Impact of Precipitable Water on Forecasting the 2013 North American Monsoon

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Wednesday, 7 January 2015: 11:25 AM
127ABC (Phoenix Convention Center - West and North Buildings)
James M. Moker Jr., University of Arizona, Tucson, AZ; and Y. L. Serra, C. Castro, and A. F. Arellano Jr.

To investigate the role of precipitable water (PW) observations on forecasting convection in the North American Monsoon (NAM) region, significant weather events are simulated using the Advanced Research Weather and Forecasting modeling system (WRF-ARW) during the 2013 season. Our WRF configuration uses three nested domains with spatial resolutions of 30 km, 10 km, and 2.5 km and boundary/initial conditions derived from the 32-km resolution North American Mesoscale Forecast System (NAM) and the 0.5-degree resolution Global Forecast System (GFS). As part of collaborative effort between the University of Arizona (UA) and the Universidad Nacional Autónoma de México (UNAM), ten (10) GPS-Met sensors were deployed across the northern Sierra Madre Occidental (SMO) and along the coastal plains of Sonora and Sinaloa as part of The NAM GNSS/GPS Monsoon Transect Experiment 2013. These sensors provided PW observations at 5-minute temporal resolution in areas where mesoscale convective complexes initiate and mature during the NAM. Case studies are categorized based on the presence of mesoscale forcing, synoptic forcing, or a combination of both, and focus on the southern Arizona and northwest Mexico region. Meteorological variables from WRF output are validated against regional surface radar imagery, satellite rainfall and cloud top temperatures, and surface GPS-Met PW observations. The ultimate goal of this project is to improve the accuracy of NAM thunderstorm forecasts. This will be done by determining the sensitivity of short-term forecasts to assimilation of GPS-Met PW observations into the WRF runs by using an ensemble Kalman filter. Unlike 3-Dimensional and 4-Dimensional Variational data assimilation schemes, the ensemble Kalman filter permits specific humidity in the WRF simulations to be nudged in a directionally-dependent manner in time and space, providing a more accurate representation of the observations being assimilated into the model.