Poster Session P1.72 Extreme precipitation events over southern Mexico: Sensitivity of WRF simulations to cloud microphysics parameterizations

Monday, 28 June 2010
Exhibit Hall (DoubleTree by Hilton Portland)
Victor Torres Puente, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico; and G. B. Raga

Handout (2.9 MB)

Mexico is characterized by a variety of climates, from extreme desert conditions in the Northwest region to tropical jungle in the Southern states (between 15 and 18 N). Moreover, these latter regions that climatologically receive over 1500 mm annual average of accumulated precipitation, are located in very rugged terrain, with mountains ranges reaching 3000m amsl, less than 100km from the coasts, enhancing the potential of orographic precipitation and are very susceptible to flooding and landslides. A general goal of our research group is to understand the meteorological events that lead to extreme precipitation events in the Pacific coast of southern Mexico. These intense events appear to be linked to easterly waves passing over the region, tropical cyclogenesis in the Gulf of Tehuantepec or other variability in the Inter-Tropical Convergence Zone. The precipitation time series from several surface stations in the region were analyzed to determine the climatological extreme events (90th percentile). Several recently observed cases of extreme precipitation were then selected for further research.

In the present study, the Weather Research and Forecasting (WRF) model has been used to simulate extreme precipitation events near the coast of the State of Oaxaca under different microphysical schemes (Lin, WRFSM3, WRFSM5 & Thompson). The WRF model was used with three nested domains with the innermost domain of 8 Km grid spacing with explicit convection. For validating simulated precipitation of these events, we have compared the results against the CMORPH precipitation estimates and with local weather stations data available. The different parameterizations explored lead to a range of accumulated surface precipitation, in some cases largely underestimating observed amounts. These results could then be used in local weather offices to improve the forecasting estimate of precipitation.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner