VOLUME 126 MONTHLY WEATHER REVIEW JUNE 1998 The Operational CMC±MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation JEAN COà TE AND SYLVIE GRAVEL Meteorological Research Branch, Atmospheric Environment Service, Dorval, Quebec, Canada ANDRE ME THOT AND ALAIN PATOINE Canadian Meteorological Centre, Atmospheric Environment Service, Dorval, Quebec, Canada MICHEL ROCH AND ANDREW STANIFORTH Meteorological Research Branch, Atmospheric Environment Service, Dorval, Quebec, Canada (Manuscript received 31 March 1997, in ®nal form 3 October 1997) ABSTRACT An integrated forecasting and data assimilation system has been and is continuing to be developed by the Meteorological Research Branch (MRB) in partnership with the Canadian Meteorological Centre (CMC) of Environment Canada. Part I of this two-part paper motivates the development of the new system, summarizes various considerations taken into its design, and describes its main characteristics. 1. Introduction time and space scales that are commensurate with those An integrated atmospheric environmental forecasting associated with the phenomena of interest, and this im- and simulation system, described herein, has been and poses serious practical constraints and compromises on is continuing to be developed by the Meteorological their formulation. Research Branch (MRB) in partnership with the Ca- Emphasis is placed in this two-part paper on the con- nadian Meteorological Centre (CMC) of Environment cepts underlying the long-term developmental strategy, Canada. The bilingual acronym GEM has been adopted and on mesoscale results obtained using the described for the model around which this system is constructed. GEM model at its present state of development. Thus in English the model is designated as ``the Global The goals of Part I are as follows: Environmental Multiscale model,'' whereas in French R Motivate and outline the staged and ongoing devel- it is referred to as ``le modeÁle Global Environmental opment of a comprehensive and fully integrated global Multi-eÂchelle.'' atmospheric environmental forecasting and simulation There are three important motivations for modeling system. the atmosphere. These are to forecast the weather, ad- R Discuss various design considerations. dress climate issues such as global change, and address R Summarize the current status of development. air quality issues such as smog, ozone depletion, and Part II (CoÃte et al. 1998) is dedicated to presenting acid rain. Modeled atmospheric phenomena cover a very mostly mesoscale results for the GEM model, in par- broad range of time and space scales, varying tempo- ticular those that led to its operational implementation rally from the subsecond scales of some chemical re- for regional forecasting on 24 February 1997 at the actions to the centuries or millennia of climate simu- CMC. lation, and spatially from the fractions of a meter of chemical reaction and molecular diffusion, right through to the global scale of tens of thousands of kilometers. 2. Rationale for developing a universal modeling Numerical models of the atmosphere must be run with system a. Operational weather forecasting considerations Two operational data assimilation and weather fore- Corresponding author address: Dr. Jean CoÃteÂ, Recherche en PreÂvi- sion NumeÂrique, 2121 Route Transcanadienne, Dorval, PQ H9P 1J3, casting cyclesÐone global and one regionalÐhave been Canada. running daily at the CMC for a number of years. The E-mail: [email protected] global cycle, based on a spectral model (Ritchie and q 1998 American Meteorological Society 1373 Unauthenticated | Downloaded 09/23/21 07:10 PM UTC 1374 MONTHLY WEATHER REVIEW VOLUME 126 Beaudoin 1994), addresses medium-range forecasting to 3 days (Yessad and BeÂnard 1996). Because of some needs and global data assimilation. The regional cycle unanticipated intrinsic limitations on resolution (Caian provides the more detailed short-range (to 2 days) fore- and Geleyn 1997) due to the use of the Schmidt (1977) casts over North America and some of its adjacent wa- coordinate transformation to achieve variable resolution ters, and was based on the Regional Finite Element with a spectral model, a limited-area nonhydrostatic ver- (RFE) model (Mailhot et al. 1997) until its recent re- sion (ALADIN) has been developed (Bubnova et al. placement by the GEM model. 