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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
2
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
4
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 )
5
Reality v.s. GCM Reality v.s. GCM
• Using Plane Parallel Homogeneous (PPH) approximation
• Using Plane Parallel Homogeneous (PPH) approximation
6
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
7
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
8
Why PPH can’t represent reality ? Why PPH can’t represent reality ?
PPH
without homogeneities
ICA
With homogeneities
9
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
10
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
11
• 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
13
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
14
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)
15
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
16
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
17
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
18
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
19
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
20
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)
21
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
22
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)
23
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
24
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.
25
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
26
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
31
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
32
ψ can be many forms including morlet, Mexican hat …ψ can be many forms including morlet, Mexican hat …
33
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
34
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
36
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
37
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
38
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
39
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
40
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