1. The Homogeneity of Midlatitude Cirrus Cloud Structural Properties Analyzed from the Extended FARS Dataset Likun Wang Ph.D. Candidate Likun Wang Ph.D. Candidate

2. 2 Content 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. 3 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. 4 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. 5 Reality v.s. GCM Using Plane Parallel Homogeneous (PPH) approximation

6. 6 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. 7 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. 8 Why PPH can’t represent reality ? PPH without homogeneities ICA With homogeneities

9. 9 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 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. 10 PPH v.s. ICA ------Albedo Bias Bias Optical Thickness τ1τ1 τ2τ2 τmτm α ICA α PPH Albedo α PPH> α ICAOverestimate Carlin et al. personal communication; Cahalan et al. 1994; Barker,1996

11. 11 OLR(ICA)-OLR(PPA) ~ 14 W/m - 2 ( Fu et al. 2000) PPH v.s. ICA ------ OLR Bias OLR PPH< OLR ICA Underestimate Bias

12. 12

13. 13 Inhomogeneous structure observed from cases study AuthorInhomogeneous structure Length Scale (KM)InstrumentsComments Heymsfield (1975) Uncinus top generating cell1-2Radar, aircraft observationMinnesota, Illinois, Colorado, Wyoming. Auria and Campistron (1987) cirrus generating cell1.3 and 0.7RadarPEP * 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) MUC Small scale cellular structure 20-500Rawinsonde and satellite observation FIRE data, Wisconsin, 1986 Sassen et al. (1990) MUC cirrus uncinus cell ~120 ~1 Lidar and aircraft observationFIRE data, Wisconsin, 1986 Grund and Eloranta (1990) MUC4-12LidarFIRE data, Wisconsin, 1986 Smith et al. (1990) Convective cell4-10Aircraft observationFIRE data, Wisconsin, 1986 Gultepe and Starr (1995) Gravity waves Quasi-two-dimensional waves Larger two-dimensional esoscale wave 2-9 10-20 100 Aircraft observationFIRE data, Wisconsin, 1986 Gultepe et al. (1995) Coherent Structure 0.2-10 Radar and Aircraft observationFIRE II data, Kansas, 1991 Smith and Jonas (1996) Convective cell Gravity waves Turbulence 2 0.05-0.6 Aircraft observationEUCREX **, England, Scotland, Iceland, 1993 Demoz et al. (1998)Convective cell Gravity waves 1.2 2-40 Aircraft observationSUCCESS ***, Oklahoma, 1996

14. 14 How about cirrus? the complexity of internal structure exists –scale: 10 -2 ~ 10 5 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 ~ 10 5 m –Include: Turbulence Kelvin-Helmholtz waves Small scale cellular structure, convective cell Gravity waves Mesoscale Unicinus Complexes (MUC)

15. 15 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. 16 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. 17 Content 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. 18 FARS 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. 19 Ruby lidar – –T wo 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 maximu m output

20. 20 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) 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. 21 FARS Data (Oct. 1987 - Dec. 2001) Total: 3216 hours

22. 22 FARS Data per month Max: 404 hours(OCT) Min: 177 hours (JUN) Max: 404 hours(OCT) Min: 177 hours (JUN)

23. 23 Content 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. 24 Signal from lidar P 0 is the power output (J), c speed of the light (m s -1 ), t the pulse length (m), A r the receiver collecting area (m 2 ),  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. 25 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)R 2 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)R 2

26. 26 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

27. 27

28. 28 Why wavelet?

29. 29 Spectrum of two process (Fourier transform)

30. 30 But using wavelet

31. 31 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

32. 32 ψ can be many forms including morlet, Mexican hat …

33. 33 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

34. 34 Content 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. 35 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. 36 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 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. 37 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 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. 38 Proposed 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. 39 Purpose of research cloud fractioncloud overlaplength scale of cloud distribution FARS lidar dataradiosonde data spatial series data wavelet methodcloud detection method Final Purpose is: Characterize the vertical and horiziontal inhomogeneities of midlatitude cirrus cloud

40. 40 Purpose of research (con’t) Characteristics from data analysis Radiation Transfer Model LW Radiation BiasAlbedo Bias Final Purpose is: Quantify the radiative bias due to the neglect of midlatitude cirrus cloud inhomogeneities using radiation transfer models

41. 41 Thank you! Need hard work!