The Homogeneity of Midlatitude Cirrus Cloud

Structural Properties Analyzed from the

Extended FARS Dataset

The Homogeneity of Midlatitude Cirrus Cloud

Structural Properties Analyzed from the

Extended FARS Dataset

Likun WangPh.D. Candidate

Likun WangPh.D. Candidate

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ContentContent

I. Motivation

II. FARS high cloud dataset

III. Proposed Method

IV. Proposed future research

I. Motivation

II. FARS high cloud dataset

III. Proposed Method

IV. Proposed future research

3

Why are cirrus clouds important?Why are cirrus clouds important?

• Influence on the radiation balance of the climate system (Liou, 1986)– Macrophysical properties

• Cloud top, base, thickness, cover, overlap

– Microphysical properties

• Ice water content (IWC) and ice crystal size

distribution

• Ice crystal habit

• Influence on the radiation balance of the climate system (Liou, 1986)– Macrophysical properties

• Cloud top, base, thickness, cover, overlap

– Microphysical properties

• Ice water content (IWC) and ice crystal size

distribution

• Ice crystal habit

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Why are cirrus clouds important? (con’t)Why are cirrus clouds important? (con’t)

• Important in the chemistry of the upper troposphere– Contribute to upper troposphere ozone depletion

(Borrman et al. 1996; Kley et al. 1996)

– Perturb chlorine chemistry (Solomon et al. 1997 )

• Important in the chemistry of the upper troposphere– Contribute to upper troposphere ozone depletion

(Borrman et al. 1996; Kley et al. 1996)

– Perturb chlorine chemistry (Solomon et al. 1997 )

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Reality v.s. GCM Reality v.s. GCM

• Using Plane Parallel Homogeneous (PPH) approximation

• Using Plane Parallel Homogeneous (PPH) approximation

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Reality v.s. GCM (con’t)Reality v.s. GCM (con’t)

• No horizontal inhomogeneities

– e.g. the distribution characteristics of cloudy and clear sky regions

– e.g. the horizontal variability of microphysical properties within a layer

• No horizontal inhomogeneities

– e.g. the distribution characteristics of cloudy and clear sky regions

– e.g. the horizontal variability of microphysical properties within a layer

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Reality v.s. GCM (con’t)Reality v.s. GCM (con’t)

• Limited vertical inhomogeneities

– e.g. How clouds overlap?

• maximum overlap for adjacent levels & random overlap

for non adjacent levels is assumed

– e.g. the vertical variability of microphysical properties within a layer

• Limited vertical inhomogeneities

– e.g. How clouds overlap?

• maximum overlap for adjacent levels & random overlap

for non adjacent levels is assumed

– e.g. the vertical variability of microphysical properties within a layer

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Why PPH can’t represent reality ? Why PPH can’t represent reality ?

PPH

without homogeneities

ICA

With homogeneities

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PPH v.s. ICAPPH v.s. ICA

• Independent column approximation (ICA)

– Sliced grid box into different column

– Radiative transfer calculations of a cloud field are done in for every column

– then an average value is determined

• Independent column approximation (ICA)

– Sliced grid box into different column

– Radiative transfer calculations of a cloud field are done in for every column

– then an average value is determined

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PPH v.s. ICA ------Albedo BiasPPH v.s. ICA ------Albedo Bias

Bias

Optical Thicknessτ1 τ2τm

αICA

αPPH

Albedo

αPPH> αICA

OverestimateOverestimate

Carlin et al. personal communication; Cahalan et al. 1994; Barker,1996Carlin et al. personal communication; Cahalan et al. 1994; Barker,1996

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• OLR(ICA)-OLR(PPA) ~ 14 W/m- 2 (Fu et al. 2000)• OLR(ICA)-OLR(PPA) ~ 14 W/m- 2 (Fu et al. 2000)

PPH v.s. ICA ------ OLR BiasPPH v.s. ICA ------ OLR Bias

OLRPPH< OLRICA

UnderestimateBias

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Inhomogeneous structure observed from cases study Inhomogeneous structure observed from cases study

Author Inhomogeneous structureLength Scale

(KM) Instruments Comments

Heymsfield (1975)

 

Uncinus top generating cell 1-2 Radar, aircraft observation Minnesota, Illinois, Colorado, Wyoming.

