The spatial configuration of the MM5 used in this work consisted in five nested domains and twenty four unevenly spaced sigma levels. The domains has an horizontal resolution of 71, 27,9, 3 and 1 km, respectively. The results of the last domain, of 1km resolution, were finally evaluated. Two-ways nesting was used to feed the information between domains. Atmospheric initial and boundary conditions were extracted from the analysis produce by the NCEP. The physical parameterizations used in the simulation were: the GRELL scheme for the cumulus parameterization, the MRF for the planetary boundary layer, the MIXED PASHED for the explicit moisture, the FIVE-LAYER SOIL for the soil model and the RRTM for the radiation scheme. This configuration is maintained for all the integrations. Finally, a 24 hours spin-up period was used in each 72-hours integration.
The simulations were carried out for a set of days selected along the year 2005. Particularly, four sets (one for each season of the year) of three consecutive days with clear-sky conditions were selected, one for each season of the year. As highlighted earlier, the aim of this work is to evaluate the ability of the MM5 to estimate the solar radiation in a complex topography area. To this end, two simulations were carried out for each season of the year. In one of the simulations (called hereinafter T, Topography), the solar radiation were computed using the MM5 subroutines OROSHAW and LEVSLP. These subroutines allows taking into account the effect of the slope, angle and shadow cast caused by the topography on the solar radiation estimates at the earth surface. In the second simulation, these subroutines were not used and, therefore, the MM5 solar radiation estimates do not account for these topographic effects. We will call these simulations as Not Topography (NT).
The MM5 estimates were evaluated in terms of the Mean Error (ME) and the Root-Mean-Square Error (RMSE). The ME quantified the overall bias and detected if the model is producing overestimation or underestimation, while the RMSE accounts for the spread of the error distribution. Al error estimates are computed using hourly values along the whole simulated period.
Table 2 to 5 shows the evaluation results for, respectively, winter, spring, summer and autumn,. The results are just presented for three representative stations: stations 4, 5 and 11. These stations have a important slope and represent different aspects (stations 4 aspect east, stations 5 aspect west and station 11 aspect south) (Table 1). Evaluation results present the comparison of the MM5 estimates using (T) and not using (NT) the topographic parameterization against the measured horizontal solar radiation (H). For instance, the NT-H notation in Tables 2 to 5 stands for the evaluation of the MM5 estimates not using topographic parameterization against the global horizontal radiation measured values.
Overall, the MM5 shows considerable skills in estimating the solar radiation under clear-sky conditions along the whole year, even for this complex topography area under study. Particularly, the lowest RMSEs are found in summer (~20%) and the highest during winter (more than 30%). Additionally, from the analysis of the ME values, it could be concluded the existence of a general tendency to overestimation in winter, spring and summer and to underestimation in autumn.
Regarding the topographic parameterization, tables 2 to 5 shows and overall improvement in the estimates, although this improvement strongly depends on the season of the year and the topographic characteristics of the location under study. Regarding the season of the year and as can be expected, the most important improvement takes place in winter (Table 2). The relatively low sun elevation angles during this seasons makes the topographic influence on the solar radiation measured ant the surface more important. The use of the topographic parameterization improves the MM5 estimates, in terms of RMSE, ranging from 10% of improvement in station 11 to less than 5% in the stations 4 and 5. This difference can be explained by the fact of that the station 11 has south aspect, while the station 4 has a west aspect and station 5 an east aspect. Therefore the station 11 receives more radiation than the other two and the potential improvement is higher. For the rest of the seasons, the improvement in the estimates provides by the MM5 topographic parameterization are lower than for the winter. Particularly, in spring and autumn, Tables 3 and 5, the improvement in the estimates in terms of the RMSE ranges from 1% to 7%. Particularly, the most important improvement (7%) is found during autumn and for station 5. Similar results are found in terms of the ME. During summer (Table 4), and as can be expected, the improvements in the estimates provide by the topographic parameterization are modest. The only important improvement (4% in terms of RMSE) is found for station 5.
 G. A. Grell, J. Dudhia and D. R: Stauffer, (1994). A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). Tech. Rep. NCAR/TN-398+STR, National Center for Atmospheric Research.