated at t = 61 minMicrophysics activated at t = 61 min Second 30 min spinup period for microphysicsSecond 30 min spinup period for microphysics

Microphysics schemeMicrophysics scheme

Based on Rutledge & Hobbs (1983), subsequently referred Based on Rutledge & Hobbs (1983), subsequently referred to as RH83to as RH83

Single-moment bulk microphysics equationsSingle-moment bulk microphysics equations– Predicts mixing ratios, but uses diagnostic formulas to determine Predicts mixing ratios, but uses diagnostic formulas to determine

ice mass, number concentration, diameter, fallspeed, etc.ice mass, number concentration, diameter, fallspeed, etc.– More advanced schemes actively predict these parameters, but at More advanced schemes actively predict these parameters, but at

a much greater computational costa much greater computational cost Five hydrometeor species: cloud water (Five hydrometeor species: cloud water (rrcc), rain (), rain (rrrr), cloud ), cloud

ice (ice (rrii), snow (), snow (rrSS), graupel (), graupel (rrgg)) Microphysical processes: collection, depositional growth, Microphysical processes: collection, depositional growth,

sublimationsublimation Aggregation is not used in this microphysical schemeAggregation is not used in this microphysical scheme Graupel and rain deactivated (not detected in observations)Graupel and rain deactivated (not detected in observations)

Ice particle number concentrationIce particle number concentration

Ice particle number concentration: greatest Ice particle number concentration: greatest of values calculated using Fletcher (1962) of values calculated using Fletcher (1962) and Cooper (1986) formulas.and Cooper (1986) formulas.

Concentration is a diagnostic function of Concentration is a diagnostic function of temperature; not directly affected by temperature; not directly affected by microphysics calculationsmicrophysics calculations

This method provides no sinks of ice nucleiThis method provides no sinks of ice nuclei Does not produce major errors in simulationDoes not produce major errors in simulation

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Cloud casesCloud cases

Three mixed-phase cloud cases:Three mixed-phase cloud cases:– 11 November 1999 (denoted Nov.11 case)11 November 1999 (denoted Nov.11 case)– 14 October 2001 (denoted Oct.14 case)14 October 2001 (denoted Oct.14 case)– 02 November 2001 (denoted Nov.02 case)02 November 2001 (denoted Nov.02 case)

All cases were observed by aircraft during the Complex All cases were observed by aircraft during the Complex Layered Cloud Experiments (CLEX)Layered Cloud Experiments (CLEX)

All are “altostratocumulus” (Larson et al., 2006)All are “altostratocumulus” (Larson et al., 2006)– Overcast (like “stratocumulus”)Overcast (like “stratocumulus”)– Isolated from boundary layer (hence “alto”)Isolated from boundary layer (hence “alto”)– ““Altocumulus” consist of “distinct elements”, while our cases are Altocumulus” consist of “distinct elements”, while our cases are

stratiformstratiform

Peak liquid at cloud top, peak snow near cloud basePeak liquid at cloud top, peak snow near cloud base

Nov. 11 case (11 November 1999)Nov. 11 case (11 November 1999)

Sampled during CLEX-5 over central MontanaSampled during CLEX-5 over central Montana Studied previously in Larson et al. (2006)Studied previously in Larson et al. (2006) Sampling occurred from 1224 – 1336 local timeSampling occurred from 1224 – 1336 local time Cloud region dissipated during sampling (Fleishauer et al., Cloud region dissipated during sampling (Fleishauer et al.,

2002)2002) Liquid layer: 500 m thickLiquid layer: 500 m thick

Large scale ascent: -3 cm sLarge scale ascent: -3 cm s-1-1

Constant solar zenith angle (observed near midday)Constant solar zenith angle (observed near midday) No induced vertical wind profile in simulation (lack of No induced vertical wind profile in simulation (lack of

vertical wind shear)vertical wind shear)

Oct. 14 case (14 October 2001)Oct. 14 case (14 October 2001)

Observed during CLEX-9 over central NebraskaObserved during CLEX-9 over central Nebraska Sampled from 0610 – 1000 and 1115 – 1300 local time Sampled from 0610 – 1000 and 1115 – 1300 local time