Auria and Campistron (1987)

 cirrus generating cell 1.3 and 0.7 Radar PEP* project, in Spain, 1987.

Sassen et al. (1989)

Mesoscale Unicinus Complexes (MUC)cirrus uncinus cell

~15- ~100  ~1

Lidar, radar and aircraft observation

FIRE data, Colorado,(1983), Utah(1985), Wisconsin(1986).

 Starr and Wylie(1990)

MUCSmall scale cellular structure

20-500 Rawinsonde and satellite observation

FIRE data, Wisconsin, 1986

 

Sassen et al. (1990)

 

MUCcirrus uncinus cell

~120~1

Lidar and aircraft observation FIRE data, Wisconsin, 1986

Grund and Eloranta(1990)

 

MUC 4-12 Lidar FIRE data, Wisconsin, 1986

Smith et al.(1990)

 

Convective cell 4-10 Aircraft observation FIRE data, Wisconsin, 1986

Gultepe and Starr (1995)

Gravity wavesQuasi-two-dimensional waves Larger two-dimensional esoscale wave

 

2-910-20100

Aircraft observation FIRE data, Wisconsin, 1986

Gultepe et al. (1995)

 

Coherent Structure

 0.2-10

 Radar and Aircraft observation FIRE II data, Kansas, 1991

Smith and Jonas (1996)

Convective cellGravity wavesTurbulence

 

220.05-0.6

Aircraft observation EUCREX**, England, Scotland, Iceland, 1993

Demoz et al. (1998) Convective cell Gravity waves

1.22-40

Aircraft observation SUCCESS***, Oklahoma, 1996

 

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How about cirrus? How about cirrus?

• the complexity of internal structure exists– scale: 10-2 ~ 105 m

– Include:

• Turbulence

• Kelvin-Helmholtz waves

• Small scale cellular structure, convective cell

• Gravity waves

• Mesoscale Unicinus Complexes (MUC)

• the complexity of internal structure exists– scale: 10-2 ~ 105 m

– Include:

• Turbulence

• Kelvin-Helmholtz waves

• Small scale cellular structure, convective cell

• Gravity waves

• Mesoscale Unicinus Complexes (MUC)

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How about cirrus? (con’t)How about cirrus? (con’t)

• Starr and Cox (1985) – embedded cellular structures develop in the

simulation of cirrostratus cloud layer

– horizontal scales : ~1 km or less

• Dobbie and Jonas (2001) – radiation could have an important effect on

cirrus clouds inhomogeneity

• Starr and Cox (1985) – embedded cellular structures develop in the

simulation of cirrostratus cloud layer

– horizontal scales : ~1 km or less

• Dobbie and Jonas (2001) – radiation could have an important effect on

cirrus clouds inhomogeneity

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Big difficulties: Big difficulties:

• Case analysis is not enough to disclose the characteristics of cirrus clouds inhomogeneities – Need a high resolution and long-term datasets

• Different scale processes often happen together and coexist in the same cloud system and not easy to locate – Need an efficient analysis tool

• Case analysis is not enough to disclose the characteristics of cirrus clouds inhomogeneities – Need a high resolution and long-term datasets

• Different scale processes often happen together and coexist in the same cloud system and not easy to locate – Need an efficient analysis tool

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ContentContent

I. Motivation

II. FARS high cloud dataset

III. Proposed Method

IV. Proposed future research

I. Motivation

II. FARS high cloud dataset

III. Proposed Method

IV. Proposed future research

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FARS SiteFARS Site

• Located 40 49’00’’N, 111 49’38”W• Instruments

– Passive Remote Sensors

– Active Remote Sensors

• Polarization Cloud Lidar (PCL) ---Ruby lidar

• Two-color Polarization Diversity Lidar (PDL)

