Short description of the MOLOCH model (CNR-ISAC)



1. Model dynamics


MOLOCH is a non-hydrostatic, fully compressible, convection resolving model recently developed at ISAC-CNR. It integrates the set of atmospheric equations with prognostic variables (pressure p, absolute temperature T, specific humidity q, horizontal (u, v) and vertical (w) components of wind velocity, turbulent kinetic energy TKE and five water species, see below), represented on the latitude-longitude, optionally rotated, Arakawa C-grid (Arakawa and Lamb, 1977) .


A hybrid terrain following coordinate zita, relaxing smoothly to horizontal surfaces away from the earth surface, is employed. Model dynamics are integrated in time with an implicit scheme for the vertical propagation of sound waves, while explicit, time-split schemes are implemented for the time integration of the remaining terms of the equations of motion. Three dimensional advection is computed using the Eulerian Weighted Average Flux scheme (Billet and Toro, 1997). Horizontal second order diffusion and a small divergence damping are included to prevent energy accumulation on the shorter space scales.


2. Model physics


The physical scheme consists of atmospheric radiation, sub-grid turbulence, water cycle microphysics, and a soil model with vegetation.


The atmospheric radiation is computed with a combined application of the RG (Ritter and Geleyn, 1992) scheme and the ECMWF scheme (Morcrette, 1991; Mlawer et al., 1997). Since the ECMWF scheme is much more computationally expensive than the RG scheme, and hence could not be applied to each time step and each grid column, it is used at alternate points and at long intervals to compute corrections to the RG scheme, the latter being used at all grid points and in rapid update mode.  In 2012 the ECMWF radiation scheme has been updated to a more recent version using the RRTM algorithm for both visible and infrared bands (14 channels each) and the McICA (Monte-Carlo Independent Column Approximation) for computing the radiative effects of clouds (Morcrette et al, 2008).  The ECMWF radiation library includes definitions of the "climatology" (seasonal and geographical distributions) of different types of aerosol climatology and of atmospheric composition. In a recent model upgrade, all the inputs (astronomical functions, aerosol, ozone, greenhouse gases, albedo, emissivity  and clouds) have been made fully consistent between the two (GR and ECMWF) schemes. Cloud fraction (used as input for radiation schemes) is computed as a function of explicit cloud variables (liquid and solid), of relative humidity and of local dry and moist stability parameters.


The turbulence scheme is based on a E-l, order 1.5 closure theory, where the turbulent kinetic energy equation (including advection) is evaluated (Zampieri et al, 2005). Surface turbulent fluxes of momentum, specific humidity and temperature are computed by the classical Monin-Obukhov theory with Businger/Holtslag functions in the unstable/stable case.  The mixing length is computed from turbulent kinetic energy (Deardorff, 1980) in the stable atmosphere and from Bougeault and Lacarrere (1989), modified by Zampieri (2004), in the unstable environment. Over the sea, a Charnock (1955) roughness is introduced, which takes into account the wave height as a function of the surface wind speed. The sea surface temperature (SST) is computed, depending on latent and sensible heat fluxes and radiative fluxes, using a simple slab ocean model in which the analysed distribution of SST is used a relaxation reference value.


The microphysical scheme was initially based on the parameterization proposed by Drofa and Malguzzi (2004). The spectral properties of hydrometeors are simulated assuming a generalized gamma function distribution. A recently upgraded scheme includes a description of the following processes:

main processes described by the microphysical scheme are:

• nucleation of cloud water (cw) and of cloud ice (ci);

• condensation and evaporation of cw;

• freezing of cw;

• nucleation sublimation and melting of ci;

• auto-conversion of cw and of ci;

• sublimation of snow and graupel in both directions;

• collection/accretion/riming: 13 different hydrometeor interaction processes involving rain (freezing or not), snow and graupel (dry or melting), ci and  cw;

• melting and evaporation of hydrometeors;

• computation of terminal fall speeds  and fall process, using a conservative-diffusive backward-upstream integration scheme;

• thermodynamic feedback based on enthalpy conservation.


The above characteristic are similar to those of the BOLAM model scheme. However, specific differences are introduced in the MOLOCH scheme in order to treat the complex processes characterizing convective systems.  In particular, the MOLOCH scheme has the capability of describing the so-called two-moment microphysics, by integrating in time the spatial distribution of the number density of cw and ci , describing the cloud spectra evolution.


The soil model of MOLOCH is similar to that of BOLAM. It includes a soil model that uses 4-6 layers, whose depths (from a few cm to more than 1 m) increasing moving downward. The soil model computes surface energy, momentum, water and snow balances, heat and water vertical transfer, vegetation effects at the surface (evapo-transpiration, interception of precipitation, wilting effects etc.) and in the soil (extraction of water by roots). It takes into account the observed geographical distribution of different soil types and soil physical parameter. The soil model includes also treatment of water freezing and melting processes within the ground.


