Neighborhood- and Object-Based Probabilistic Verification of the OU MAP Ensemble Forecasts during 2017 and 2018 Hazardous Weather Testbeds

• DOI: 10.1175/waf-d-19-0060.1
• Publication Date: 2019-12-06T14:19:26Z
• Authors: Aaron Johnson; Xuguang Wang; Yongming Wang; Anthony Reinhart; Adam J. Clark; Israel L. Jirak
• Keywords: Predictability; Ensemble forecasting; Probabilistic Forecasting; Forecast verification; Consensus forecast

Abstract An object-based probabilistic (OBPROB) forecasting framework is developed and applied, together with a more traditional neighborhood-based framework, to convection-permitting ensemble forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation Predictability (MAP) laboratory during 2017 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Case studies from are used for parameter tuning demonstration methodology, while systematically verified. The case study demonstrates that OBPROB forecast product can provide unique tool operational forecasters includes convective-scale details such as storm mode morphology, which typically lost in methods, also providing quantitative guidance about those easily interpretable format than commonly paintball plots. objective verification metrics reveal different relative performance at lead times depending on (i.e., object versus neighborhood) because features emphasized object- evaluations. Both frameworks then systematic evaluation 26 spring 2018. configured this shows less sensitivity time neighborhood forecasts. indicate need calibration improve reliability. However, lower discrimination noted.