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Parametric representation of the hydrometeor spectra for LES warm bulk microphysical schemes.

Olivier Geoffroy, Pier Siebesma (KNMI), Jean-Louis Brenguier, Frederic Burnet (Météo-France). Parametric representation of the hydrometeor spectra for LES warm bulk microphysical schemes. Problematic, methodology and measurements Cloud spectrum: results Rain spectrum: results

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Parametric representation of the hydrometeor spectra for LES warm bulk microphysical schemes.

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  1. Olivier Geoffroy, Pier Siebesma (KNMI), Jean-Louis Brenguier, Frederic Burnet (Météo-France) Parametric representation of the hydrometeor spectra for LES warm bulk microphysical schemes. • Problematic, methodology and measurements • Cloud spectrum: results • Rain spectrum: results • Sensitivity tests in shallow cumulus simulations. • Z-R relationship

  2. Problematic Nc (M0) & qc (~M3), Nr (M0) & qr (~M3) Bulk prognostics variables = Interaction with radiative transfert: Microphysical processes / variables Radar reflectivity: Cloud Sedim: Cond/evap: autoconversion: ~SM1 =M6 τ~M2 Radar reflectivity: Rainevap: RainSedim =M6 ~M1 & M2 To derive other moments from M0 & M3, M0 & M3 it is necessary to make an assumption about the shape of the CDSD and the RDSD

  3. Common distributions Generalized Gamma Lognormal α=3 Mass distri = Gamma α=1 Size distri = Gamma ν =1 ν =6 ν=11 = Marshall Palmer 4 parameters M0, M3 = prognostics α =1 or 3 3 parameters M0, M3 = prognostics ν =? σg=? Are Lognormal, Gamma, Gamma in mass suitable ? With which value of the width parameter σgor ν?

  4. Observationnal data Data = particule counters in situ Measurements at 1Hz resolution (~ 100 m). campaign ACE-2 : 8 cases of Sc RICO : 7 cases of Cu flight plan Instruments Fast FSSP : ~2 ~50 µm OAP-260-X : 5635 µm 2DP-200X: 245 12645 µm Fast FSSP : ~2 ~40 µm OAP-200-X : 35 310 µm • Sc and Cu spectra • Measurements at each levels in the BL • ~100 m resolution • Complete hydrometeors spectra : 1 µm to 10 mm

  5. Methodology For each spectrum: Cloud: ACE-2 : 19000 spectra RICO : 8500 spectra Cloud Rain D0 D0 = 75 µm

  6. Methodology For each spectrum: Cloud: ACE-2 : 19000 spectra RICO : 8500 spectra Cloud Rain D0 M1 D0 = 75 µm qc, Nc Lognormal σ g

  7. Methodology For each spectrum: Cloud: ACE-2 : 19000 spectra RICO : 8500 spectra Cloud Rain D0 M6 M1 M2 M5 D0 = 75 µm qc, Nc Lognormal σ g σ g σ g σ g

  8. Methodology For each spectrum: Cloud: ACE-2 : 19000 spectra RICO : 8500 spectra Cloud Rain D0 M6 M1 M2 M5 D0 = 75 µm qc, Nc Lognormal σ g σ g σ g σ g Gamma ν1 ν1 ν1 ν1

  9. Methodology For each spectrum: Cloud: ACE-2 : 19000 spectra RICO : 8500 spectra Cloud Rain D0 M6 M1 M2 M5 D0 = 75 µm qc, Nc Lognormal σ g σ g σ g σ g Gamma ν1 ν1 ν1 ν1 ν3 ν3 ν3 ν3 Gamma in mass

  10. Methodology For each spectrum: Cloud: ACE-2 : 19000 spectra RICO : 8500 spectra Rain: ACE-2 : not used RICO : 2860 spectra Cloud Rain D0 M6 M6 M1 M2 M5 M1 M2 M4 D0 = 75 µm qc, Nc qr, Nr Lognormal σ g σ g σ g σ g σ g σ g σ g σ g Gamma ν1 ν1 ν1 ν1 ν1 ν1 ν1 ν1 ν3 ν3 ν3 ν3 ν3 ν3 ν3 ν3 Gamma in mass

  11. Plan • Methodology and measurements • Cloud spectrum: results • Rain spectrum: results • Sensitivity tests in shallow cumulus simulations.

