COSMO Sibiu 2013 Matthias Raschendorfer Some Challenges related to physical parameterizations Current WG3a-Activity towards solving the problems 1-st Part:

COSMO Sibiu 2013Matthias Raschendorfer

Some Challenges related to physical parameterizations

Current WG3a-Activity towards solving the problems

1-st Part: Turbulence and Convection

2-nd Part: Microphysics and Radiation (U. Blahak)


Some Challenges related to physical parameterizations:

The filtered model equations contain local and GS parameterizations:

p,Qp,ˆQkˆkˆˆˆ iiiiiiiiiit

vvv

SGS flux density roughness layer modification of transport

SGS source term including form drag

GS source termGS flux density


molecular flux density

Momentum:

(Enthalpy) ~Temperature:

water phases:

pressure gradient + gravity + Coriolis force

pressure work + cloud microphysics + radiation

cloud microphysics

functions in various covariance terms: of scalar variables

( + dissipation )

to be closed by restricting assumptions

Whole SGS variability needs to separated into classes to which specific closure assumptions can be applied

Turbulence: isotropic, normal distributed, only one characteristic length scale at each grid point, forced by shear and buoyancy closure by truncated 2-nd order equations

Circulation: non isotropic, arbitrarily skewed and coherent structures of several length scales, supplied by various pressure forces: closure adapted to process (e.g. by mass flux equations)Convection; kata- and anabatic flows; wakes; horizontal shear eddies; braking gravity waves

with turbulent and circulation contribution

Principal Problems:


Separation Turbulence Circulations

Parameterizations of source terms

Parameterizations of SGS processes

integrated in

Cloud-microphysics

Radiation transport

• Moist turbulence using statistical saturation adjustment, but

Turbulent contributions to phase change terms not yet considered in GS budgets

Non equilibrium processes (icing and precipitation) not included

• Convection scheme treats micro-physics including precipitation, but

Not complementary with GS microphysics

Radiation is not included

STICinteraction

Local parameterizations:

GS parameterizations:

• Radiation parameterization considers cloud properties, but Precipitating hydrometeors (snow) are not yet included SGS variability of cloud properties not properly considered

Missing interaction to be included :

• TKE scheme contains interaction terms, but

Some interaction terms are crude estimates and related circulations don’t even have their own contribution to transport (mixing) of 1-st order variables as well as TKE

• Mass flux convection scheme (seems not to be dispensible): Does not yet contain any dependency from turbulence Convective mixing of TKE not yet considered Is not separated against resolved convection (grey-zone, double counting) Is not able to give estimates of volume fractions of convective subdomains

Overlap of turbulent and convective contributions to microphysical source terms can’t be treated properly (no consistent description of cloud processes)

UTCS?


Some specific challenges:

• Some simplifying approximations are no longer valid due to increased or variable horizontal resolution:

3D-extensions: tilted columns; horizontal diffusion; transport of 2-nd order moments (TKE)Grey-zone; scale adaptive convection

Neglect of horizontal gradients compared to vertical ones, allowing single column solutions

Neglect of up- and downdraft fraction and mean vertical wind speed in convection parameterization (completely unresolved convection)

Roughness layer due to land use is only a small part of the lowest model layer, allowing to treat it in the SAT scheme only

vertically resolved roughness layer: additional form drag; smaller roughness length, modification of turbulent length scale

• More consistent and complete parameterizations: Avoiding numerical artefacts and instabilities Avoiding contradictory, artificial or unnecessary approximations Removing problems with diurnal cycle, stable boundary layer, low

level stratus; SAT Consolidation /merging of independent development

• Application of parameterizations in COSMO and ICON: common physics library: generation of clear interfaces; multi parameterization ensemble; modularization; cleaning up of NAMELISTs; adaptations for surface tiles; outstanding documentations

autom. parameter estim.; PP CALMO; statistical hyper-parameterization or post-processing

ongoing improvement; finishing PP UTCS; PT ConSAT and followers

• Improvement by non-physical extensions:

using advantage of different approaches

using direct impact of error estimates

Including non-deterministic aspects stochastic physics

Expensive calculations can be called less frequently (smooth evolution in time) or can even be avoided (single column solutions) Adaptive parameterizations

short term

longer term

interdisciplinary ; longer term


Current WG3a-Activity towards solving the problems:

