Evaluation study of a 2D Microphysics Model Thomas NOEL (1), Nicolas VILTARD (1) , Georges SCIALOM (1)

(1) Centre d’Etude des Environnements terrestre et planétaires, France Thomas NOEL - CETP - 2007

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OUTLINE 

Introduction



Microphysics Model



Wind Field (Data)



Microphysics Fields ( Results )



Conclusion

Thomas NOEL - CETP - 2007

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Aim of the study : improve ice parameterization in RTM fixed ρ

Variable ρ(D)

Radiative transfer modeling (RTM) of brightness temperature using two different parameterization of ice Thomas NOEL - CETP - 2007

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The Microphysics Model 

The microphysics model was first designed by Virginie Marécal and Danièle Hauser (1992).



The model is 2D stationary and diagnostic.



We use a stationary wind field and we compute the microphysics fields in balance with the wind field.



We have two ice species: - Cloud ice monodisperse non-falling. - Precipitation ice : V(D), ρ(D)



The model was extensively used on FRONTS 87 to diagnostic microphysics in frontal rainbands

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Unsuccessful attempts were made to use it TOGA CORE DATA for tropical convection.

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At that point, we succeeded to simulate tropical convection on synthetic wind field.

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Microphysics model (Marécal 92)

Melting

Collection of rain by cloud ice

Cloud Ice Accretion

Riming

Autoconversion

Accretion

Autoconversion

Cloud Wator

Melting Accretion

Rain

Accretion by Melting Thomas NOEL - CETP - 2007

Snow / Graupel 5

Evaporation of melting graupel

Evaporation/Condensation

Depositional growth

Initiation

Wator Vapor

Sublimation of graupel Depositional growth Sublimation

Evaporation

Synthesis of wind field Continuity equation :

∂u ∂w w  − =0 ∂ x ∂z H

w  x,z  =Ce−y² sin S u  x,z  = Altitude ( km)

y=





 Π . Δx . C . erf x− x0 [ sin S −α cos S ] 2

H

Δx

x−x 0 Δx

S=α .

z−z 0 H

α=.

Π− H A

z Π B=sin −  2 2

H=8 .52 A= 16 C=5 y0=−1

X ( km) Thomas NOEL - CETP - 2007

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Rain and Snow -

Parameterization : m(D) = 0.02 D1.9 et Vt (D) = 5.1 D 0.27 .f(z)

Rain

Snow

Altitude ( km)

Altitude ( km)

Mixing Ratio

Mixing Ratio

(g/kg)

(g/kg)

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Process for snow parameterization Depositional growth (-) or sublimation of cloud ice (+)

Accretion of cloud ice by snow

Altitude ( km)

Altitude ( km)

g/kg/s

Thomas NOEL - CETP - 2007

g/kg/s

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Process for snow parameterization Depositional growth (-) or sublimation of snow (+)

Accretion of cloud water by rain

Altitude ( km)

Altitude ( km)

g/kg/s

Thomas NOEL - CETP - 2007

g/kg/s

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Cloud Ice and Cloud Water, Rain and Graupel Parameterization : m(D) = 19.6 D2.7 et Vt (D) = 124 D 0.37 .f(z)

Altitude ( km)

Altitude ( km)

Mixing Ratio (g/kg)

Mixing Ratio (g/kg)

Graupels Cloud Ice

Altitude ( km)

Altitude ( km)

Mixing Ratio (g/kg)

Mixing Ratio (g/kg)

Cloud Water Rain

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V - Conclusion    

   

High sensitivity to wind field quality and RH profile Sensitivity to spatial resolution Model is now stable under tropical conditions with synthetic wind field Results are coherent with expectation although some processes are too intense Next step is to use 2D Ronsard wind field from the 28 July 2006 Addition of a 3rd ice species for more realistic convective/stratiform structure Comparison with polarimetric classification (presentation by R. Evaristo) A systematic survey of the observed cases will permit a classification of the systems to be able to establish statistical relations between the microphysics and dynamics.

Thomas NOEL - CETP - 2007

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Thank you for your attention.

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Wind Field

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Computation scheme of the model

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The numerical resolution method : SOR For each calculating microphysics variables , we have an conservation equation of this type:



u 

∂q ∂ ²q ∂²q 1 ∂  ρV p q +w ∂ q/∂ z−K −K − =S ∂x ∂ x² ∂ z² ρ ∂ z

This equation can be linearized and rephrase of the following way: −a1 X i,j +a 2 X i −1, j +a 3 X i,j −1 +a 4 X i+ 1, j +a 5 X i,j+1 =S i,j



To the balance, one must have: −a1 X i,j +a 2 X i −1, j +a 3 X i,j −1 +a 4 X i+ 1, j +a 5 X i,j+1−S i,j =0



To reach the balance we correct the content microphysics of a R quantity:

−a1  X i,j +R  +a 2 X i− 1, j +a 3 X i,j−1 +a 4 X i+1, j +a 5 X i,j+ 1 =S i,j ==>

a 1 R=a 2 X i −1, j +a 3 X i,j −1 +a 4 X i+1, j +a5 X i,j+1−a 1 X i,j−S i,j



To every iteration we do an adjustment therefore:

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To the balance we must have R=0. In each iteration, R must decrease and stretch toward 0

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We speak of convergence of the model when R stretches toward 0

X ik−1, j =X ki,j−1 +ωR ki,j−1

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Accretion of rain by melting snow

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Evaporation of rain

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Accretion of cloud water by rain

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Depositional growth (-) or sublimation of snow (+)

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Accretion of cloud ice by snow

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Depostional growth (-) or sublimation of cloud ice (+)

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