Short description of the BOLAM model

Short description of the BOLAM model (CNR-ISAC)

1. Model main characteristics

BOLAM is a hydrostatic meteorological model, operating over a limited area of the globe, developed at CNR-ISAC (Bologna). The prognostic variables are the wind components u and v, the absolute temperature T, the surface pressure p_s, the specific humidity q and the turbulent kinetic energy TKE. The water cycle for stratiform precipitation is described by means of five additional prognostic variables: cloud ice, cloud water, rain, snow and graupel.

1.1 Model dynamics

The model prognostic variables are distributed in the vertical on a non-regular Lorenz (1960) grid, with higher resolution in the atmospheric boundary layer near the lower surface. The vertical discretization is based on a hybrid vertical coordinate system, in which the terrain-following sigma coordinate (p/p_s) gradually tends to a pure pressure coordinate with increasing height above the ground. The relaxing factor from sigma to p is prescribed as a function of the orography height in the domain. The horizontal discretization is based on a staggered Arakawa-C grid (Arakawa and Lamb, 1977), in geographical coordinates (latitude-longitude). The equator can be taken to be any great circle on the Earth in order to minimize grid anisotropy over the limited area of the simulation. The advection scheme presently implemented is the Weighted Average Flux scheme (WAF – Billet and Toro, 1997).

The temporal integration scheme is split-explicit, forward-backward for the gravity modes. The horizontal diffusion scheme is of the second order for all the prognostic variables, except for surface pressure. Local and vertically integrated divergence is slightly diffused to control internal and external gravity wave modes. The lateral boundary conditions are applied on a number (typically 8) of grid point rows, using a relaxation scheme (Leheman, 1993) that efficiently absorbs wave energy, helping in reducing spurious reflection by the lateral boundaries.

1.2 Model physics: atmospheric water cycle

The scheme describing the processes related to the atmospheric water cycle (model “microphysics”) has been recently modified with respect to the earlier BOLAM scheme that was based mainly on the simplified approach of Schultz (1995). The updated scheme is based on explicit assumptions of spectral distributions of clouds (droplets and ice crystals) and of liquid and solid hydrometeors. The spectral properties of hydrometeors are simulated assuming a generalized gamma function distribution. The 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.

Although this scheme has been devised to represent mainly stratiform precipitation processes (since convection is parameterized), it has been made as coherent as possible with the microphysical scheme implemented in the MOLOCH model, in order to assure the maximum consistency between the two models that are typically run in cascade.

The effects due to the development of deep moist convection are parameterized using the Kain–Fritsch (Kain and Fritsch, 1990) convective scheme, updated on the basis of the revision proposed by Kain (2004). The version implemented in BOLAM has been re-written by imposing conservation of liquid water static energy and after having introduced modifications in some processes (e.g. downdraft starting height and evaporation). Parameterization of shallow moist convection is also implemented as comprised in the same scheme, with some modifications.

1.3 Model physics: parameterization of turbulence and of orographic drag

The surface layer and the planetary boundary layer are modelled accordingly to the similarity theory (Monin and Obukhov, 1955). The mixing-length based turbulence closure model, widely used to compute the ABL (Atmospheric Boundary Layer) fluxes for atmospheric modelling, is applied to parameterize the turbulent vertical diffusion of momentum, heat and moisture. The turbulence closure is of order 1.5 (E-l scheme, Zampieri et al, 2005), in which the TKE is predicted. To take into account buoyancy effects for a stratified ABL, the Blackadar (1962) mixing length is used together with stability functions that depend on the Richardson number. For the unstable ABL case, a modified version of the non-local mixing length of Bougeault and Lacarrere (1989) is applied. The roughness length is computed as a function of vegetation and of sub-grid orography variance. 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.

A parameterization of the orographic wave drag, associated with the deceleration of the mean flow passing over orography, is applied.

1.4 Model physics: surface, soil and vegetation processes

BOLAM 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 soil model has been subject to various upgrades in recent years.

1.5 Model physics: atmospheric radiation

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. Regarding cloud variables, the implementation of the new microphysical scheme has allowed to compute cloud fraction (used as input for radiation schemes) as a function of explicit cloud variables (liquid and solid) and of local dry and moist stability parameters. A small fraction of cloud cover is still made depending also on relative humidity, in order to parameterize subgrid moisture variability.

BOLAM has the capability to perform one-way nested simulations. The highest suitable resolution is limited by the hydrostatic approximation and by the parameterization of convection: it is typically around 6-8 km. For smaller space scales, the MOLOCH model is used, typically nested into BOLAM. The entire BOLAM 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.

