152 Predicting 'Double Impact' Concurrent and Collocated Tornadoes and Flash Floods

Thursday, 10 November 2016
Broadway Rooms (Hilton Portland )
Gregory R. Herman, Colorado State Univ., Fort Collins, CO; and E. R. Nielsen, J. M. Peters, and R. S. Schumacher

While tornadoes and flash floods are both individually hazardous and dangerous phenomena associated with severe storms, when both threats are concurrently present at the same location, an especially threatening situation- with a unique combination of dangers and considerations- arises.  Nielsen et al. (2016) first investigated these so-called TORFF events- concurrent and collocated tornadoes and flash floods- and found that while TORFFs occurred most frequently in the southeastern US, TORFFs were observed across most of the contiguous United States (CONUS), and were also found in a wide assortment of synoptic environments and in association with many different types of precipitating systems, from isolated supercells, to mesoscale convective systems (MCSs), to tropical and extratropical cyclones.  A brief investigation of the large scale conditions associated with TORFF events found largely similar characteristics to environments that produced tornadoes without collocated flash floods (TORs), albeit with generally moister conditions and stronger synoptic-scale forcing for ascent.  Due to the unique dangers present in TORFF scenarios when compared with TORs, and the different preparative actions required, clear and accurate prediction and communication of TORFF threats is critical for public safety.  This follow-on work uses a record of model guidance from both global and convection-allowing scales to identify specific features in NWP output that correspond to TORFFs and build a skillful statistical model for the prediction of TORFFs.  Specifically, a combination of principal component analysis (PCA) and machine learning techniques will be applied towards output from NOAA’s Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) model and the National Severe Storms Laboratory WRF (NSSL-WRF).  Findings of the distinguishing meteorological characteristics and the predictive capabilities of the trained models will be presented.
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