Wednesday, 19 July 2023: 11:15 AM
Madison Ballroom CD (Monona Terrace)
Accurate forecasts of the development and evolution of deep, moist convection in convection-allowing models (CAMs) are both a priority and a challenge for the National Oceanic and Atmospheric Administration (NOAA). Additionally, modeling of the microphysical and internal structures of convection is difficult, as this can affect the storm mode, intensity, and longevity. Novel observations from the WSR-88Ds and GOES-16 have the potential to improve the forecasts of deep convection in CAM ensembles. Since the upgrade to the national network of WSR-88Ds was completed in 2013, polarimetric radar data offer a wealth of information about the shape, size, and type of hydrometeors present in clouds. Several distinct polarimetric signatures in early stages of deep convection have been identified, such as the differential reflectivity (ZDR) column. These columns are vertical protrusions of positive ZDR values above the environmental melting level and can aid significantly in characterizing storm updrafts. Information on the updraft location and intensity have potential to improve CAM representation of convection. In addition, GOES-16 infrared all-sky brightness temperatures provide complimentary information on cloud structures and cover that Doppler radars cannot directly measure. To explore the benefits of both types of data, an ensemble data assimilation approach is used with their simultaneous assimilation. The CAM selected for this study is the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model with the High-Resolution Rapid Refresh (HRRR) configuration. Observations are assimilated using the Ensemble Kalman Filter (EnKF). Different observations in the experiments are conducted jointly and separately, and all experiments include conventional observations. Analysis is conducted using a real case to realize the influence of these observations on different aspects of the convection, and results are presented and discussed.

