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Development of cloud resolving model with microphysical bin model and parameterizations

Development of cloud resolving model with microphysical bin model and parameterizations to predict the initial cloud droplet size distribution KUBA, Naomi Frontier Research Center for Global Change (FRCGC / JAMSTEC) kuba@jamstec.go.jp

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Development of cloud resolving model with microphysical bin model and parameterizations

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  1. Development of cloud resolving model with microphysical bin model and parameterizations to predict the initial cloud droplet size distribution KUBA, Naomi Frontier Research Center for Global Change (FRCGC / JAMSTEC) kuba@jamstec.go.jp ICMW 2004, 2004, 7, 13, Hamburg

  2. Contents • Introduction • Parameterizations to predict cloud droplet number • Parameterizations to predict cloud droplet size distribution • Comparison between the parameterization and particle method • Results of numerical experiments • Conclusions

  3. 1. Introduction Purpose Cloud microphysical model 3D non-hydrostatic regional model CCN Spectrum Number of cloud droplets Smax Size distribution of cloud droplets Updraft Rain water Generation Efficiency Optical properties

  4. 1. Introduction CCN Spectrum Number of cloud droplets Smax Size distribution of cloud droplets Updraft Rain water Generation Efficiency Bin method (Eulerian framework) Optical properties

  5. 1. Introduction CCN Spectrum Number of cloud droplets Smax Size distribution of cloud droplets Updraft Rain water Generation Efficiency Parcel model with particle method (Lagrangian framework) Optical properties

  6. 1. Introduction CCN Spectrum Number of cloud droplets Smax Size distribution of cloud droplets Updraft Rain water Generation Efficiency Parameterization derived from numerical experiments using parcel model Optical properties

  7. Contents • Introduction • Parameterizations to predict cloud droplet number • Parameterizations to predict cloud droplet size distribution • Comparison between the parameterization and particle method • Results of numerical experiments • Conclusions

  8. 2. Parameterization to predict cloud droplet number Nd= A Nc(S) / (Nc(S) + B )Vbase< 0.24m s -1  S = 0.2 % A = 4710 Vbase1.19 B = 1090 Vbase+ 33.2

  9. 2. Parameterization to predict cloud droplet number Nd= A Nc(S) / (Nc(S) + B )0.24 < Vbase< 0.5m s -1  S = 0.4 % A = 11700 Vbase - 1690 B = 10600 Vbase+ 1480

  10. 2. Parameterization to predict cloud droplet number Nd= A Nc(S) / (Nc(S) + B )0.5 < Vbase< 1.0m s -1  S = 0.5 % A = 4300 Vbase1.05 B = 2760 Vbase0.755

  11. 2. Parameterization to predict cloud droplet number Nd= A Nc(S) / (Nc(S) + B )1.0 < Vbase< 3.0m s -1  S = 1.0 % A = 7730 -15800 exp(-1.08Vbase) B = 6030 - 24100 exp(-1.87Vbase)

  12. 2. Parameterization to predict cloud droplet number Nd= A Nc(S) / (Nc(S) + B )3.0 < Vbase< 10.0m s -1  S = 2.0 % A = 1140 Vbase - 741 B = 909Vbase- 56.2

  13. Relatioship between Critical supersaturation and dry radius of CCN Nc ( Sc < S ) = Nc( rd > R ) S (%) 0.2 0.4 0.5 1.0 2.0 R (mm) Sea SaltNaCl 0.036 0.023 0.019 0.012 0.0077 Sulfate (NH4)2SO40.048 0.031 0.027 0.017 0.011 Organic carbon??? Black carbon ??? Dust ???

  14. Relatioship between Critical supersaturation and dry radius of CCN Nc ( Sc < S ) = Nc( rd > R ) S (%) 0.2 0.4 0.5 1.0 2.0 R (mm) Sea SaltNaCl 0.036 0.023 0.019 0.012 0.0077 Sulfate (NH4)2SO40.048 0.031 0.027 0.017 0.011 Organic carbon0.074 0.047 0.040 0.025 0.016 Black carbon Dust Ghan et al., 2001, J. Geophys. Res., 106, D6, 5295-5316 Table 1 density =1 hygroscopicity = 0.14

  15. Contents • Introduction • Parameterizations to predict cloud droplet number • Parameterizations to predict cloud droplet size distribution • Comparison between the parameterization and particle method • Results of numerical experiments • Conclusions

  16. 3. Parameterization of cloud droplet size distribution Gamma distribution. n( r ) = C rb exp(-Dr) dr C = Nd ( 4p(b+3)(b+2)(b+1)Nd / 3Q ) (b+1)/3/ b! D = ( 4p(b+3)(b+2)(b+1)Nd / 3Q )1/3 n( r ) : Number density ( cm-4 ) Nd: Numberof cloud droplets ( cm-3 ) Q : Cloud water ( g cm-3 ) Qadjust > Qcrit

  17. Contents • Introduction • Parameterizations to predict cloud droplet number • Parameterizations to predict cloud droplet size distribution • Comparison between the parameterization and particle method • Results of numerical experiments • Conclusions