1-st Part: GS Parameterizations

Matthias RaschendorferCOSMO

Work on turbulence and SAT:

Allowing for TKE-advection (U.Blahak), o Implemented in 4.18; technically working; can be implemented in current version shortly

Adding scale interaction terms (M.Raschendorfer), Production due to SSO-wakes, horizontal shear eddies and convection

o SSO-term: operational at DWDo Production by convection: needs to be verified, but model output for EDR-forecasto Horizontal shear term: tuning parameter only estimated, but still used for EDR-forecast

Reformulation of TKE scheme (including SAT) (M.Raschendorfer), Changing positive definite solution of prognostic TKE-equation Weakening numerical security limits and modularization: common SUBS for turbulence and SAT Diffusion of conserved variables Same implicit diffusion solver for 1-st order variables and TKE with options for better coupling

o implemented in private test-version; and ICON not yet verified; common version in work

3D-Smagorinsky scheme implemented (W.Langhans, ETHZ) o Implemented in private test version (already documented)o Horizontal shear production + horizontal diffusion can be activated as well using current TKE

scheme

Diagnostics of TKE-scheme in stable conditions (Ines Cerenzia) Analytical and experimental study

o Report just availabe

Sibiu 2013


Thermodynamic corrections:

Carrying adiabatic source terms in prognostic pressure equations (U.Blahak) o Is implemented and being tested

Former isobaric grid scale saturation adjustment changed to an isochoric process (U.Blahak) Adjustment generates now a pressure correction, is mass conserving and fits to ICON

o Implemented and tested: only small impact

UTCS/TKESV: (D. Mironov, E. Maschuskaya) TKESV extension; statistical cloud scheme now based on double Gaussian distribution

o Implemented in test version

Turb-i-Sim: (J. Schmidli, O. Fuhrer, …) Evaluation and improvement of COSMO turbulence over Alpine topography

o Project at ETH and MeteoSwiss, just started

Deardorff-restriction of turbulent master length scale (M.Raschendorfer)o Implemented since more than a year in current version, needs to be verified

Mixed water-ice phase for turbulence and statistical saturation adjustment (M.Raschendorfer)o Implemented in old test-version only; tested by E.Avgoustoglou


Work on microphysics:

Implementation of 2-moment scheme (A.Seifert) Runtime 60-100% increased! Only as an reference or for special purpose (COSMO-ART) Further work on hail-microphysics and optimization

o Adopted as an extra code to 4.25 and tested: slightly better over all verification

Prognostic treatment of melted water fraction within solid water parcels (A.Seifert)o Ready for testing in case of snowo Further work for graupel and hail planned only as an extension of the 2-moment scheme

Almost ready improvement of the 1-moment scheme (F.Rieper) Changing exponential distribution function to a more general gamma-function Implementation of an improved sedimentation formulation for snow and rain Some bug fixes

o All to gather implemented in current version and being tested

Running improvement of 1-moment scheme Consideration of homogeneous ice nucleation in cirrus clouds allowing higher oversaturation (C.Köhler) Improved simulation of super-cooled water to improve forecast of aircraft icing (F.Rieper)

Sibiu 2013


Work on radiation:

Using an improved aerosol climatology (J.Helmert)o Test runs performed: currently too transparent clouds

Slightly modifying cloud cover diagnostics for ice clouds in radiation scheme (A.Seifert)o Already in current code

Considering precipitating hydrometeors in radiation calculation (U.Blahak) In particular slowly falling snow should be considered

o Work just started

Adaptive sampling of grid points used for radiation calculation (V.Venema, Uni Bonn) Running radiation only once for all grid points with similar properties related to radiation

o Promising, only research version prepared

Monte Carlo spectral integration (MPI Hamburg; B. Ritter) Varying stochastically the absorption coefficients of a reduced number of spectral bands

o Promising, only research version prepared

Sibiu 2013

Separated TKE equation (including scale interaction sources):

buoyancy production

eddy-dissipationrate (EDR)

