Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Previous studies have shown that simulations of North American Monsoon (NAM) precipitation using a convective-permitting Weather Research and Forecasting (WRF) model exhibit high sensitivity to the initial specification of the precipitable water vapor (PWV) field. This is especially true during weakly-forced days when limited dynamic forcing mechanisms present in the main NAM region are available to facilitate convective organization. These studies suggest that assimilating observational data of PWV may be of greatest value in improving model precipitation forecasts for these types of days. Here, we extend these studies by assimilating GPS-derived PWV into the same model configuration of WRF used in previous convective simulations during the NAM GPS Transect Experiment 2013 (Transect 2013). We apply an ensemble-based data assimilation (DA) system using the Data Assimilation Research Testbed (DART) in integrating PWV observations into WRF several hours prior to initiation of each daily 24-hour model forecasts for the duration of Transect 2013. Our initial results show that there is a relative improvement in PWV and rainfall forecasts in northwestern Mexico with PWV assimilation as compared to forecasts without assimilation. However, we also find no appreciative improvements at high elevations in the central Sierra Madre Occidental because of limited PWV data constraints in this area. We report some key factors influencing the performance of WRF/DART (e.g., DA configuration, data availability, structural biases in WRF, and error specification) in improving initial estimates and consequently its model forecasts.
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