The Homogeneity of Midlatitude Cirrus Cloud Structural Properties Analyzed from the Extended FARS Dataset

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The Homogeneity of Midlatitude Cirrus Cloud
Structural Properties Analyzed from the Extended
FARS Dataset

• Likun Wang
• Ph.D. Candidate

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Content

1. Motivation
2. FARS high cloud dataset
3. Proposed Method
4. Proposed future research

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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

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Why are cirrus clouds important? (cont)

• 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

• Using Plane Parallel Homogeneous (PPH)
  approximation

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Reality v.s. GCM (cont)

• 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 (cont)

• 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 cant represent reality ?
PPH without homogeneities
ICA With homogeneities
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PPH 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

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PPH v.s. ICA ------Albedo Bias
aPPHgt aICA Overestimate
aPPH
Bias
aICA
Albedo
t2
tm
Optical Thickness
t1

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

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

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

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Inhomogeneous structure observed from cases study
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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)

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How about cirrus? (cont)

• 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

• 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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Content

1. Motivation
2. FARS high cloud dataset
3. Proposed Method
4. Proposed future research

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FARS Site

• Located 40? 4900N, 111? 4938W
• 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 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 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)

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FARS Data (Oct. 1987 - Dec. 2001)

• Total 3216 hours

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FARS Data per month
Max 404 hours(OCT) Min 177 hours (JUN)
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Content

• Motivation
• FRAS high cloud dataset
• Proposed Method
• Proposed future research

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Signal from lidar

• 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 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

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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

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Why wavelet?
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Spectrum of two process (Fourier transform)
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But using wavelet
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Continuous Wavelet Transform (CWT)

• the element transform wavelet function can be
  defined
• Where
• t is translation parameters
• s is scale parameters

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? can be many forms including morlet, Mexican hat

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Continuous Wavelet Transform (CWT)

• CWT is defined as follows
• Where
• x(t) is the signal
• ?(t) is the wavelet function
• t and s , the translation and scale parameters,
  respectively

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Content

• Motivation
• FRAS high cloud dataset
• Proposed Method
• Proposed future research

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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

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Proposed 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

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Proposed 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 types inner structures
• Try to find the relationship between inner
  structures and dynamics

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Proposed future work

• Calculating the bias of radiative quantities due
  to the neglect of cirrus cloud inhomogeneities
• Use Fu and Liaos radiation transfer model
• Structural characteristics
• Quantify the bias of albedo and OLR between ICA
  and PPH

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Purpose of research
FARS lidar data
radiosonde data
Final Purpose is Characterize the vertical and
horiziontal inhomogeneities of midlatitude cirrus
cloud
spatial series data
wavelet method
cloud detection method
cloud fraction
cloud overlap
length scale of cloud distribution
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Purpose of research (cont)
Final Purpose is Quantify the radiative bias
due to the neglect of midlatitude cirrus cloud
inhomogeneities using radiation transfer models
Characteristics from data analysis
Radiation Transfer Model
LW Radiation Bias
Albedo Bias
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Thank you! Need hard work!