The MOLOCH model is normally nested into the BOLAM runs performed at coarser resolution. The model is optimized to operate as short-range (12-48 h) weather forecasting model, with horizontal resolution (grid spacing) in the range 1-4 km, with 40-80 atmospheric levels in the vertical.  For research purposes, it has been tested with horizontal resolution up to 500 m and more than 100 levels. The entire MOLOCH code is written in Fortran 90. It is fully parallelized, applying the domain splitting technique, and is compatible with MPICH2 and OpenMP parallel computing  environments. 


3. MOLOCH model short history


The development of the non-hydrostatic MOLOCH model started at ISAC in Bologna in year 2000 (Tettamanti et al, 2002), based on the experience gained in the development of the hydrostatic BOLAM model. It was created as a scientific tool in dynamic meteorology for high resolution  atmospheric simulations and mesoscale weather forecasting, allowing the explicit treatment of atmospheric convection.  Although specific research stand-alone versions of MOLOCH were created for scientific use (see, for instance, Fantini and Malguzzi, 2008; Malguzzi et al, 2010; Fantini et al, 2012), the model has been mainly employed, nested in BOLAM,  to form a model chain apt to simulate and study meteorological events and  to apply this tool for real-time forecasting. As examples of the latter case, the model was used for example to study episodes of heavy precipitation (Davolio et al, 2006; Malguzzi et al, 2006; Davolio et al, 2007, 2008, 2009;) and of strong winds (Tettamanti et al, 2002; Davolio et al, 2009; Buzzi et al, 2011).


The model was employed, among other studies, in the international forecasting demonstration project called MAP-DPHASE (Rotach et al, 2009), in which many mesoscale high-resolution NWP models where compared in real time (during autumn 2007), especially in relation to QPF (Quantitative Precipitation Forecasting - Davolio et al, 2009), and in the European project RISKMED (Bartzokas et al, 2010).  The MOLOCH model performance was directly compared with those of other mesoscale models and with generally satisfactory results (Zampieri et al, 2004; Richard et al, 2007; Diomede et al, 2008; Davolio et al, 2009; Bartzokas et al, 2010). Recently the MOLOCH model has been employed in conjunction with an oceanographic model to predict sea state and elevation over the Mediterranean sea around the Italian coasts (Kassandra project, Ferrarin et al, 2012). 


The MOLOCH model has been employed in real time forecasting, besides the above mentioned projects, by Italian agencies (for example, Region Liguria). The model is being used in a daily forecasting demonstration at CNR-ISAC, on behalf of the National Civil Protection Department. It has been used also during the Olympic Games in summer 2012, to assist the Italian sailing team (Federazione Italiana Vela).  





Arakawa, A., and V. R. Lamb, 1977: Computational design of the basic dynamical processes of the UCLA general circulation model. Methods in Computational Physics, J. Chang, Ed., Academic Press, 174–267.


Bartzokas, A., J. Azzopardi, L. Bertotti, A. Buzzi, L. Cavaleri, D. Conte, S. Davolio, S. Dietrich, A. Drago, O. Drofa, A. Gkikas, V. Kotroni, K. Lagouvardos, C.J. Lolis, S. Michaelides, M. Miglietta, A. Mugnai, S. Music, K. Nikolaides, F. Porcù, K. Savvidou, and M.I. Tsirogianni, 2010: The RISKMED project: Philosophy, methods and products. Nat. Hazards Earth Syst. Sci., 10, 1393–1401.


Billet, S. and Toro, E. F., 1997: On WAF-type schemes for multidimensional hyperbolic conservation laws, J. Comput. Phys., 130, 1–24.


Bougeault, P., and P. Lacarrere, 1989: Parameterization of orography-induced turbulence in a mesoscale model. Mon. Wea. Rev., 117, 1872-1890.


Buzzi, A., S. Davolio, O. Drofa, P. Malguzzi, 2011: High-resolution short-range wind forecasts with the ISAC model MOLOCH. Proceedings of the COST Action ES1002 "WIRE" Workshop, 22-23 March 2011, Nice, France.


Charnock, H., 1955: Wind stress over a water surface. Quart. J. Roy. Meteorol. Soc., 81, 639–640.


Davolio, S., A. Buzzi and P. Malguzzi, 2006: Orographic influence on deep convection: case study and sensitivity experiments. Meteorol. Z., 15,  215-223.


Davolio, S., A. Buzzi and P. Malguzzi, 2007: High resolution simulations of an intense convective precipitation event.  Meteorol. Atmos. Phys. 95, N. 3-4, 139-154.