• 95 GHz Polarimetric Doppler Radar

• Located 40 49’00’’N, 111 49’38”W• Instruments

– Passive Remote Sensors

– Active Remote Sensors

• Polarization Cloud Lidar (PCL) ---Ruby lidar

• Two-color Polarization Diversity Lidar (PDL)

• 95 GHz Polarimetric Doppler Radar

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Ruby lidarRuby lidar

–Two channels

– Vertical polarization transmitted

– Manually "tiltable" ± 5° from zenith

– 0 .1 Hz PRF, 7.5 m maximum range resolution

– Maximum 2K per channel data record length

– 1-3 mrad receiver beamwidths

– 25 cm diameter telescope

– 0.694 µm wavelength, 1.5J maximum output

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FARS high cloud datasetFARS high cloud dataset

• October,1987 --- Now• Typical 3-hour data (10 sec resolution)

– Using the average wind speed: 25 m/s

– Spatial scale : 250 m ~ 270 km

• Mainly focus on higher, colder and thinner cirrus cloud independent with low clouds (lidar limit)

• October,1987 --- Now• Typical 3-hour data (10 sec resolution)

– Using the average wind speed: 25 m/s

– Spatial scale : 250 m ~ 270 km

• Mainly focus on higher, colder and thinner cirrus cloud independent with low clouds (lidar limit)

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0

50

100

150

200

250

300

350

400

450

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001Year

Obs

erva

tion

hou

rs

FARS Data (Oct. 1987 - Dec. 2001)FARS Data (Oct. 1987 - Dec. 2001)

Total: 3216 hoursTotal: 3216 hours

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FARS Data per monthFARS Data per month

0

50

100

150

200

250

300

350

400

450

JAN FEB MAR APR MAY JUN JUL AUG SPT OCT NOV DEC

Month

Ob

serv

ati

on

ho

urs

Max: 404 hours(OCT)

Min: 177 hours (JUN)

Max: 404 hours(OCT)

Min: 177 hours (JUN)

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ContentContent

I. Motivation

II. FRAS high cloud dataset

III. Proposed Method

IV. Proposed future research

I. Motivation

II. FRAS high cloud dataset

III. Proposed Method

IV. Proposed future research

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Signal from lidar Signal from lidar

])()

0

)()((2exp[))()(220

dRRR

RR

cR

mR

cβR

m)(β

R

rctA(PP(R)

•P0 is the power output (J) , •c speed of the light (m s-1),• t the pulse length (m), •Ar the receiver collecting area (m2),• the volume backscatter coefficient (m sr)-1,• the volume extinction coefficient area (m-1), the multiple forward-scattering correction factor. •m and c denote contributions from molecules and cloud.

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Signal from lidarSignal from lidar

• Calibrate the scattering and extinction due to air molecules under the pure molecular scattering assumption (Sassen 1994)

• Assume a relationship (Klett 1984):

• It is possible to gather the information on inhomogeneous properties by analyzing P(R)•R2

• Calibrate the scattering and extinction due to air molecules under the pure molecular scattering assumption (Sassen 1994)

• Assume a relationship (Klett 1984):

• It is possible to gather the information on inhomogeneous properties by analyzing P(R)•R2

kconst

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From Time series to spatial series data From Time series to spatial series data

• Assume that the internal cloud properties vary much more with space than with typical observation periods

• Also assume cirrus moves faster horizontally than vertically

• Using radiosonde data, we can transfer time series data to spatial series data

• Assume that the internal cloud properties vary much more with space than with typical observation periods

• Also assume cirrus moves faster horizontally than vertically

• Using radiosonde data, we can transfer time series data to spatial series data

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Why wavelet?Why wavelet?