(sunrise through midday)(sunrise through midday) Satellite observations show cloud region persists through Satellite observations show cloud region persists through

sampling periods (not shown)sampling periods (not shown) Liquid layer: 800 m thickLiquid layer: 800 m thick Ice layer: extends 2000 m below liquidIce layer: extends 2000 m below liquid Above- and below- cloud data from supplemental sounding Above- and below- cloud data from supplemental sounding

– Launched at NWS site on Lee Bird Field (LBF), North Platte, NELaunched at NWS site on Lee Bird Field (LBF), North Platte, NE– 45 miles away from aircraft observation location45 miles away from aircraft observation location

Varied solar zenith angle using Liou (2002)Varied solar zenith angle using Liou (2002) Ascent of 1.4 cm sAscent of 1.4 cm s-1-1 (obtained from NCEP North American (obtained from NCEP North American

Regional Reanalysis)Regional Reanalysis)

Nov. 02 case (02 November 2001)Nov. 02 case (02 November 2001)

Also observed during CLEX-9 over central NebraskaAlso observed during CLEX-9 over central Nebraska Sampled from 0620 – 1020 local time (sunrise through mid-Sampled from 0620 – 1020 local time (sunrise through mid-

morning)morning) Satellite images indicate cloud region dissipated by 1230 Satellite images indicate cloud region dissipated by 1230

local time (not shown)local time (not shown) Warmer temperatures than Oct.14 caseWarmer temperatures than Oct.14 case Liquid layer: only 400 m thickLiquid layer: only 400 m thick Ice layer: extends 1500 m below liquidIce layer: extends 1500 m below liquid Again, supplemental sounding launched at LBFAgain, supplemental sounding launched at LBF

Varied solar zenith angleVaried solar zenith angle Ascent of 0.7 cm sAscent of 0.7 cm s-1-1

Verification methodsVerification methods

1. Comparisons of observed versus simulated 1. Comparisons of observed versus simulated profiles at end of spinup (t = 61 min)profiles at end of spinup (t = 61 min)– Simulated profiles tuned to match observationsSimulated profiles tuned to match observations

2. Comparisons of observed versus simulated snow 2. Comparisons of observed versus simulated snow mixing ratio at t = 90 minmixing ratio at t = 90 min– Snow profiles NOT tuned to match observationsSnow profiles NOT tuned to match observations– t = 90 min is selected to account for microphysical t = 90 min is selected to account for microphysical

spinupspinup

3. Examination of time series evolution for liquid and 3. Examination of time series evolution for liquid and snowsnow

Simulation already saturated

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Budget analysisBudget analysis

Which model processes are important?Which model processes are important? We evaluate budget equationsWe evaluate budget equations Large scale and microphysical processes includedLarge scale and microphysical processes included

– We focus primarily on microphysicsWe focus primarily on microphysics Small or negligible contributions neglectedSmall or negligible contributions neglected Individual budget terms (including negligible Individual budget terms (including negligible

terms) add up to equal total tendencyterms) add up to equal total tendency Cloud water and snow budgets are examinedCloud water and snow budgets are examined We observe from t = 91 min to t = 150 min, to We observe from t = 91 min to t = 150 min, to

account for microphysical spinupaccount for microphysical spinup

Budget equationsBudget equations

where:where:Mix = change due to turbulent mixingMix = change due to turbulent mixingAscent = change due to large-scale ascentAscent = change due to large-scale ascentRad = change due to radiative forcingRad = change due to radiative forcingSediment = change due to the motion of falling snow (“sedimentation”)Sediment = change due to the motion of falling snow (“sedimentation”)PSACW = change due to snow collecting cloud waterPSACW = change due to snow collecting cloud waterPSDEP = change due to depositional growth of snow PSDEP = change due to depositional growth of snow PDEPI = change due to depositional growth of cloud icePDEPI = change due to depositional growth of cloud icePCONV = conversion of cloud ice to snowPCONV = conversion of cloud ice to snow

cccccc rrrrrrc

t

rPDEPIPSDEPPSACWRadAscentMix

SSSSS rrrrrs

t

rPCONVPSDEPPSACWSedimentMix

Major observations from budgetsMajor observations from budgets

Most important microphysical process: Most important microphysical process: Depositional growth of snowDepositional growth of snow

