14A.2 Assessing Impacts of Assimilating Surface Observations for the Forecast of Convection Initiation on 3 April 2014 within the Dallas-Fort Worth Urban Testbed

Thursday, 7 June 2018: 1:45 PM
Colorado A (Grand Hyatt Denver)
Nicholas Antonio Gasperoni, Univ. of Oklahoma, Norman, OK; and X. Wang, F. H. Carr, and K. A. Brewster

The ‘Nationwide Network of Networks’ (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system. Part of this growing need is the continued advancement toward accurate high-resolution numerical weather prediction. 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 a data assimilation system, 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 (ERNET) 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 ‘nonconventional’ observations are combined with conventional operational data from METARs, mesonet, aircraft, rawindsondes, profilers, and operational radars to form the testbed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information.

In this study, the GSI-based EnKF data assimilation system was used together with the WRF-ARW model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments were conducted testing the impact of high-frequency (5-min) assimilation of nonconventional data on the timing and location of CI, as well as storm development as they progress through the testbed domain. Results using ensemble probability of reflectivity and neighborhood ensemble probability of hail showed nonconventional observations were necessary to capture local details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations. Accurate metadata was found to be crucial to the application of nonconventional observations in high-resolution assimilation and forecasts systems.

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