Session 6A.5 The conditional risk of severe convection estimated from archived NWS/Storm Prediction Center mesoscale objective analyses: potential uses in support of forecast operations and verification

Tuesday, 2 June 2009: 5:00 PM
Grand Ballroom East (DoubleTree Hotel & EMC - Downtown, Omaha)
Andrew R. Dean, CIMMS/Univ. of Oklahoma, Norman, OK; and R. S. Schneider, R. L. Thompson, J. Hart, and P. D. Bothwell

Presentation PDF (900.8 kB)

The SPC maintains a database of environmental parameters associated with severe convection. These parameters are archived from hourly SPC mesoscale analysis grids that are available from 2003-present. The database also includes severe weather reports, gridded CG lightning data, and archived SPC forecast products.

Using the environmental analyses in conjunction with lightning and report data, the conditional probability of severe convective elements (tornado, wind, hail) is calculated given the presence of deep convection (using lightning as a proxy). In particular, we have used a 5-year sample (2003-2007) from the environment database to estimate the conditional risk of tornadoes as a function of CAPE, CIN, deep-layer (0-6 km) bulk shear, low-level (0-1 km) bulk shear, and LCL height. In addition to investigating various combinations of these ingredients, we have also populated a 5-dimensional parameter space that includes all of these ingredients in order to develop a multi-parameter estimate of the conditional tornado risk.

The utility of this work to the SPC is two-fold. First, an objective multi-parameter estimate of the conditional tornado (or wind/hail) risk would be a valuable real-time tool in assisting forecast operations. Second, this conditional risk can be used as a proxy for the difficulty of the forecast, which would provide a valuable context for SPC's forecast verification. Using archived analysis data from the past can thus be used to help forecast operations in the present, in terms of real-time guidance, and in the future, by providing valuable feedback that can be used by forecasters to improve forecast performance. Results of this ongoing research and visions for its future will be presented.

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