10.2 Forecasting North American Monsoon Precipitation with Data Assimilation

Wednesday, 15 January 2020: 1:45 PM
259A (Boston Convention and Exhibition Center)
C. Bayu Risanto, The Univ. of Arizona, Tucson, AZ; and C. L. Castro, A. F. Arellano Jr., L. Mendoza-Fierro, and J. M. Moker Jr.

North American Monsoon precipitation substantially impacts the hydrometeorology of the semi-arid region of the southwest U.S. and northwest Mexico. Our project aims to improve short-term forecasts of monsoon precipitation in this region, especially with respect to convective organization and propagation, using convective-permitting modeling (CPM). Days during the monsoon can be characterized as ‘strongly’ or ‘weakly’ synoptically forced by the respective presence or absence of a transient inverted trough (IV) over northwest Mexico. We apply data assimilation (DA) of precipitable water vapor (PWV) observations to the CPM forecast simulations. PWV data are derived from 18 GPS PWV sensors deployed during a field campaign that took place in northwest Mexico from June-September 2017. In the 2017 monsoon, there were 39 weakly forced days and 9 strongly forced days. Of particular interest is the potential forecast improvement on the weakly forced days, as CPM forecasts are less skillful relative to strongly forced days. The Advanced Research Weather and Forecasting Model (WRF-ARW) with an ensemble adjustment Kalman Filter (EAKF) in the Data Assimilation Research Testbed (DART) is used for this research. The methodological approach to assimilating GPS-derived PWV will be summarized, emphasizing considerations for horizontal and vertical localization of the measurements. Simulations are evaluated against level-3 precipitation products from the Integrated Multi-satelliteE Retrievals for Global Precipitation Measurement Final product (GPM IMERG Final) and 23 rain gauge measurements located within the model domain. We also use an Infrared product from GPM MERGIR to validate the model-simulated Outgoing Longwave Radiation. Various verification metrics are used to assess forecast skill, including Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). This work serves to establish a methodological approach for assimilation of GPS-PWV, that may help to improve short-term forecasts in other regions of the world, that face similar logistical challenges as Mexico, with respect to available observational data to initialize high resolution numerical weather prediction forecast modeling systems.
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