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

As numerical weather prediction continues its advancement towards accurate convective-scale resolution forecasts, the need for more mesoscale coverage of observations has become increasingly important. A nationwide “Network of Networks” initiative provides a path towards a national mesoscale observing network, through the combination of observations from many government and private sector providers. As part of this effort, a research testbed known as the Dallas – Fort Worth (DFW) Urban Demonstration network has been created to experiment with many kinds of observations that could be used in a data assimilation system, in order to identify observational systems that are most impactful on a forecast. Many observation systems have been implemented for the DFW testbed, including X-band radars from the Center for Collaborative Sensing of the Atmosphere, Earth Networks Weather Bug surface stations, surface stations from the Citizen Weather Observer Program (CWOP), observations from Global Science and Technology (GST) trucks, SODARs, and radiometers. These “non-conventional” observations are combined with 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 the 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. Data denial experiments will focus in particular on relative impacts of observations from GST transportation fleets, as well as amateur surface stations from CWOP and Earth Networks Weather Bug stations. 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) . The EFSO metric is appealing because no adjoint or additional experiments are necessary to evaluate impacts from several different observing platforms.