Angela Mose - April 22

Convective Meteorology (Mesoscale Dynamics) Observing System Simulation Experiments using the Warn-On Forecast System and a Passive, Multistatic Radar Network for the 7 May 2020 Northern Texas Hail-Producing Supercell Angela Mose Friday, April 22 03:00 PM NWC 5600 The Warn-On Forecast System (WoFS) is a rapid assimilation, storm-scale, convection-allowing ensemble that

Start

April 22, 2022 - 3:00 pm

End

April 22, 2022 - 4:00 pm

Convective Meteorology (Mesoscale Dynamics)

Observing System Simulation Experiments using the Warn-On Forecast System and a Passive, Multistatic Radar Network for the 7 May 2020 Northern Texas Hail-Producing Supercell

Angela Mose

Friday, April 22

03:00 PM

NWC 5600

The Warn-On Forecast System (WoFS) is a rapid assimilation, storm-scale, convection-allowing ensemble that aims to provide watch-to-warning guidance for National Weather Service forecasters leading up to a convective event. WoFS differs from other convection-allowing models (CAMs) and ensembles (CAEs) in that it uses an Ensemble Kalman filter to assimilate satellite, radar reflectivity and radial velocity, surface observing station, and mesonet data (when available) roughly four times more often than other systems. Many studies have shown the capability of WoFS to provide accurate, short-term guidance to forecasters, and have shown WoFS’s ability to accurately forecast severe weather events, even when other CAMs/CAEs do not. Specifically, WoFS was one of few CAMs/CAEs that hinted at the August 2020 derecho across Iowa and northern Illinois. Despite the many benefits provided by WoFS, this model has struggled with accurately predicting convective initiation (CI). Namely, it is often too late, resulting in poor timing guidance for forecasters. This issue is exacerbated when storms initiate shortly after the model initializes, resulting from a relative lack of data being ingested compared to later times.

Recently, a network of low-cost multistatic, passive radar receivers have been developed for use with the WSR-88D network. A passive radar network allows for the sampling of crossbeam Doppler velocities across a wide region and provides a more accurate depiction of the three-dimensional wind field in and around thunderstorms. For this study, synthetic passive radar network data are generated and will be assimilated into WoFS around CI of a hail-producing supercell in Northern Texas on 7 May 2020. Observing System Simulation Experiments will be used to assess the implications of assimilating passive radar network observations into WoFS. The truth run was created by downscaling a single, real-time WoFS member forecast to 250 m horizontal grid spacing. Synthetic WSR-88D data and multistatic passive radar network observations were created from the truth run and will be assimilated into the remaining WoFS members to assess the impact of multistatic observations on the quality of WoFS analyses and forecasts. Object-based verification, ensemble spread, and other statistical methods will be used to compare the truth run to the experimental runs. Through the addition of the passive receiver velocity and reflectivity, it is expected that these data will improve the performance of WoFS around CI and will continue to enhance WoFS output throughout the forecast period.