Monday, 13 January 2020
Hall B1 (Boston Convention and Exhibition Center)
Operational forecasting centers are progressing toward the use of short-range, convection-allowing model (CAM) ensembles as they make upgrades to their computational resources. CAM ensembles provide quantitative probabilistic forecasts of convective hazards that forecasters would otherwise qualitatively assess, such as updraft helicity. However, various ensemble strategies can be used to generate CAM probabilistic forecasts, and it is still unclear how different configurations perform. This suggests that a focus on quality of initial conditions may be worth further study, especially in the frame of probabilistic guidance for convective hazards. Comparisons of CAM ensembles are challenging due to the computational resources required, especially when considering large verification periods. Because of this, previous studies have been limited in the number of cases the authors consider, as well as the number and type of verification methods used to assess forecast accuracy and quality.
In this study, we verify the current Texas Tech University (TTU) ensemble modeling system and three additional initial condition ensembles. The TTU Atmospheric Science group currently runs an EnKF data assimilation and ensemble modeling system with WRF-ARW version 3.5.1. The system assimilates observations every 6 hours on a 12-km domain over CONUS and initializes 48-hour forecasts every 12 hours at 0000 and 1200 UTC on the 12-km domain and a 4-km domain centered on the southern Great Plains. Holding the model configuration constant, we generate initial conditions for additional ensembles by downscaling GEFS member forecasts, perturbing from GFS initial conditions, and re-centering EnKF members on GFS initial conditions. These three strategies represent feasible systems that could become the TTU ensemble modeling system given its use in several public and private forecasting applications. We initialize forecasts for six consecutive weeks from 0000 UTC 27 April 2016 to 1200 UTC 3 June 2016 and verify them using traditional, object-based, and neighborhood methods.
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