Convective Meteorology (Mesoscale Dynamics)

Using the NSSL-WRF Ensemble to Provide Hazard Guidance

Burkely Twiest
OU School of Meteorology

06 February 2015, 2:00 PM

National Weather Center, Room 5600
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

Experimental versions of convection-allowing models (CAMs) have been providing meteorologists with insights to convection and the associated hazards of tornadoes, hail, and winds for the past decade. CAMs allow for guidance with regards to occurrence, areal coverage, and mode of convection by explicitly allowing convective processes rather than parameterizing them. Hazard forecasting, a subset of convective forecasting, often takes the form of probabilities, as seen in the outlooks issued by the Storm Prediction Center (SPC). Forecasters have traditionally formulated hazard probabilities through synthesizing numerical output from both ensembles and deterministic models with observations and prior experience. The research community has attempted to objectively generate similar hazard probabilities using both deterministic CAMs and CAM-based ensembles. Development and application of CAMs to hazard forecasting will first be discussed, with a focus on CAM-based ensembles.
The NSSL-WRF high-resolution ensemble is one CAM ensemble currently being used to develop 24hr hazard probabilities from hourly output of relevant variables. This study is currently generating baseline probabilities from hourly maximum proxy variables (updraft helicity, maximum estimated hail size, and 10m winds) computed by the members of the ensemble. These probabilities use a neighborhood radius consistent with the SPC’s definition of severe weather, to aid in comparison with products already in use operationally. Tunable parameters such as proxy variable threshold and smoothing radius have been examined via ROC curves and reliability diagrams. Preliminary results indicate over-forecasting of severe events, particularly for wind. The over-forecasting for all hazards could be related to a number of factors. For example, non-convective events can be included in the wind probabilities, while tornado probabilities are generated using simulated mesocyclones and only a portion of observed mesocyclones produce tornadoes.
Future work will first focus on improving the baseline hazard probabilities and reducing the over-forecasting by incorporating environmental data tailored to individual hazards. The generated probability product is to be tested in the Hazardous Weather Testbed, and input from forecasters will be a key aspect of the product development.

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