COSMO

1. 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) Sibiu 2013 COSMO Matthias Raschendorfer

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

3. The filtered model equations contain local and GS parameterizations: Momentum: pressure gradient + gravity + Coriolis force water phases: cloud microphysics pressure work + cloud microphysics + radiation (Enthalpy) ~Temperature: ( + dissipation ) GS flux density molecular flux density GS source term roughness layer modification of transport SGS flux density SGS source term includingform drag functions in various covariance terms: of scalar variables • to be closed by restricting assumptions • with turbulent and circulation contribution 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 Sibiu 2013 COSMO Matthias Raschendorfer

4. Principal Problems: • Radiation parameterization considers cloud properties, but • Precipitating hydrometeors (snow) are not yet included • SGS variability of cloud properties not properly considered • 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 Radiation transport Cloud-microphysics Parameterizations of source terms Local parameterizations: integrated in • Convection scheme treats micro-physics including precipitation, but • Not complementary with GS microphysics • Radiation is not included Parameterizations of SGS processes GS parameterizations: STIC interaction Separation Turbulence Circulations UTCS? 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) Sibiu 2013 COSMO Matthias Raschendorfer

5. Some specific challenges: • Some simplifying approximations are no longer valid due to increased or variable horizontal resolution: longer term 3D-extensions: tilted columns; horizontal diffusion; transport of 2-nd order moments (TKE) • 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) Grey-zone; scale adaptive convection vertically resolved roughness layer: additional form drag; smaller roughness length, modification of turbulent length scale • 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 • Expensive calculations can be called less frequently (smooth evolution in time) or can even be avoided (single column solutions) Adaptive parameterizations • 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 • using advantage of different approaches short term • 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 ongoing improvement; finishing PP UTCS; PT ConSAT and followers autom. parameter estim.; PP CALMO; statistical hyper-parameterization or post-processing • Improvement by non-physical extensions: interdisciplinary ; longer term • using direct impact of error estimates • Including non-deterministic aspects stochastic physics Sibiu 2013 COSMO Matthias Raschendorfer

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

7. Work on turbulence and SAT: • 3D-Smagorinsky scheme implemented (W.Langhans, ETHZ) • Implemented in private test version (already documented) • Horizontal shear production + horizontal diffusion can be activated as well using current TKE scheme • Allowing for TKE-advection (U.Blahak), • 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 • SSO-term: operational at DWD • Production by convection: needs to be verified, but model output for EDR-forecast • Horizontal shear term:tuning parameter only estimated, but still used for EDR-forecast • Diagnostics of TKE-scheme in stable conditions (Ines Cerenzia) • Analytical and experimental study • Report just availabe • 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 • implemented in private test-version; and ICON not yet verified; common version in work COSMO Sibiu 2013 Matthias Raschendorfer

8. Deardorff-restriction of turbulent master length scale (M.Raschendorfer) • 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) • Implemented in old test-version only; tested by E.Avgoustoglou • UTCS/TKESV: (D. Mironov, E. Maschuskaya) • TKESV extension; statistical cloud scheme now based on double Gaussian distribution • Implemented in test version • Turb-i-Sim: (J. Schmidli, O. Fuhrer, …) • Evaluation and improvement of COSMO turbulence over Alpine topography • Project at ETH and MeteoSwiss, just started Thermodynamic corrections: • Carrying adiabatic source terms in prognostic pressure equations (U.Blahak) • 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 • Implemented and tested: only small impact Sibiu 2013 COSMO Matthias Raschendorfer

9. 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 • 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) • Ready for testing in case of snow • 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 • 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) COSMO Sibiu 2013 Matthias Raschendorfer

10. Work on radiation: • Using an improved aerosol climatology (J.Helmert) • Test runs performed: currently too transparent clouds • Slightly modifying cloud cover diagnostics for ice clouds in radiation scheme (A.Seifert) • Already in current code • Considering precipitating hydrometeors in radiation calculation (U.Blahak) • In particular slowly falling snow should be considered • 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 • 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 • Promising, only research version prepared COSMO Sibiu 2013 Matthias Raschendorfer

11. Separated TKEequation (including scale interaction sources): Formal scale separation automatically produces interaction between GS parameterizations of turbulence and circulations • Additional Shear -Production of TKE by: • SSO wakes • Horizontal shear eddies • Vertical convective currents • Missing link; • Computationally extremely cheap; • clear impact More physically based mixing even for stable stratification eddy-dissipation rate(EDR) shear production by sub grid scale circulations transport (advection + diffusion) time tendency of TKE buoyancy production shear production by the mean flow labil: neutral: stabil: DWD Matthias Raschendorfer COSMO Lugarno 2012

12. including horizontal shear – and SSO-production reference mountain ridge pot. temperature [K] Wind speed [m/s] including horizontal shear –, SSO- and convective production COSMO-US: cross section across frontal line and Appalachian mountains DWD Matthias Raschendorfer COSMO Lugarno 2012

13. Consequences of scale interaction terms and general model improvment: • 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 Computationally extremely cheap; large impact in particular for T_2m_Min (SK=-13.33 for a 2-month exp.) !! DWD Matthias Raschendorfer CUS 2013

14. 15.11.2012 12 UTC

15. cloud-water-content/[Kg/Kg]: Routine Experiment time-height cut Vel/[m/s] Theta/[°C] all values are area averages

16. 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 TKMmin and TKHmin) enabled in stable cases Very stable conditions not well described and led to less stable cases Artificial mixing background TKE forcings sum increased so that Ri never exceeds Ric Increase of turbulence in stable conditions Ines Cerenzia: ISAC-CNR, Arpa-SIMC

17. Diagnostics of PBL parameterization in stable conditions First test: reduction of TKMmin and TKHminfrom 1 to 10-2 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 Ines Cerenzia: ISAC-CNR, Arpa-SIMC

18. Diagnostics of PBL parameterization in stable conditions 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 Ines Cerenzia: ISAC-CNR, Arpa-SIMC

19. 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

20. 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: • 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 skewnessSs 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 Ssequation 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

21. 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).

22. TKESV + New Cloud Scheme: TKE and Buoyancy Flux

23. Outlook • 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)

24. Challenges related to parameterization extensions: Sibiu 2013 COSMO Matthias Raschendorfer

25. 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 Stochastic physics ? • introduction of additional statistical moments by simulation of stochastic processes Sibiu 2013 COSMO Matthias Raschendorfer

26. Principal of the parameterization complements: Trying to improve physical parameterizations by systematic parameter tuning: • by minimizing the model error of a verification quantity might even decreasecurrentstochastic complement • Stochastic variation of tendencies • stochastic properties of SGS surface tiles or convective cells Stochastic variationsofmodel input: should decreaseexpectation ofstochastic complement Stochastic variationsof parameterizations: Model integration Model output Model input prognostic variables Hyper- parameterization boundary values implicit diagnostics initial values assimilation diagnosticmodel calculation global constants Explicite diagnostics regression coefficients global parameter local (external) parameter • Providing a list of parameter sub sets containing as few as possible parameters, related to specific conditionsand 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. parameter tuning Additional parameters for explicit diagnostics Observations conditional sampling increasing decreasing stochastic complement averaging averaging Superobservation Supercalculation compare Sibiu 2013 COSMO Matthias Raschendorfer

27. 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