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Spectrum of two process (Fourier transform)

Spectrum of two process (Fourier transform)

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But using wavelet But using wavelet

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Continuous Wavelet Transform (CWT)Continuous Wavelet Transform (CWT)

• the element transform wavelet function can be defined :

– Where

• τ is translation parameters

• s is scale parameters

• the element transform wavelet function can be defined :

– Where

• τ is translation parameters

• s is scale parameters

)()(

1)(

2/1, s

t

sts

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ψ can be many forms including morlet, Mexican hat …ψ can be many forms including morlet, Mexican hat …

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Continuous Wavelet Transform (CWT)Continuous Wavelet Transform (CWT)

• CWT is defined as follows :

Where

• x(t) is the signal

• Ψ*(t) is the wavelet function

• τ and s , the translation and scale parameters,

respectively

• CWT is defined as follows :

Where

• x(t) is the signal

• Ψ*(t) is the wavelet function

• τ and s , the translation and scale parameters,

respectively

dts

ttx

ssW )( )(

)(

1),( *

2/1

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ContentContent

I. Motivation

II. FRAS high cloud dataset

III. Proposed Method

IV. Proposed future research

I. Motivation

II. FRAS high cloud dataset

III. Proposed Method

IV. Proposed future research

35

Proposed future work Proposed future work

• Examining structural inhomogeneity of broken cirrus cloud cases – Determining the statistics of broken cirrus

fractional cloud amounts

– Determining cloud layer overlap for multiple layer cirrus clouds without low water clouds

– Creating the relationship between the cloud top temperature and the length scales of cloud distribution

• Examining structural inhomogeneity of broken cirrus cloud cases – Determining the statistics of broken cirrus

fractional cloud amounts

– Determining cloud layer overlap for multiple layer cirrus clouds without low water clouds

– Creating the relationship between the cloud top temperature and the length scales of cloud distribution

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Proposed future workProposed future work

• Examining inhomogeneous properties in ‘homogeneous’ cirrus – Check all the cirrostratus cases

– Locate inner inhomogeneous dynamics process such as gravity waves, Kelvin-Helmholtz waves and convective cell

– Evaluate statistics characteristics of these process

• Examining inhomogeneous properties in ‘homogeneous’ cirrus – Check all the cirrostratus cases

– Locate inner inhomogeneous dynamics process such as gravity waves, Kelvin-Helmholtz waves and convective cell

– Evaluate statistics characteristics of these process

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Proposed future workProposed future work

• Furthering the knowledge of cirrus cloud structures and the dynamics to the major cloud generating mechanisms– Classified into four kinds type

– Check every type’s inner structures

– Try to find the relationship between inner structures and dynamics

• Furthering the knowledge of cirrus cloud structures and the dynamics to the major cloud generating mechanisms– Classified into four kinds type

– Check every type’s inner structures

– Try to find the relationship between inner structures and dynamics

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Proposed future workProposed future work

• Calculating the bias of radiative quantities due to the neglect of cirrus cloud inhomogeneities– Use Fu and Liao’s radiation transfer model

– Structural characteristics

– Quantify the bias of albedo and OLR between ICA and PPH

• Calculating the bias of radiative quantities due to the neglect of cirrus cloud inhomogeneities– Use Fu and Liao’s radiation transfer model

– Structural characteristics

– Quantify the bias of albedo and OLR between ICA and PPH

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Purpose of research Purpose of research

cloud fraction cloud overlap length scale of cloud distribution

FARS lidar data radiosonde data

spatial series data

wavelet methodcloud detection method

Final Purpose is:

Characterize the vertical and horiziontal Characterize the vertical and horiziontal inhomogeneities of midlatitude cirrus cloudinhomogeneities of midlatitude cirrus cloud

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Purpose of research (con’t)Purpose of research (con’t)

Characteristics from data analysis

Radiation Transfer Model

LW Radiation Bias Albedo Bias

Final Purpose is:

Quantify the radiative bias due to the neglect of Quantify the radiative bias due to the neglect of midlatitude cirrus cloud inhomogeneities using midlatitude cirrus cloud inhomogeneities using

radiation transfer modelsradiation transfer models

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Thank you!

Need hard work!

Thank you!

Need hard work!