Simulation of the Diurnal Cycle of Integrated Precipitable Water in the North American Monsoon Region

Abstract:

Organized deep convection over the North American monsoon region (NAM) is a salient climatic feature that has been the subject of several experimental campaigns and modeling efforts. Recently, however, in Mexico and the Caribbean, there has been mounting interest towards implementing low-cost, low-maintenance GPS-meteorological networks (TLALOCNet and COCOnet) that provide near real-time Integrated Precipitable Water data (IPW) into the assimilation cycle of regional models. A wealth of interesting new observational results concerning the link between the diurnal cycle of deep convection and the processes that could alter it at the surface and aloft has open up opportunities of model verification and improvements to the physics that are specific to subtropical deep convection. In this work, the diurnal cycle of IPW is studied using observational data collected during the North American Monsoon GPS Transect Experiment 2013 experiment and numerical simulations with the Weather Research and Forecasting model (WRF). WRF was run in climate mode to generate a simulation for the entire experiment using ECMWF ERA-Interim analysis data for initial and boundary conditions and spectral nudging. We classified the days during the experiment, according to type of mesoscale phenomena present each day and averaged days with same weather types in both data sets (observed and simulated). Preliminary results show that the simulated diurnal cycle of IPW is very sensitive to Land Use/Land Cover data and to initial and the boundary conditions. Preliminary results show that the simulated amplitude and phase of the diurnal cycle of IPW is well represented only when a more up-to-date LULC is used (MODIS v.s. 99 USGS LULC) and the Thompson mycrophysics scheme is used. In agreement with the previous results, modeled precipitation time series agree better with observed GPS-meterological station reports during the NAM 2013 experiment.