Other microphysical processes generate Other microphysical processes generate smaller effectssmaller effects

Balance between depositional growth of Balance between depositional growth of snow and sedimentation in Oct.14 and snow and sedimentation in Oct.14 and Nov.02 casesNov.02 cases

Time tendency of snow is relatively small, Time tendency of snow is relatively small, except with strong descent in Nov.11 caseexcept with strong descent in Nov.11 case

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Analytic equationsAnalytic equations

Useful to predict snow mixing ratio and Useful to predict snow mixing ratio and precipitation fluxprecipitation flux

Analytic equations allow for simple Analytic equations allow for simple predictions without a lot of informationpredictions without a lot of information

Formulas are derived from RH83Formulas are derived from RH83

Simplified snow budgetSimplified snow budget

PSDEP

dt

rwd ss

Presumptions: Presumptions: – Sedimentation balances depositional growth Sedimentation balances depositional growth

exactlyexactly– Steady state processes (no time tendency)Steady state processes (no time tendency)– Other microphysical terms are negligibleOther microphysical terms are negligible

Unknown variables:Unknown variables:– p p (pressure)(pressure)– ρρ (air density) (air density)– T T (temperature)(temperature)

– zztoptop (liquid cloud top altitude) (liquid cloud top altitude)

– zz (liquid cloud base altitude) (liquid cloud base altitude)

– SSii (fraction of saturation with respect to ice) (fraction of saturation with respect to ice)

– eesisi (saturation vapor pressure) (saturation vapor pressure)

By using a reasonable estimate for each unknown variable, we can explicitly solve these equations.By using a reasonable estimate for each unknown variable, we can explicitly solve these equations.

Analytic formulasAnalytic formulas

bb

topstop

b

Ss rzzNb

b

c

cr

1

1,102

3

1

b

b

topbSssPSDEP zzb

bccNrwF

1

3

1

20 1

11141

01

D

FBA

Sc i

13

0

4.0

02 6

1

1

1

b

S Db

p

pac

11

3013 6

1

Dcc s

1

TR

L

TK

LA

v

v

a

v

si

v

e

TRB

OutlineOutline


Methods for verifying analytic Methods for verifying analytic formulaformula

Completed a series of sensitivity simulationsCompleted a series of sensitivity simulations No collection processes used in studyNo collection processes used in study Adjusted variables:Adjusted variables:

– Large scale ascentLarge scale ascent– Variable Variable a’’a’’ (affects snow fall velocity) (affects snow fall velocity)

– Variable Variable NN0S0S (affects snow particle number (affects snow particle number

concentration)concentration)

Total number of sensitivity simulations:Total number of sensitivity simulations:

17 different settings x 3 cloud cases = 51 total17 different settings x 3 cloud cases = 51 total

Formula verificationFormula verification

For each simulation, observation time is For each simulation, observation time is based on when peak snow mixing ratio based on when peak snow mixing ratio occursoccurs

Diagnosed value of snow mixing ratio and Diagnosed value of snow mixing ratio and snow precipitation flux obtained directly from snow precipitation flux obtained directly from simulation resultssimulation results

Analytic results also calculated with inputs Analytic results also calculated with inputs from simulationfrom simulation

Results plotted using scatter plotResults plotted using scatter plot

Formula underpredicts mixing ratio

Formula overpredicts mixing ratio

Line indicates equality

Results from our verificationResults from our verification

Formulas consistently underpredict snow mixing Formulas consistently underpredict snow mixing ratio and precipitation fluxratio and precipitation flux

Underprediction is likely due to neglected terms, Underprediction is likely due to neglected terms, especially time tendencyespecially time tendency

We still need to evaluate tendencies of the We still need to evaluate tendencies of the individual resultsindividual results

A multiplicative or additive factor could be usefulA multiplicative or additive factor could be useful

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ConclusionsConclusions

For this study, we simulate three mixed-phase alto cloudsFor this study, we simulate three mixed-phase alto clouds Depositional growth is the strongest microphysical process Depositional growth is the strongest microphysical process

affecting liquid and snowaffecting liquid and snow Sedimentation of snow nearly balances depositional growthSedimentation of snow nearly balances depositional growth Time tendency of snow mixing ratio is nearly zero in two Time tendency of snow mixing ratio is nearly zero in two

simulationssimulations A series of simple analytic equations was derived from A series of simple analytic equations was derived from

budget observations. budget observations. Analytic equations require inputs that can easily be Analytic equations require inputs that can easily be

estimatedestimated Equations consistently underestimate snow properties, but Equations consistently underestimate snow properties, but

they still provide accurate and useful information.they still provide accurate and useful information.