  18. Two schemes for microphysics particle method ( in the parcel ) bin method ( on the grid ) FrameworkLagrangianEulerlian ri = r12(i-1)/3k (i = 1, 2,…,200) nj (j = 1, 2,…,200) Fixed valuesNumber concentration of CCN Representative radius of included in each class droplets included in each bin. Variable values rj (t) ni (t) Radius of droplets forming Number concentration of on CCN included in each class. droplets included in each bin. ActivationTakeda and Kuba (1982)not considered 2 - moment bin method ( Chen and Lamb, 1994 ) 2 - moment bin method ( Chen and Lamb, 1994 ) CondensationTakeda and Kuba (1982) Coalescencenot considered D t 0.05 s 0.5 s

  19. Parcel model is triggered Bin on the grid point Whenrelative humidity at the grid point reaches 100% for the first time Initial cloud droplets size distribution When relative humidity at the grid point is larger than 100% and cloud water on the windward side of the point does not exist Influx of droplets from the windward

  20. Two schemes for microphysics particle method ( in the parcel ) bin method ( on the grid ) FrameworkLagrangianEulerlian ri = r12(i-1)/3k (i = 1, 2,…,200) nj (j = 1, 2,…,200) Fixed valuesNumber concentration of CCN Representative radius of included in each class droplets included in each bin. Variable values rj (t) ni (t) Radius of droplets forming Number concentration of on CCN included in each class. droplets included in each bin. ActivationTakeda and Kuba (1982)not considered 2 - moment bin method ( Chen and Lamb, 1994 ) 2 - moment bin method ( Chen and Lamb, 1994 ) CondensationTakeda and Kuba (1982) Coalescencenot considered D t 0.05 s 0.5 s Parameterization

  21. WMO 5th Cloud Modeling Workshop2000, Aug, 7-11 Glenwood Springs, Colorado, U.S.A. Case1-Warm Rain Development Provided by Szumowski et al. (1998) Dynamical flame flow field : 2D shallow convection (time dependent flow function) domain : 9 km wide x 3 km deep Dx, Dz : 50 m Dt : 3 sec advection scheme: modified Smolarkiewicz (1984) CCN spectrumNCCN = fn(S) etc.

  22. Wind Field(25 min )

  23. Size distribution of CCN Chemical composition NaCl

  24. Contents • Introduction • Parameterizations to predict cloud droplet number • Parameterizations to predict cloud droplet size distribution • Comparison between the parameterization and particle method • Results of numerical experiments • Conclusions

  25. Initial size distribution of cloud droplets for bin method CCN-1.0 (4.5 km,1.78km) 5.5min. Without parameterizations (with parcel model) With parameterizations gamma distr. b = 2 gamma distr. b = 4 106 104 dN/dR ( cm-4) 102 100 Radius of droplet ( mm )

  26. Size distribution of cloud droplets CCN-1.0 (4.5 km,1.93km) 8.5min. Without parameterizations (with parcel model) With parameterizations gamma distr. b = 2 gamma distr. b = 4 106 104 dN/dR ( cm-4) 102 100 Radius of droplet ( mm )

  27. Number concentration of cloud droplets ( cm-3 ) 25 min. CCN-0.5 without parameterization (with parcel model) Altitude ( km ) with parameterization Horizontal distance ( km )

  28. Number concentration of cloud droplets ( cm-3 ) 25 min. CCN-1.0 without parameterization (with parcel model) Altitude ( km ) with parameterization Horizontal distance ( km )

  29. Number concentration of cloud droplets ( cm-3 ) 25 min. CCN-5 without parameterization (with parcel model) Altitude ( km ) with parameterization Horizontal distance ( km )

  30. CCN- 0.5 50 min. 5 without parameterization 1.22 Average in a domain (mm) with parameterization gamma distri.b = 2 b = 4 1.24 4 1.19 3 Accumulated Rainfall ( mm ) 2 1 0 0 1 2 3 4 5 6 7 8 9 Horizontal distance ( km )

  31. CCN- 1 50 min. 5 without parameterization 0.97 Average in a domain (mm) with parameterization gamma distri.b = 2 b = 4 0.99 4 0.92 3 Accumulated Rainfall ( mm ) 2 1 0 0 1 2 3 4 5 6 7 8 9 Horizontal distance ( km )

  32. CCN- 5 50 min. 5 without parameterization 0.16 Average in a domain (mm) with parameterization gamma distri.b = 2 b = 4 0.24 4 0.16 3 Accumulated Rainfall ( mm ) 2 1 0 0 1 2 3 4 5 6 7 8 9 Horizontal distance ( km )

  33. Contents • Introduction • Parameterizations to predict cloud droplet number • Parameterizations to predict cloud droplet size distribution • Comparison between the parameterization and particle method • Results of numerical experiments • Conclusions

  34. 6. Conclusions Parameterizations Cloud microphysical model Number Size distribution Bin method Rainfall Optical properties Cloud dynamical model Small error Useful to Non-hydrostatic 3D Model !

  35. We are installing these parameterizations and 2-moment bin method to CReSS Cloud Resolving Storm Simulator Tsuboki, K and A. Sakakibara, Large-scale parallel computing of Cloud Resolving Storm Simulator. High Performance Computing, Springer, H. P. Zima et al. Eds, 243--259. 2002 We plan to run it on Earth Simulator (Simulation for Case 1) and to compare the results of original CReSS with that of CReSS with bin model

  36. Issues Lack of data CCN spectrum Nc( S %) CCN counter Updraft velocity at the cloud base

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