0labil:neutral:stabil: 0

00

time tendency of TKE

transport(advection + diffusion)

shear production by sub grid scale circulations

0

v

shear production by the mean flow

0

v

Matthias Raschendorfer COSMO Lugarno 2012DWD

Additional Shear -Production of TKE by:

SSO wakesHorizontal shear eddiesVertical convective currents

Formal scale separation automatically produces interaction between GS parameterizations

of turbulence and circulations

More physically based mixing even for stable stratification

• Missing link; • Computationally extremely cheap; • clear impact

Matthias Raschendorfer COSMO Lugarno 2012DWD

pot. temperature [K]Wind speed [m/s]

referenceincluding horizontal shear – and SSO-production

including horizontal shear –, SSO- and convective production

mountain ridge

COSMO-US: cross section across frontal line and Appalachian mountains

Consequences of scale interaction terms and general model improvment:

Matthias RaschendorferDWD

More physical based TKE and mixing in the stable BL

- Is already beneficial for CAT-forecast needed for aviation (s. previous reports)

- Should be beneficial also for near surface SBL.

- Previous artificial security measures needs to be adopted!

First candidate: the minimal diffusion coefficient

- Previous value: tkv[h,m]min = 1.0 m2/s (same for scalars and momentum)

- Seems to dissolve BL clouds much to early now (and was presumably always a bit too large)

- Previous attempts to decrease it has not been successful

- After lots of general numerical improvement of the model and the introduction of at least the SSO-source term,

a further attempt has now been tried

- New value: tkv[h,m]min = 0.4 m2/s

CUS 2013

Computationally extremely cheap; large impact in particular for T_2m_Min (SK=-13.33 for a 2-month exp.) !!

15.11.2012 12 UTC

Theta/[°C]

Vel/[m/s]

cloud-water-content/[Kg/Kg]:

time-height cutRoutine Experiment

all values are area averages

Diagnostics of PBL parameterization in stable conditions

Considerations about the stable PBL parameterizations in COSMO (operational setting in Arpa-SIMC) evidenced through a case study in the Po Valley

TKE forcings sum increased so that Ri never exceeds Ric

Very stable conditions not well described and led to less stable cases

Increase of turbulence in stable

conditions

Ines Cerenzia: ISAC-CNR, Arpa-SIMC

First test: reduction of TKMmin and TKHmin from 11 to 1010-2-2 m2/s


Diff. Coeff fall below 1 m2/s in some periods in stable conditions

Increased amplitude of the oscillations in turbulence-related variables

CAUSE?

Effect on 2m Temperature


Removal of the oscillations by setting pat_len=0



Neglect the triple term in TKE eq. due to pressure-velocity correlation

Effect on the whole PBL to be further investigated

Modelling Scalar Skewness: an Attempt to Improve the Representation of Clouds and Mixing Using a Double-Gaussian Based Statistical Cloud Scheme

Dmitrii Mironov 1, Ekaterina Machulskaya1, Ann Kristin Naumann2,

Axel Seifert 1,2, and Juan Pedro Mellado2

1) German Weather Service, Offenbach am Main, Germany

2) Max Plank Institute for Meteorology, Hamburg, Germany

Naumann et al. (2013) developed a statistical cloud scheme based on a 3-moment double-Gaussian PDF of linearized saturation deficit (s); the scheme requires mean, variance, and skewness of s as input

Transport equation for the skewness Ss of s is developed, and closure assumption for the third-order and fourth-order s-velocity correlations are formulated that account for high-skewness cloud regimes (e.g. cumuli)

The Ss equation is coupled to the TKE-Scalar Variance mixing scheme (see Machulskaya and Mironov 2013, COSMO Newsletter No. 13) and to the 3-moment double-Gaussian cloud scheme

The new scheme is tested against LES data (Heinze 2013) through single-column simulation of shallow cumuli (BOMEX test case); first results look promising

A statistical cloud scheme (statistical saturation adjustment) based on a pure Gaussian PDF is part of the current (separated) TKE scheme

In terms of scale separation, cloud processes due to non-Gaussian processes are due to circulations treated in different schemes (e.g. mass flux scheme for “shallow convection”).

