Using a Neural Network Calibration of the SREF PQPF to Increase Situational Awareness By

1. Using a Neural Network Calibration of the SREF PQPF to Increase Situational Awareness By Jeff Davis, Senior Forecaster WFO Tucson, Arizona Goals of this project: a. Increase awareness of potential high impact precipitation events. b. Begin integrating PQPF concepts into WFO operations c. Downscale calibrated SREF PQPF to a 5km NDFD grid 3. Output January 19, 2010 February 2, 2010 2. Process used to create calibrated SREF PQPF values JOONE Threat key: AWIPS SREF Ensemble Data • Visualization for Algorithm Development • Java API • VisAD Java API used to extract SREF values from an • AWIPS netCDF file • - Neural Network 3 hourly SREF Input • Mean precipitable water • 5km gridded elevation • Probability of CAPE > 500 • Forecast hour • PQPF for each threshold • (PQPF> .01, >.25,>.50, > 1.0) • Separate neural network for each • model cycle • Java Object Oriented Neural Engine API • - Open source neural network API used to process SREF • data and determine relationships between the key • parameters. • The neural engine can determine these • relationships with a couple of years of data. • These relationships are divided into a winter • (Oct 1 to Mar 31) and summer (Apr 1 to Sept 30) season. • Ground truth is determined from the RTMA. None PQPF > 0.01 with Prob < 10% in any one time period Low PQPF > 0.01 with Prob > 10% in more than 4 periods Moderate PQPF > 0.25 with Prob > 40% in 2 or more periods High PQPF > 0.25 with Prob > 40% in 4 or more periods or PQPF > 0.50 with Prob > 20% in 2 or more periods