An Analog Methodology for Identifying Tropical Cyclone Intensity Forecast Biases in the Hurricane Weather Research and Forecasting (HWRF) Model

Understanding and forecasting tropical cyclone (TC) intensity change continues to be a paramount challenge in the research and operational communities alike.  In fact, the National Hurricane Center and Joint Typhoon Warning Center list guidance on TC intensity change as their highest priority operational forecast improvement need.  Numerous statistical, dynamical, and statistical-dynamical guidance products are available, and each contains inherent systematic biases that can be difficult to diagnose.  This talk presents a method to identify biases in any model by comparing large intensity error forecasts with analog forecasts that exhibit small intensity errors. Specifically, this methodology is applied to the Hurricane Weather Research and Forecasting (HWRF) model. The results can provide important guidance to operational forecasters and inform model developers where deficiencies exist.

This study examines numerous 2015 version HWRF 24 h TC intensity forecasts during 2011-2014 over the North Atlantic and eastern North Pacific basins. Forecasts with large intensity error are defined as errors in the top 10% of the sample distribution.  These are compared to analog forecasts with small intensity errors (taken from the bottom 50% of the error distribution to ensure a sufficient sample).  Analog forecasts must contain 0-24 h intensity and wind shear magnitude traces that are similar to the large intensity error forecasts to ensure that the differences between large and small intensity errors are not due to differences in TC intensity or environmental shear. Composites of the large and small intensity error forecasts will be presented in a shear relative reference frame. A comparison/contrast of results between the basins also will be discussed.