This approach tries to treat these processes within a turbulence framework:

TKESV + New Cloud Scheme: Cloud Fraction and Cloud Water

BOMEX shallow cumulus test case (http://www.knmi.nl/~siebesma/BLCWG/#case5) .Profiles are computed by means of averaging over last 3 hours of integration (hours 4 through 6). LES data are from Heinze (2013).

TKESV + New Cloud Scheme: TKE and Buoyancy Flux

Comprehensive testing of the new scheme (stratus and stratocumulus regimes, etc.)

Consideration of numerical issues

Implementation into COSMO an ICON

Further development of the scheme, e.g. consideration of the effect of microphysical processes on the scalar variance and skewness (in co-operation with the HErZ-CC team)

Outlook


Challenges related to parameterization extensions:


Non physical parameterization complements:

• Trying to improve physical parameterizations using model error estimates:

“Verification with feed back” should help to ask the following questions:

1. How good is a certain model?

Meaningful error measure not unique but dependent on most important output for customers

Important information by comparison with other models

2. What is wrong with the model?

Errors need to be related to specific inaccurately simulated processes

Conditional verification by VERSUS or other specific diagnostic tools helps to limit the possible sources of errors

Arbitrary conditions in terms of model output, measurements and external parameters

Most selective conditions need to be found in cooperation with modelers

Detection of most crucial parts in physical parameterizations and testing alternative formulations

3. How can we improve the model systematically?

Statistical post-processing

o Offline application of regression functions generating user optimized products (T_2m, EDR, …)

Automatic parameter optimization (related to PP CALMO)

o Successive parameter estimation for specific processes separated by component testing

o Statistical hyper-parameterization (needed, if parameters are dependent on internal model state)

o Online expression of each physical parameter by a regression function in some model variables

Stochastic extensions (needed, if the model output still doesn’t describe the reality satisfactory)

o Non-deterministic contributions to initial conditions, boundary values, numerics or parameterizations

classical verification

model diagnostics

data assimilation

ensemble prediction; probabilistic forecast; error estimate

o introduction of additional statistical moments by simulation of stochastic processes Stochastic physics ?

Model input

boundary values t,M

0t,M

global constants

initial values

global parameter p

local (external) parameter Me

Model output

prognostic variables t,M

t,Mimplicit diagnostics

Explicite diagnostics t,M

Model integration

cdiagnosticmodel calculation

Observations T,L,,

Superobservation T,L,, Supercalculation T,L,,

averagingaveraging

compare

Additional parameters for explicit diagnostics

Md

assimilation

Principal of the parameterization complements:

Trying to improve physical parameterizations by systematic parameter tuning:


parametertuning

Providing a list of parameter sub sets containing as few as possible parameters, related to specific conditions and a verification quantity that can be compared with measurements and that is sensitive only to those parameters in the sub set in case of the applicability of that condition.

by minimizing the model error of a verification quantity

conditional sampling

regression coefficients k

Hyper- parameterization

decreasing stochastic complementincreasing

T,L,,

Stochastic variations of parameterizations:

might even decrease current stochastic complement

Stochastic variations of model input:

should decrease expectation of stochastic complement

• Stochastic variation of tendencies

• stochastic properties of SGS surface tiles or convective cells

Stochastic PhysicsMotivations

•to improve the model stochastically if it is not possible to do it deterministically•to estimate the background (model) error for the data assimilations purposes•to provide the users with an estimation of the forecast reliability and uncertainty

Possible steps

•to determine the entire model error and to approximate it with a random process with the same time and space correlations•to go further into the determination of different types of the model error•to develop a more consistent approach: noise structure should not be arbitrary, but should be determined by the governing equations

COSMO Sibiu 2013 Matthias Raschendorfer Some Challenges related to physical parameterizations Current WG3a-Activity towards solving the problems 1-st Part:

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