Davolio, S., A. Buzzi and P. Malguzzi, 2009: Orographic triggering of  long-lived convection in three dimensions. Meteorol. Atmos. Phys. , 103, 35-44.


Davolio, S., D. Mastrangelo, M. M. Miglietta, O. Drofa, A. Buzzi and P. Malguzzi, 2009: High resolution simulations of a flash flood near Venice. Nat. Hazards Earth Syst. Sci., 9, 1671-1678.


Davolio, S., M. M. Miglietta, A. Moscatello, F. Pacifico, A. Buzzi and R. Rotunno, 2009: Numerical forecast and analysis of a tropical-like cyclone in the Ionian Sea. Nat. Hazards Earth Syst. Sci., 9, 551-562.


Deardorff, J. W., 1980: Stratocumulus-capped mixed layers derived from a three dimensional model. Boundary Layer Meteorol., 18, 495-527.


Diomede, T., S. Davolio, C. Marsigli, M. M. Miglietta, A. Moscatello, P. Papetti, T. Paccagnella, A. Buzzi and P. Malguzzi, 2008: Discharge prediction based on multi-model precipitation forecasts. Meteorol. Atmos. Phys., 101, 245-265.


Drofa, O. V., and P. Malguzzi, 2004: Parameterization of microphysical processes in a non hydrostatic prediction model. Proc. 14th Intern. Conf. on Clouds and Precipitation (ICCP). Bologna, 19-23 July 2004, 1297-3000.


Fantini M, and Malguzzi P., 2008: Numerical study of two-dimensional moist symmetric instability. Adv. Geosci., 17, 1–4.


Fantini, M., P. Malguzzi and A. Buzzi, 2012: Numerical study of slantwise circulations in a strongly-sheared pre-frontal environment. Quart. J. Roy. Meteor. Soc., 138, 585-595.


Ferrarin, C., M. Bajo, A. Roland, G. Umgiesser, A. Cucco, S. Davolio, A. Buzzi, P. Malguzzi and O. Drofa, 2012: Tide-surge-wave modelling and forecasting in the Mediterranean Sea with focus on the Italian coast. Ocean Modelling, in print.


Malguzzi, P., M. Fantini and A. Buzzi, 2010: Numerical study of a banded precipitation event over Italy. Adv. Geosci., 26, 19-23. 


Malguzzi, P., G. Grossi, A. Buzzi, R. Ranzi, and R. Buizza, 2006, The 1966 'century' flood in Italy: A meteorological and hydrological revisitation. J. Geophys. Res., 111, D24106.


Mlawer, E.J., S.J. Taubman, P.D. Brown, M.J. Iacono, and S.A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102D, 16, 663-682.


Morcrette, J.-J., 1991: Radiation and cloud radiative properties in the ECMWF operational weather forecast model. J. Geophys. Res., 96D, 9121-9132.


Ritter, B., and J. F. Geleyn, 1992: A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Wea. Rev., 120, 303-325.


Richard, E., A. Buzzi and  G. Zängl, 2007: Quantitative precipitation forecasting in mountainous regions: The advances achieved by the Mesoscale Alpine Programme. Q. J. R.. Meteorol. Soc., 133, 831-846.


Rotach, M.W., P. Ambrosetti, F. Ament, C. Appenzeller, M. Arpagaus, H.S. Bauer, A. Behrendt, F. Bouttier, A. Buzzi, M. Corazza, S. Davolio, M. Denhard, M. Dorninger, L. Fontannaz, J. Frick, F. Fundel, U. Germann, T. Gorgas, C. Hegg,  A. Hering, C. Keil, M.A. Liniger, C. Marsigli, R. McTaggart-Cowan, A. Montani, K. Mylne, R. Ranzi, E. Richard, A. Rossa, D. Santos-Muñoz, C. Schär, Y. Seity, M. Staudinger, M. Stoll, H. Volkert, A. Walser, Y. Wang, J. Werhahn, V. Wulfmeyer, M. Zappa, 2009: MAP D-PHASE: Real-time Demonstration of Weather Forecast Quality in the Alpine Region. Bull. Amer. Meteor. Soc., 90 (9), 1321–1336.


Tettamanti, R., P. Malguzzi and D. Zardi, 2002: Numerical simulation of  katabatic winds with a non-hydrostatic meteorological model. Polar Atmospheres, 1, 1-95. ISSN 1591-3902.


Zampieri, M., 2004: Comparison among first, second and third order CBL model. The 6th Symposium on Boundary Layers and Turbulence. Portland, ME.


Zampieri, M., P. Malguzzi and A. Buzzi, 2005: Sensitivity of quantitative precipitation forecasts to boundary layer parameterization: a flash flood case study in the Western Mediterranean. Natural Hazard Earth System Sci., 5, 603-612.