2. BOLAM model short history

The BOLAM model (Buzzi et al, 1994; Malguzzi and Tartaglione, 1999, Buzzi and Foschini, 2000, Malguzzi et al, 2006, Orlandi et al, 2010) was continuously developed over the years, starting in 1992. The model was tested and favourably compared with many other mesoscale limited area models, in the course of the Comparison of Mesoscale Prediction and Research Experiments (COMPARE) multi-annual project organized by the World Meteorological Organisation (WMO). Model inter-comparison was conducted on a case of mid-latitude explosive cyclogenesis (Compare I: Gyakum et al, 1996), on a well documented case of flow over orography in the presence of lee waves and wakes (Compare II: Georgelin et al, 2000), and on a case of explosive development of a tropical cyclone ("super-typhoon") over the Pacific (Compare III: Nagata et al, 2001). In the first comparison exercise (1995), the cumulative BOLAM model score of performance turned out to be the second among 14 different limited area models (Gyakum et al, 1996). In the second (1999), devoted to much smaller scales characterizing the flow past steep orography, BOLAM was the best model over 13 mesoscale models regarding what the organizers considered the most stringent test, i.e. the formation of a mesoscale low level vortex in the lee of the Pyrenees (Georgelin et al, 2000). In the third exercise (2001), BOLAM performed as the best over 12 models in simulating both the intensity and the precipitation field of the predicted typhoon growth and evolution (Nagata et al, 2001). The BOLAM model also exhibited the comparatively best performance in simulating precipitation fields in the Alpine area in the context of the EU RAPHAEL project (Bacchi and Ranzi, 2000; Richard et al, 2003). It also performed among the most accurate mesoscale models during the MAP (Mesoscale Alpine Programme) field phase (Bougeault et al, 2001; Richard et al, 2006).

The BOLAM model is being used operationally at various Italian national agencies and regional meteorological services (see, for example, Corazza et al, 2003; Chessa et al, 2004; Mariani et al, 2011), by the National Observatory of Athens in Greece (Lagouvardos et al, 2003; Lagouvardos and Kotroni, 2006), by the National Center for Hydrological and Meteorological Forecasting (NCHMF) of Viet Nam (Ranzi et al, 2012; Ngo et al, 2012) and by the University of Addis Ababa in Ethiopia. The BOLAM model is being employed in real time forecasting, 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).

BOLAM has been used for various research purposes and studies, including case studies of heavy precipitation (see, for example, Buzzi et al, 1994; Buzzi et al, 1998; Buzzi and Foschini, 2000; Buzzi et al, 2003; Malguzzi et al, 2006; Davolio et al, 2009; Orlandi et al, 2010; Martiani et al, 2011; Ranzi et al, 2012), of marine forecasting (Cavaleri et al, 2010) and idealized studies, using a channel version of the model grid (Miglietta and Buzzi, 2004; Buzzi and Catania, 2009). Recently, a chemical and aerosol transport model BOLCHEM has been developed at ISAC, starting from the BOLAM dynamical core.

References: BOLAM model code and development

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.

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

Blackadar, A. K., 1962: The Vertical Distribution of Wind and Turbulent Exchange in a Neutral Atmosphere, J. Geophys. Res., 67, 3095–3102.

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

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

Kain, J.S., 2004: The Kain-Fritsch convective parametrization: an update. J. App. Meteorol., 43, 170-181.

Kain, J. S. and Fritsch, J. M., 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802.

Leheman, R., 1993: On the choice of relaxation coefficients for Davies’ lateral boundary scheme for regional weather prediction models. Meteorol. Atmos. Phys., 52, 1-14.

Lorenz, E., 1960: Energy and numerical weather prediction. Tellus, 12, 364-373.

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, 663-16, 682.

Monin, A. S. and Obukhov, A., 1955: Basic laws of turbulence mixing in the surface layer of the atmosphere. Trudy Geophys. Inst. AN SSSR, 24, 163–187.

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

Morcrette, J.-J., H.W. Barker, J.N.S. Cole, M.J. Iacono, and R. Pincus, 2008: Impact of a new radiation package, McRad, in the ECMWF Integrated Forecasting System. Mon. Wea. Rev., 136, 4773-4798.

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.

Schultz, P., 1995: An explicit cloud physics parameterization for operational numerical weather prediction. Mon. Wea. Rev., 123, 3331–3343.

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

References: BOLAM scientific literature

Bougeault, P., P. Binder, A. Buzzi, R. Dirks, J. Kuettner, R. Houze, R.B. Smith, R. Steinacker, H. Volkert, 2001: The MAP Special Observing Period. Bull. Am. Meteor. Soc., 82, 433-462.