Future workFuture work

Examine log-log plot results more closelyExamine log-log plot results more closely– Why does the analytic formula produce different results Why does the analytic formula produce different results

when the simulation predicts almost exactly the same when the simulation predicts almost exactly the same value?value?

Can we factor time tendency into our equations?Can we factor time tendency into our equations? What about other neglected terms?What about other neglected terms? How do equations perform when analyzing How do equations perform when analyzing

different snow habits?different snow habits? Test equations versus a more robust microphysics Test equations versus a more robust microphysics

scheme.scheme.

AcknowledgementsAcknowledgements Professor Vince Larson for advising my researchProfessor Vince Larson for advising my research My research associates: Michael Falk, Dave Schanen, My research associates: Michael Falk, Dave Schanen,

Brian Griffin, Brandon Nielsen, and Joshua Fasching for Brian Griffin, Brandon Nielsen, and Joshua Fasching for working with me on various computer and scientific issuesworking with me on various computer and scientific issues

Dr. Jean-Christophe Golaz (NOAA / GFDL) for providing Dr. Jean-Christophe Golaz (NOAA / GFDL) for providing technical assistance with COAMPS-LEStechnical assistance with COAMPS-LES

Dr. Larry Carey (ESSC / Univ. of Alabama Huntsville), Dr. Dr. Larry Carey (ESSC / Univ. of Alabama Huntsville), Dr. Jingguo Niu (Texas A&M Univ.), and Dr. J. Adam Jingguo Niu (Texas A&M Univ.), and Dr. J. Adam Kankiewicz for providing vital aircraft and rawinsonde dataKankiewicz for providing vital aircraft and rawinsonde data

My fellow graduate students, family, friends, and my My fellow graduate students, family, friends, and my fiancefianceéé Apryle Apryle

Viewers Like YouViewers Like You

COAMPSCOAMPS®® is a registered trademark of the Naval is a registered trademark of the Naval Research LaboratoryResearch Laboratory

Any questions?Any questions?

ReferencesReferences Ackerman, T. P. et al., 2004: Atmospheric Radiation Measurement program science Ackerman, T. P. et al., 2004: Atmospheric Radiation Measurement program science

plan. plan. http://www.arm.gov/sciencehttp://www.arm.gov/science Cober, S. .G. and G. A. Isaac, 2002: Aircraft icing environments observed in mixed-Cober, S. .G. and G. A. Isaac, 2002: Aircraft icing environments observed in mixed-

phase clouds. Preprints, phase clouds. Preprints, 4040thth Aerospace Sciences Meeting & Exhibit Aerospace Sciences Meeting & Exhibit, Reno, NV, , Reno, NV, American Institute of Aeronautics and AstronauticsAmerican Institute of Aeronautics and Astronautics

Cooper, W. A., 1986: Ice initiation in natural clouds. Cooper, W. A., 1986: Ice initiation in natural clouds. Precipitation Enhancement – A Precipitation Enhancement – A Scientific ChallengeScientific Challenge, Meteor. Monogr., No. 43, Amer. Meteor. Soc., 29-32., Meteor. Monogr., No. 43, Amer. Meteor. Soc., 29-32.

Falk, M. J. and V. E. Larson, 2007: What causes partial cloudiness to form in Falk, M. J. and V. E. Larson, 2007: What causes partial cloudiness to form in multilayered midlevel clouds? A simulated case study. multilayered midlevel clouds? A simulated case study. J. Geophys. Res.J. Geophys. Res., , 112 (D12206)112 (D12206), , doi:10.1029/2006JD007666.doi:10.1029/2006JD007666.

