To evaluate the impact of data assimilation in the precipitation forecast, we aggregate the results in a region within the inner domain, and then we calculate the average precipitation forecast between 0 and 36 predicted hours for RDFS with and without data assimilation. The results suggest a strong relationship between the forecast start time and the improve of precipitation forecast accuracy using data assimilation. The diurnal cycle of precipitation in the study region has a minimum in the morning (12 UTC) and a maximum in the afternoon (00 UTC) and during the night (09 UTC). The correspondence between the forecast improvement using data assimilation and the diurnal cycle of precipitation is likely due to the amount of assimilated data. In order to quantify the precipitation differences between the different microphysics parametrizations used in the SYNAPSIS strategy and the observed precipitation, we calculated the model skill scores for the average forecast of precipitation in a region inside the inner domain. Results show that the use of the latter three parametrizations is likely redundant because the results did not show a significant difference in forecasts skill among the different microphysics schemes. According to the results, we conclude that a configuration with the domain distribution of RDFS, with radar reflectivity data assimilation at the hour of maximum precipitation, produces the most accurate and skillfully forecast of precipitation. For surface air temperature, we observe systematic errors as a result of the height differences between the real and the model topography. We use a Model Output Statistics (MOS) technique to improve the forecast results, consisting of a quantile-quantile linear regression, between the observations and the temperature forecasts. This correction works well for mean temperatures, but not for maximum and minimum temperature. In general, the surface temperature forecasts are warmer at noon and colder before sunrise than the observations from an AWS network. We also compare the observed and simulated turbulent heat fluxes inside the urban areas; the difference has a strong correlation with the observed cold or hot bias in the temperature forecast.