Buzzi, A., R. Cadelli and P. Malguzzi, 1997: Low level jet simulation over the Antarctic ocean. Tellus, 49A, 263-276.

Buzzi, A., and L. Catania, 2009: Dynamical and physical processes characterizing upper-level cut-off lows in winter. Extended abstract, 30th International Conference on Alpine Meteorology, Rastatt (GE), 11-15 May 2009. Annalen der Meteorologie, 44, 240-241. Deutscher Wetterdienst.

Buzzi, A., S. Davolio, M. D’Isidoro and P. Malguzzi, 2004: The impact of resolution and of MAP reanalysis on the simulations of heavy precipitation during MAP cases. Meteorol. Z., 13, 91-97.

Buzzi, A., M. D'Isidoro, S. Davolio, 2003: A case study of an orographic cyclone south of the Alps during the MAP SOP. Quart. J. Roy. Meteor. Soc., 129, 1795-1818.

Buzzi, A., M. Fantini, P. Malguzzi and F. Nerozzi, 1994: Validation of a limited area model in cases of Mediterranean cyclogenesis: surface fields and precipitation scores. Meteorol. Atmos. Phys., 53, 137-153.

Buzzi, A., and L. Foschini, 2000: Mesoscale meteorological features associated with heavy precipitation in the southern Alpine region. Meteorol. Atmos. Phys., 72, 131-146.

Buzzi, A., N. Tartaglione and P. Malguzzi, 1998: Numerical simulations of the 1994 Piedmont flood: Role of orography and moist processes. Mon. Wea. Rev., 126, 2369-2383.

Cardinali, C., M. Caian, J. Pailleaux, N. Tartaglione, A. Buzzi, A. Lavagnini, 1998: An intercomparison between a global variable mesh and two limited area models on a case of rapid cyclogenesis. Meteorol. Atmos. Phys., 65, 93-111.

Cavaleri, L., L. Bertotti, R. Buizza, A. Buzzi, V. Masato, G. Umgiesser, M. Zampieri, 2010: Predictability of extreme meteo-oceanographic events in the Adriatic Sea. Quart. J. Roy. Meteor. Soc., 136, 400-413.

Chessa, P.A., G. Ficca, M. Marrocu and R. Buizza, 2004: Application of a limited-area short-range ensemble forecast system to a case of heavy rainfall in the mediterranean region. Wea. Forecasting, 19, 566-581.

Corazza, M., A. Buzzi, D. Sacchetti, E. Trovatore, C. F. Ratto, 2003: Simulating extreme precipitation with a mesoscale forecast model. Meteorol. Atmos. Phys., 83, 131-143.

Davolio, S., and A. Buzzi, 2002: Mechanisms of Antarctic katabatic currents near Terra Nova Bay. Tellus, 54A, 187-204.

Davolio, S., and A. Buzzi, 2004: A nudging scheme for the assimilation of precipitation data into a mesoscale model. Wea. Forecasting, 19, 855-871.

Davolio, S., A. Buzzi and P. Malguzzi, 2009: Orographic triggering of long-lived convection in three dimensions. Meteorol. Atmos. Phys. , 103, 35-44. DOI 10.1007/s00703-008-0332-5.

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.

Georgelin, M., P. Bougeault, T. Black, N. Brzovic, A. Buzzi, J. Calvo, V. Cassé, M. Desgagné, R. El-Khatib, J. F. Geleyn, T. Holt, S.-Y. Hong, T. Kato, J. Katzfey, K. Kurihara, B. Lacroix, F. Lalaurette, Y. Lemaitre, J. Mailhot, D. Majewski, P. Malguzzi, V. Masson, J. Mcgregor, E. Minguzzi, T. Paccagnella and C. Wilson 2000: The second COMPARE exercise: a model intercomparison using a case of a typical mesoscale orographic flow, the PYREX IOP3. Quart. J. Roy. Meteor. Soc., 126, 991-1030.

Gyakum, J.R., M. Carrera, D.-L. Zhang, S. Miller, J. Caveen, R. Benoit, T. Black, A. Buzzi, C. Chouinard, M. Fantini, C. Folloni, J.J. Katzfei, Y.-H. Kuo, F. Lalaurette, S. Low-Nam, J. Mailhot, P. Malguzzi, J.M. McGregor, M. Nakamura, G. Tripoli and C. Wilson., 1996: A regional model intercomparison using a case of explosive oceanic cyclogenesis. Weather and Forecasting, 11, 521-543.

