| The Mexico City metropolitan area (MCMA) has complex meteorological patterns which affect millions of people and pose flash flood and air pollution risks. Mexico City is situated in a basin, surrounded by higher terrain to the west, south, and east, with a valley further south and southwest. Local topography as well as the larger synoptic meteorological environment influence circulation patterns. The challenges include how to better understand MCMA flow fields, which would enhance the predictability of weather events, as well as ascertain interseasonal and interannual patterns and variability, leading to planning and policy benefits.;Numerical simulation, satellite, and observational data from the Megacity Initiative: Local And Global Research Observations (MILAGRO) campaign were used and focused on 21--31 March 2006. Choice of planetary boundary layer was found to affect model results more so than other examined parameters.;Model kinematic and thermodynamic fields generally agree with observations. However, cold, moist biases prevail at 2 m, with temperature biases peaking in late afternoon and moisture biases in the evening, along with diminished diurnal cycles in temperature. Overproduction of precipitation is evident in numerical simulation data and is believed to be a large contributor to such error.;Greater understanding of processes involved with WRF error should allow for improved forecasting, such as highlighting risk areas for heavy rainfall and high pollution on a given day. TRMM data showed similar precipitation patterns when compared to WRF, though precipitation amount was roughly 5 times less for the 11-day period. Satellite data over land confirmed seasonal and diurnal cycles in precipitation in past studies. No long-term trend in rainfall amounts was evident, and monthly variability appeared weaker than in observations. Regarding satellite data, the magnitude of the rainfall amounts over land was problematic; passive microwave sensing techniques often yielded underestimates, possibly due to lesser amounts of ideal scatterers in warm rain clouds over complex topopgraphy. Better understanding of numerical model error in addition to a fuller understanding of climatology from satellite data may also help determine policy evaluation, providing potential social benefits. |