  12. Cloud, width parameter=f(M1) Grey points = value of σg that best represent M1 for each spectrum Circles = value that minimize the standard deviation of the absolute errors Mmeasure-Manalytic in each moment class Triangles = value that minimize the standard deviation of the relative errors Mmeasure/Manalytic in each moment class

  13. Cloud, width parameter=f(Mp) Value of the width parameter: Lognormal: Gamma: Gamma in mass: Circles = value that minimize the standard deviation of the absolute errors Mmeasure-Manalytic in each moment class Triangles = value that minimize the standard deviation of the relative errors Mmeasure/Manalyticin each moment class

  14. Cloud, width parameter=f(qc) Parameterization formulation : Lognormal: Gamma: Gamma in mass: Circles = value that minimize the standard deviation of the absolute errors Mmeasure-Manalytic in each LWC class Triangles = value that minimize the standard deviation of the relative errors Mmeasure/Manalyticin each LWC class

  15. Cloud, relative error=f(Mp) Value of the width parameter: Lognormal: Gamma: Gamma in mass:

  16. Cloud, relative error = f(qc) Parameterizations: Lognormal: Gamma: Gamma in mass:

  17. Cloud, relative error=f(Mp) Value of the width parameter: Lognormal: Gamma: Gamma in mass:

  18. Cloud, relative error = f(qc) Parameterizations: Lognormal: Gamma: Gamma in mass:

  19. Plan • Methodology and measurements • Cloud spectrum: results • Rain spectrum: results • Sensitivity tests in shallow cumulus simulations.

  20. Rain: Gamma, ν=f(Dv) ν=f(Dv) Seifert (2008) ν=f(Dv) 16 13 10 Stevens and Seifert (2008) 7 4 ν=f(Dv) Marshall and Palmer (1948) 1 Marshall and Palmer (1948) Measurements vs Seifert (2008) results: - Some distributions larger than Marshall Palmer at low Dv - Less narrow distributions at high Dv Differences: - Measurements at every levels in cloud region - Seifert (2008): distribution at the surface, no condensation

  21. Rain: free parameter=f(qr) Parameterizations : Lognormal: Dependance in function of qr  Better results Gamma: Gamma in mass: Circles = value that minimize the standard deviation of the absolute errors Mmeasure-Manalytic in each RWC class Triangles = value that minimize the standard deviation of the relative errors Mmeasure/Manalytic in each RWC class

  22. Rain: relative errors Marshall Palmer Parameterizations: Lognormal: Dependance in function of qr  Better results Gamma: Gamma in mass:

  23. Plan • Problematic, methodology and measurements • Cloud spectrum: results • Rain spectrum: results • Sensitivity tests in shallow cumulus simulations. • Z-R relationship

  24. Sensivity test: RICO case Models of the intercomparison exercise (black) DALES simulations LWP (g m-2) Ensemble of models ν3c=1, νr=1 RWP (g m-2) ν3c=f(lwc), νr=f(lwc) Rsurface (W m-2)

  25. Deeper BL based on RICO -0.6 K Colder θl -0.6 K Moister qt + 2.5 g kg-1 Averaged profiles restart + 0.5 g kg-1

  26. Sensitivity to ν3c Autoconversion rate : (Seifert and Beheng, 2006) =A υc=1  A=8 υc=2  A=3.75 υc=3 A= 2.7 3 10-8

  27. Sensitivity to νr Processes depending on νr : rain sedim, evap, self-collection and break-up width CT CB

  28. Plan • Problematic, methodology and measurements • Cloud spectrum: results • Rain spectrum: results • Sensitivity tests in shallow cumulus simulations. • Z-R relationship

  29. Z-R Z=68 R2 Snodgrass (2009)

  30. Summary • Development of a parameterization of the width parameter of the cloud droplet spectra as a function of the LWC. • Development of a parameterization of the width parameter of the rain drop spectra as a function of the RWC Lognormal: Gamma: Gamma in mass: Lognormal: Gamma:

