Creation of real-time probabilistic thunderstorm guidance products from a time-lagged ensemble of High Resolution Rapid Refresh (HRRR) forecasts

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Monday, 18 January 2010: 4:45 PM
B314 (GWCC)
Curtis R. Alexander, NOAA/ESRL/GSD and CIRES-Univ. of Colorado, Boulder, CO; and D. A. Koch, S. S. Weygandt, T. G. Smirnova, and S. G. Benjamin

Starting in June of 2009, ESRL GSD began creating a real-time experimental probabilistic thunderstorm guidance product based on the High Resolution Rapid Refresh (HRRR). The HRRR (Weygandt et al, this conference) is an hourly updating, convection resolving model run over a domain covering the eastern 2/3 of the United States. The HRRR utilizes a 3 km horizontal grid spacing configuration of the Weather Research and Forecasting (WRF) model, with a diabatic digital filter initialization (DFI) based technique radar reflectivity assimilation procedure. Plans for the 2010 convective season include an expansion of the HRRR domain to cover all of the U.S. and a further demonstration / evaluation of its utility in providing guidance for convective storms and other mesoscale applications.

The real-time probabilistic thunderstorm guidance product is known as the HRRR Convective Probability Forecast (HCPF) and uses time-lagged ensemble output from the HRRR to create thunderstorm likelihood forecasts. Verification and evaluation of an initial prototype HCPF product over the summer 2009 convective season has lead to a number of refinements to the algorithm leading to improved performance. These refinements have included: 1) switching from use of the HRRR reflectivity field as the primary predictor to an hourly summed updraft field, 2) use of a diurnally varying updraft threshold to adjust for diurnal variations in the bias, and 3) use of a linear regression procedure to obtain non-constant weight factors for the different time-lagged ensemble members. Ongoing evaluation of the HCPF indicates very encouraging performance.

At the conference we will describe the HCPF algorithm, describe the enhancements to it and illustrate the improvement in skill from the enhancements. Presentation of traditional skill score metrics will be augmented by case study examples. Lastly, we will discuss ongoing work, including plans to extract probability information from the HRRR for other useful guidance parameters, including storm mode and porosity, and likelihood of specific hazards (hail, tornadoes, etc.)