1 Introduction and motivation

The aim of this paper is to present the numerical results obtained by global a non-hydrostatic computational model to predict severe hydrometeorological mesoscale weather phenomena on complex orographic area. In particular, the performed experiments regard the impact of heavy rain correlated to a fine topography. In the framework of an agreement between CIRA and the Earth Simulator Center (ESC), the new Global Cloud Resolving Model (GCRM)[1] has been tested on the European area The GCRM was developed by Holistic Climate Simulation group at ESC. More specifically, the selected forecast test cases were focusing on the North-Western Alpine area of Italy, which is characterised by a very complex topography [2].

The GCRM model is based on a holistic approach: the complex interdependences between macroscale and microscale processes are simulated directly. This can be achieved by exploiting the impressive computational power available at ESC.

In standard models the microscale phenomena have spatial and time scale smaller than the minimum allowed model grid resolution; thus, a parameterization is necessary in order to take into account the important effect that the interaction between phenomena at different length scales produces on weather. However, this parameterization introduces some arbitrariness into the model. On the other hand, by using the Earth Simulator it is possible to adopt a resolution higher than that ever tested so far, thus decreasing the model arbitrariness.

Another advantage of using GCRM is that both global (synoptic) and local (mesoscale) phenomena can be simulated without introducing artificial boundary conditions (which cause some “side effects” in regional models); the latter being the approach adopted in the nested local model.

Computational meteorological models with higher resolution, as emphasized before, have a more correct representation of the terrain complex orography and also a more realistic horizontal distribution of the surface characteristics (as albedo and surface roughness). These characteristics are very interesting compared with those of LAMI (Italian Limited Area Model) [3] [4], the numerical model operatively used in Italy to forecast mesoscale-phenomena. The non-hydrostatic local model LAMI uses a computational domain covering Italy with a horizontal resolution of about 7 km . The initial and boundary conditions are provided by the European global models (ECMWF [5] and/or GME [6]), which have a resolution of about 40 km. In LAMI the influence of the wave drag on upper tropospheric flow is explicitly resolved, while in the global models the wave drag can only be simulated adopting a sub-grid orography scheme, as the small scale mountain orography is at subgrid scale level. LAMI model provides a good forecast of the general rain structure but an unsatisfactory representation of the precipitation distribution across the mountain ranges. An improvement of the rain structure was obtained [7] by adopting a not-operative version of the LAMI model with a higher resolution (2.8 km). Furthermore, the convection phenomena are explicitly represented in the LAMI version with higher resolution, and smaller and more realistic rainfall peak have been computed.

This background was useful to analyze and give suggestions in order to improve the GCRM. The performances of the model have been assessed in some test cases [8]. The focus of the analysis has been placed on the evolution of the Quantitative Precipitation Forecast (QPF), one of the most complex and important meteorological variables. An accurate estimation of spatial and temporal rainfall is also important to forecast floods [9]. The simulations performed aimed to investigate the QPF sensitivity with respect to some physical and numerical parameters of the computational model.

2 The test cases

The GCRM performances were studied by considering one event of intense rain occured on November 2002 from 23^th to 26^th in Piemonte, a region in the Northwestern part of Italy. Piemonte is a predominantly alpine region, of about 25000 km , situated on the Padania plain and bounded on three sides by mountain-chains covering 73% of its territory (figure 1).

One problem to well forecast the meteorological calamitous in this area is due to its complex topography in which steep mountains and valleys are very close to each other (figure 2). In the event investigated, the precipitation exceeded 50 mm/24 hours over a vast Alpine area, with peaks above 100 mm over Northern Piemonte and also 150 mm in the South-Eastern Piemonte (figure 3).

3 Characteristic of numerical model and performed runs

A very special topic of the GCRM is the Yin-Yang grid system, characterized by two partially overlapped volume meshes which cover the Earth surface. One grid component is defined as part of the low-latitude region between 45N and 45S, extended for 270 degrees in longitude of the usual latitude-longitude grid system; the other grid component is defined in the same way, but in a rotated spherical coordinate system (ffigure 4)[10].

The upper surface of both meshes is located 30000 m above sea level. The flow equations for the atmosphere are non-hydrostatic and fully compressible, written in flux form [11]. The prognostic variables are: momentum, and perturbation of density and pressure, calculated with respect to the hydrostatic reference state. Smagorinsky-Lilly parameterization for sub-grid scale mixing, and Reisner scheme for cloud microphysics parameterization are used. A simple radiation scheme is adopted. The ground temperature and ground moisture are computed by using a bucket model as a simplified land model. No cumulus parameterization scheme is used, with the hypothesis that the largest part of the precipitation processes is explicitly resolved with a 5.5 km grid resolution*. Silhouette topography is interpolated to 5.5 km from the 1 km USGS Global Land One-kilometer Base Elevation (GLOBE) terrain database. In the GCRM, as in LAMI, the influence of the wave drag on upper tropospheric flow is explicitly resolved. The GCRM programming language is Fortran90. On Earth Simulator (ES) 640 NEC SX-6 nodes (5120 vector processors) are available. The runs were performed using 192 nodes for a configuration with 32 vertical layers; the elapsed time for each run was about 6 hours. For each ran the forecast time was 36 hours.

A particular hybrid model of parallel implementation is used in this software code; the parallelism among nodes is achieved by MPI or HPF, while in each node microtasking or OpenMP is used. The theoretical peak performance using 192 nodes is about 12 TFlops, while the resulting sustained peak performance is about 6 TFlops; this means that the overall system efficiency is 48.3 %

4 The spin up

Since observation data are not incorporated into this version of GCRM, some period is required by the model to balance the information for the mass and wind fields, coming from the initial data interpolated by analysis* (data from Japan Meteorological Agency, JMA). This feature gives rise to spurious high-frequency oscillations of high amplitude during the initial hours of integration. This behaviour is called “spin up”. For GCRM the spin up is also amplified because the precipitation fields (graupel, rain and snow) are set to zero at initial time. A more accurate interpolation and a reduction of the “spin up” phenomenon should be obtained by using a small ratio between the horizontal resolution of the analysis (data to initialize the model run) and of the model one. In our test cases the horizontal resolution of the analysis is 100 km while the GCRM resolution is 5,5 km (in some test cases also 11 km was used). Test performed with similar meteorological models have shown that a good resolution ratio is between 1:3-1:6 [12] [13]. The spin up “window” occurs for 6 to 12 hours (in the forecast time) after initialization; in order to avoid spin up problems the compared results are obtained by cumulating QPF data on 24 hours, beginning from +12 to +36 forecasts hours, then the first 12 hours of forecast are not reliable.

Without a sufficiently long spin-up period the output data may contain, as verified in our runs, strange transient values of QPF. In particular one typical spin up problem, consisting in an erroneous structure of rainfalls (with “spot” of rain maxima), has been identified in the runs performed.

Acknowlegment

The authors wish to thank all the meteorological staff of the Environment Protection Regional Agency of Piedmont (ARPA) for having provided the data and maps and for help and support.