1995) for higher and more-focused resolution applica- Even though the two-cycle strategy is a costly choice, tions using code shared with that of the global version. it is considered the only acceptable alternative for ful- The strategy of the UKMO is both similar and dif- ®lling the operational needs for both medium-range (and ferent. It uses the UKMO's global uniform-resolution therefore necessarily global) forecasting, and high-res- ®nite-difference uni®ed forecast/climate model (Cullen olution regional forecasting. If the two cycles are cen- 1993; Cullen et al. 1997) for medium-range forecasting tered around two distinct models, the strategy then re- and climate simulation, and a limited-area code-shared quires the maintenance, improvement, and optimization version for mesoscale forecasting. In the context of me- of two sets of libraries and procedures. This is very dium-range forecasting and climate simulation, Cullen labor intensive (Courtier et al. 1991; Cullen 1993; CoÃte et al. 1993), and is so for principally three reasons. First, (1993) remarked that ``Maintenance of two separate sys- numerical weather prediction models and data assimi- tems is no longer practicable or justi®ed.'' In the context lation systems need signi®cant recoding in order to reap of medium-range and mesoscale forecasting he further the bene®ts afforded by the new high-performance sig- noted that the uni®ed strategy ``avoids the need for two ni®cantly parallel computer platforms (Barros et al. separate teams of scientists and software systems, and 1995; Dickinson et al. 1995; Drake et al. 1995; Estrade allows the techniques used in large-scale modelling to and Birman 1995; Hack et al. 1995; Hammond et al. be rigorously tested at the higher resolution used in the 1995; Henderson et al. 1995; Isaksen and Barros 1995; mesoscale model against the detailed observations avail- Michalakes et al. 1995; von Laszewski et al. 1995; Wol- able over the United Kingdom.'' It also ensures a certain ters et al. 1995). Second, to improve the accuracy of consistency between the driven and driving models re- the initial state of the atmosphere, that is, that of the garding the numerical methods and parameterizations analysis, requires a signi®cant investment in the re- used, inasmuch as there is much communality for the search and development of new data assimilation meth- latter between the two con®gurations. ods (e.g., Daley 1991). In this regard, the development In our view, most of the considerations that led both of the tangent linear model of a forecast model and its MeÂteÂo-France and the UKMO to adopt uni®ed modeling adjoint, often needed for four-dimensional data assim- strategies also apply in the Canadian context. Our own ilation [e.g., Courtier et al. (1991), (1994)], is time con- uni®cation strategy is based on the global variable-res- suming. Third, to improve the predictive capability of olution strategy outlined in the shallow-water proof-of- a weather forecast model requires that a signi®cant effort concept work of CoÃte et al. (1993). As mentioned there- be devoted to the improvement of model parameteri- in, the adopted strategy was in¯uenced by the Courtier zations. These motivate the consolidation of both the and Geleyn (1988) work but is, as argued both in CoÃte global and regional assimilation and forecasting systems et al. (1993) and below, more ¯exible and of broader within a single ¯exible modeling framework. application. To build a model whose intrinsic versatility would allow it to be used as the axle of both forecast cycles, and therefore create a uni®ed environment, was thus the b. Air quality modeling considerations main incentive for the development of the GEM model. Using similar reasoning, both MeÂteÂo-France (Courtier A multiscale atmospheric model is also bene®cial for et al. 1991) and the U.K. Meteorological Of®ce (Cullen the study of a wide range of air quality issues. A con- 1993) have developed uni®cation strategies, which are, sistent treatment of physical processes in the atmo- however, different. At MeÂteÂo-France, the IFS/ARPEGE/ ALADIN (Integrated Forecast System/Action de Re- sphere, advection, and chemical conversions, is now cherche Petite E chelle Grande E chelle/Aire LimiteÂe Ad- recognized as essential to appropriately model the aptation Dynamique DeÂveloppement International) fore- earth's atmospheric chemistry (Dastoor and Pudykiew- cast system has been developed in collaboration with icz 1996; Rood 1996). The coupling of the GEM model the ECMWF (European Centre for Medium-Range with a comprehensive treatment of chemistry should Weather Forecasts). It is based on the use of a global provide a framework for the study of atmospheric chem- variable-resolution spectral model as proposed in Court- istry, both tropospheric and stratospheric, from the glob- ier and Geleyn (1988). For medium-range applications al scale down to the meso-g scale. The integrated system the system is run at uniform resolution by ECMWF, also potentially permits better operational forecasts due whereas it is run by MeÂteÂo-France with variable reso- to an improved radiation budget based on a reliable lution concentrated over France for regional forecasts ozone distribution. Unauthenticated | Downloaded 09/23/21 07:10 PM UTC JUNE 1998 COà TE ET AL. 1375 c. Research community modeling considerations olution over the area of interest, initial conditions, and lower boundary conditions. On the one hand, the Canadian mesoscale community needs a nonhydrostatic mesoscale research model for the development and validation of physical parameter- d. Climate modeling considerations izations, such as surface- and boundary layer phenom- As time progresses, the distinction between weather- ena, moist convection, gravity wave drag, as well as for forecast and climate models becomes ever blurred due nowcasting research.