COSMO Sibiu 2013 Matthias 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)

COSMO Sibiu 2013 Matthias Raschendorfer Some Challenges related to physical parameterizations:

The filtered model equations contain local and GS parameterizations: SGS flux density roughness layer modification of transport SGS source term including form drag GS source termGS flux density COSMO Sibiu 2013 Matthias Raschendorfer 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: COSMO Sibiu 2013 Matthias Raschendorfer 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 STIC interaction 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?

COSMO Sibiu 2013 Matthias Raschendorfer 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

COSMO Sibiu 2013 Matthias Raschendorfer Current WG3a-Activity towards solving the problems: 1-st Part: GS Parameterizations

Matthias Raschendorfer COSMO 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 DWD oProduction by convection: needs to be verified, but model output for EDR-forecast o 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 oimplemented 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

COSMO Sibiu 2013 Matthias Raschendorfer 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

Matthias Raschendorfer COSMO Work on microphysics:  Implementation of 2-moment scheme (A.Seifert)  Runtime % 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 snow o 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

Matthias Raschendorfer COSMO 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- dissipation rate (EDR) labil: neutral: stabil: time tendency of TKE transport (advection + diffusion) shear production by sub grid scale circulations shear production by the mean flow Matthias RaschendorferCOSMO Lugarno 2012 DWD Additional Shear -Production of TKE by:  SSO wakes  Horizontal shear eddies  Vertical 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 RaschendorferCOSMO Lugarno 2012 DWD pot. temperature [K] Wind speed [m/s] reference including 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 Raschendorfer DWD  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 m 2 /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 m 2 /s CUS 2013 Computationally extremely cheap; large impact in particular for T_2m_Min (SK= for a 2-month exp.) !!

UTC

Theta/[°C] Vel/[m/s] cloud-water-content/[Kg/Kg]: time-height cut RoutineExperiment 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 Minimum limitation of the diffusion coefficients (by TKM min and TKH min ) enabled in stable cases Artificial mixing background TKE forcings sum increased so that Ri never exceeds Ri c 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 TKM min and TKH min from 1 to m 2 /s Diagnostics of PBL parameterization in stable conditions Diff. Coeff fall below 1 m 2 /s in some periods in stable conditions Increased amplitude of the oscillations in turbulence- related variables CAUSE? Effect on 2m Temperature Ines Cerenzia: ISAC-CNR, Arpa-SIMC

Removal of the oscillations by setting pat_len=0 Diagnostics of PBL parameterization in stable conditions Ines Cerenzia: ISAC-CNR, Arpa-SIMC 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 Machulskaya 1, Ann Kristin Naumann 2, Axel Seifert 1,2, and Juan Pedro Mellado 2 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 S s 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 S s 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 ( 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

COSMO Sibiu 2013 Matthias Raschendorfer Challenges related to parameterization extensions:

COSMO Sibiu 2013 Matthias Raschendorfer Non physical parameterization complements: Trying to improve physical parameterizations using model error estimates: 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 global constants initial values global parameter local (external) parameter Model output prognostic variables implicit diagnostics Explicite diagnostics Model integration diagnostic model calculation Observations SuperobservationSupercalculation averaging compare Additional parameters for explicit diagnostics assimilation Principal of the parameterization complements: Trying to improve physical parameterizations by systematic parameter tuning: COSMO Sibiu 2013 Matthias Raschendorfer parameter tuning  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 Hyper- parameter ization decreasing stochastic complementincreasing 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 Physics Motivations 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