  31. Z-R Snodgrass: red TRMM: green Only 2dp

  32. Z-R

  33. Sensitivity to νr Processes depending on νr : rain sedim, evap, self-collection and break-up width Fluxprecip CT CB Without rain evaporation • - Sensivity to νr in sedim process  similar results as Stevens and Seifert (2008) • Main sensitivity : sedimentation process. • νr in sedim  RWP • νr in sedim Vqr  evap  LWP  RWP • νr in evap  evap  LWP

  34. Observational data Scatterplot all qr-Nr values Scatterplot all qc-Nc values ACE-2 : 19000 spectra RICO : 8500 spectra ACE-2 : not used RICO : 2860 spectra (RF07, RF08, RF11, RF13)  Large number of spectra typical of Sc and Cu

  35. Measured spectra RICO : 7 cases of Cu ACE-2 : 8 cases of Sc Fast FSSP : ~2 ~50 µm, 266 bins OAP-260-X : 5635 µm, 63 bins, Δbin~ 10 µm 2DP-200X: 45 12645 µm, 63 bins, Δbin~ 200 µm Fast FSSP : ~2~40 µm, 266 bins OAP-200-X : 15 310 µm, 15 bins, Δbin~ 20 µm - Complete hydrometeors spectra : 1 µm to 10 mm

  36. Cloud, absolute error=f(Mp) Parameterization formulation : Normalization: M1: 100 µm cm-3 M2 :1000 µm2 cm-3 M5:107 µm5 cm-3 M6 :109 µm6 cm-3 σ: 1 µm

  37. Cloud, absolute error =f(qc) Parameterization formulation : Normalization: M1: 100 µm cm-3 M2 :1000 µm2 cm-3 M5:107 µm5 cm-3 M6 :109 µm6 cm-3 σ: 1 µm

  38. ACE 2 - RICO

  39. Only ACE 2

  40. Only ACE 2

  41. Only RICO

  42. Only RICO

  43. Rain sedimentation Terminal velocities parameterization (Stevens and Seifert, 2008) : Vqr > VNr V=f(Dv), νr=1 V=f(Dv), νr=6 V=f(Dv), νr=11 Vqr VNr Vqr-VNr Vqr VNr Vqr-VNr Vqr VNr Vqr-VNr broader : νr Vqr ,VNr distribution Vqr-VNr  Size sorting

  44. Rain sedimentation (averaged profiles) sc / b-up : low impact Evap : low impact µ evap but larger droplets  Rsurf  LWP  RWP (peaks)  RWP , Rsurf ν width  Vqr  Rsurf  dRWP /dt  RWP Sedim (large drops) ν width  RWP  evap  LWP (positive feedback)

  45. Rain evaporation Rain mixing ratio rr Rain concentration Nr Cevap = 1  Dv = constant during evaporation (happens if preence of little drops) Cevap = 0  Nr = constant during evaporation (happens if only large drops) Cevap = 0.7 – 1 (A. Seifert personal com)

  46. Cevapsensitivity Cevap=1 Cevap=0.7 Cevap=0 ~2 mm j-1 Cevap = 1  Dv = constant, Nr Cevap = 0  Nr = constant, Dv  evap  LWP and RWP Cevap = 0.7 – 1 (A. Seifert personal com)

  47. Autoconversion, sensitivity Autoconversion rate : = 8 (υc=1) = 3.75 (υc=2) = 2.7 (υc=3) (Cloud droplet width) Collection efficiency kcc=4.44 E9 m3 kg-2 s-1 10.44 E9 m3 kg-2 s-1 ~2 mm j-1 Sensitivity to the coefficients υc(cloud droplet spectra width)

  48. The rain drop distribution Gamma law : with :  1 free parameter : νr 1-D bin model spectra : Gamma law (rr = 0.2 g kg-1, Nr = 10000 m-3) ν=f(Dv) ν 16 νr= 1 νr=6 νr=11 13 = Marshall Palmer 10 7 4 1 Seifert (2008) ν Narrower distribution Dv  νr

  49. νr sensivity νr=1 νr=f(Dv) νr=6 νr=11 Impact due to sedimention ~2 mm j-1 ν RWP evap LWP ν Width Size sorting Vqr Rsurf dRWP /dt RWP (acrr ~ cste)

  50. Precipitating flights : RF07, RF08, RF12 (low vlues and low number of points , 0.10 g m-3), RF13, RF11

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