Koussis A., K. Lagouvardos, K. Mazi, V. Kotroni, D. Sitzmann, J. Lang, H. Zaiss, A. Buzzi, and P. Malguzzi, 2003: Flood forecasts for an urban basin with an integrated hydro-meteorological model. Journal of Hydrological Engineering, 8, 1-11

Lagouvardos, K., V. Kotroni, A. Koussis, C. Feidas, A. Buzzi, P. Malguzzi, 2003: The meteorological model BOLAM at the National Observatory of Athens: Assessment of two-year operational use. J. Appl. Meteor., 42, 1667-1678.

Lionello, P., P. Malguzzi and A. Buzzi, 1998: Coupling between the atmospheric circulation and the ocean wave field: an idealized case. J. Phys. Ocean., 28, 161-177.

Malguzzi, P. and N. Tartaglione, 1999: An economical second order advection scheme for explicit numerical weather prediction. Quart. J. Roy. Meteor. Soc., 125, 2291-2303.

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,

Malguzzi, J. McGregor, A. Murata, J. Nachamkin, M. Roch, C. Wilson, 2001: A Mesoscale Model Intercomparison: A Case of Explosive Development of a Tropical Cyclone (COMPARE III). J. Meteorol. Soc. Japan, 79, 999-1033.

Mariani, S., O. Drofa, S. Davolio, A. Buzzi, A. Speranza, 2011: The impact of the assimilation of satellite-retrieved precipitation and humidity products into the hydrostatic BOLAM: two Italian case studies. Proceedings of the 6th European Conference on Severe Storms (ECSS 2011), 3 - 7 October 2011, Palma de Mallorca, Spain.

Miglietta, M.M., and A. Buzzi, 2004: A numerical study of moist stratified flow regimes over isolated topography. Quart. J. Roy. Meteor. Soc., 130, 1749-1770.

Nagata, M., L. Leslie, H. Kamahori, R. Nomura, H. Mino, Y. Kurihara, E. Rogers, R. L. Elsberry, B. K. Basu, A. Buzzi, J. Calvo, M. Desgagne, M. D'Isidoro, S.-Y. Hong, J. Katzfey, D. Majewski, P. Nagata, M., L. Leslie, Y. Kurihara, R. L. Elsberry, M. Yamasaki, H. Kamahori, R. Abbey, K. Bessho, J. Calvo, J. C. L. Chan, P. Clark, M. Desgagne, S.-Y. Hong, D. Majewski, P. Malguzzi, J. McGregor, H. Mino, A. Murata, J. Nachamkin, M. Roch, C. Wilson, 2001:Third COMPARE workshop: A model intercomparison experiment of tropical cyclone intensity and track prediction. Bull. Am. Meteor. Soc., 82, 2007-2020.

Ngo L.A., T.T. Hoang, H.S. Nguyen, M.C. Vũ, L.T. Do, V.H. Vo, S. Davolio, O. Drofa, P. Malguzzi, S. Barontini, R. Ranzi, R., 2012: A hydrometeorological flood forecasting system for the Red and Ca rivers (China, Laos and Vietnam). Part II: application and results, Proc. XXXIII Conference of Hydraulics and Hydraulic Engineering, Brescia (Italy), 10-14 September 2012, Bacchi B., Ranzi R. and Tomirotti M. (editors), ISBN: 978-88-97181-18-7 (on CD), Edibios, Cosenza (Italy), 10 pp.

Orlandi, E., F. Fierli, S. Davolio, A. Buzzi, O. Drofa, 2010: A nudging scheme to assimilate satellite brightness temperature in a meteorological model: Impact on representation of African mesoscale convective systems. Quart. J. Roy. Meteor. Soc., 136, 462-474.

Ranzi, R., L.A. Ngo, T.T. Hoang, H.S. Nguyen, S. Barontini, G. Grossi, B. Bacchi, A. Buzzi, S. Davolio, O. Drofa, P. Malguzzi, L.T. Do, V.H. Vo, M.C. Vu, 2012: A hydrometeorological flood forecasting system for the Red and Ca Rivers (China, LAOS and Vietnam) Part I: investigated areas and model setup, Proc. XXXIII Conference of Hydraulics and Hydraulic Engineering, Brescia (Italy), 10-14 September 2012, Bacchi B., Ranzi R. and Tomirotti M. (editors), ISBN: 978-88-97181-18-7 (on CD), Edibios, Cosenza (Italy), 10 pp.

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.

Richard., E., S. Cosma, R. Benoit, P. Binder, A. Buzzi and P. Kaufmann, 2003: Intercomparison of mesoscale meteorological models for precipitation forecasting. Hydrology Earth System Sciences (HESS), 7(6), 799-811.