Assessing the Impact of High-Frequency Non-conventional Observations on a Convective Initiation Case using the GSI-based EnKF System

Nicholas Gasperoni* and Xuguang Wang
University of Oklahoma

The research testbed known as the Dallas – Fort Worth (DFW) Urban Demonstration network was created to experiment with many kinds of mesoscale observations that could be used in data assimilation systems, in order to identify observational systems that are most impactful on high-resolution forecasts. Many observation systems have been implemented for the DFW testbed, including Earth Networks Weather Bug surface stations, Citizen Weather Observer Program (CWOP) amateur surface stations, Global Science and Technology (GST) mobile truck observations, CASA X-band radars, SODARs, and radiometers. These “non-conventional” observations are combined with conventional operational data from METARs, mesonet, aircraft, rawindsondes, profilers, and operational radars to form the testbed network. In this study, the GSI-based EnKF data assimilation system is used together with the WRF-ARW core model to examine impacts of observations assimilated for the 3 April 2014 case. On that day, two rounds of supercells initiate in Wise County, Texas, within the NW portion of the testbed domain along a NE-SW oriented dryline. The storms advance ENE within the northern part of the testbed domain, producing large hail and a few tornadoes. Data denial experiments are conducted testing the impact of high-frequency (5-min) assimilation of nonconventional data on the timing and location of the initiation of these supercells, as well as their development as they progress through testbed domain.. There are two areas to investigate the impacts of non-conventional observations relative to conventional observations. First, the non-conventional observations offer opportunities to fill in areas where conventional observations are lacking near areas of storm initiation. Second, the non-conventional observations provide a much higher resolution snapshot along and just ahead of the dryline within the DFW domain. In the future, results of these data denial experiments will be compared with Ensemble-based Forecast Sensitivity to Observations (EFSO) metric (Kalnay et al. 2012; Gasperoni and Wang 2015).

*Preference: Poster