Fleishauer, R. P., V. E. Larson, and T. H. Vonder Haar, 2002: Observed microphysical Fleishauer, R. P., V. E. Larson, and T. H. Vonder Haar, 2002: Observed microphysical structure of midlevel, mixed-phase clouds. structure of midlevel, mixed-phase clouds. J. Atmos. Sci.J. Atmos. Sci., , 5959, 1779-1804., 1779-1804.

Fletcher, N. H., 1962: Fletcher, N. H., 1962: The Physics of RaincloudsThe Physics of Rainclouds. Cambridge University Press, 386 pp.. Cambridge University Press, 386 pp. Fowler, L. D., D. A. Randall, and S. A. Rutledge, 1996: Liquid and ice cloud microphysics 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 in the CSU general circulation model. Part I: Model description and simulated microphysical processes. microphysical processes. J. ClimateJ. Climate, , 99, 489-529., 489-529.

Golaz, J.-C., S. Wang, J. D. Doyle, and J. M. Schmidt, 2005: Second and third moment Golaz, J.-C., S. Wang, J. D. Doyle, and J. M. Schmidt, 2005: Second and third moment vertical velocity budgets derived from COAMPS-LES. vertical velocity budgets derived from COAMPS-LES. Bound.-Layer Meteor.Bound.-Layer Meteor., , 116116, 487-, 487-517.517.

http://www.arm.gov/science

ReferencesReferences Haulman, D. L. 2003: U. S. unmanned aerial vehicles in combat, 1991-2003. Report, Air Haulman, D. L. 2003: U. S. unmanned aerial vehicles in combat, 1991-2003. Report, Air

Force Historical Research Agency, 19 pp., Maxwell AFB, AL.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 Larson, V. E., A. J. Smith, M. J. Falk, K. E. Kotenberg, and J.-C. Golaz, 2006: What

determines altocumulus dissipation time? determines altocumulus dissipation time? J. Geophys. Res.J. Geophys. Res., , 111 (D19207)111 (D19207), , doi:10.1029/2005JD007002.doi:10.1029/2005JD007002.

Liou, K. N., 2002: Liou, K. N., 2002: An Introduction to Atmospheric RadiationAn Introduction to Atmospheric Radiation. 2. 2ndnd ed., Academic Press, ed., Academic Press, 583 pp.583 pp.

Rutledge, S. A. and P. V. Hobbs, 1983: The mesoscale and microscale structure and 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 organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the seeder-feeder process in warm-frontal rainbands. seeder-feeder process in warm-frontal rainbands. J. Atmos. Sci.J. Atmos. Sci., , 4040, 1185-1206., 1185-1206.

Sassen, K. and V. I. Khvorostyanov, 2007: Microphysical and radiative properties of Sassen, K. and V. I. Khvorostyanov, 2007: Microphysical and radiative properties of mixed-phase altocumulus: A model evaluation of glaciation effects. mixed-phase altocumulus: A model evaluation of glaciation effects. Atm. Res.Atm. Res., , 8484, 390-, 390-398398

Vonder Haar, T. H. et al., 1997: Overview and objectives of the DoD Center for Vonder Haar, T. H. et al., 1997: Overview and objectives of the DoD Center for Geosciences sponsored “Complex Layered-Cloud Experiment”. Preprints, Geosciences sponsored “Complex Layered-Cloud Experiment”. Preprints, Cloud Cloud Impacts on DoD Operations and Systems ConfImpacts on DoD Operations and Systems Conf., Newport, RI, Phillips Laboratories, 163-., Newport, RI, Phillips Laboratories, 163-169.169.

Warren, S. G., C. J. Hahn, J. London, R. M. Chervin, and R. Jenne, 1988: Global 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. distribution of total cloud cover and cloud type amount over land. Tech. Report NCAR Tech. Report NCAR TN-317 STRTN-317 STR, Natl. Cent. for Atmos. Res., Boulder, CO., Natl. Cent. for Atmos. Res., Boulder, CO.

Zhang, M. H. et al., 2005: Comparing clouds and their seasonal variations in 10 Zhang, M. H. et al., 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurement. atmospheric general circulation models with satellite measurement. J. Geophys. ResJ. Geophys. Res., ., 110 (D15S02)110 (D15S02), doi:10.1029/2004JD005021., doi:10.1029/2004JD005021.

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