A Statistical Prediction Scheme for Integrated Kinetic Energy of Atlantic Tropical Cyclones

A new statistical-prediction scheme is presented for predicting Integrated Kinetic Energy (IKE) in North Atlantic tropical cyclones from a series of environmental input parameters. Predicting IKE is desirable because the metric quantifies the energy across a storm's entire wind field, allowing it to respond to changes in storm structure and size. As such, IKE is especially useful for quantifying the risk in large low-intensity high-impact storms such as Sandy in 2012. The prediction scheme, named the Statistical Prediction of Integrated Energy Version 2 (SPIKE2), builds upon a previous statistical IKE scheme, by using a series of artificial neural networks instead of more basic linear regression models. By using a more complex statistical scheme, SPIKE2 is able to distinguish non-linear signals in the environment that could cause fluctuations in IKE. In an effort to evaluate SPIKE2's performance in a future operational setting, the model is calibrated using input parameters from GEFS control analyses, and is run in a hindcast mode from 1990 to 2015 using National Centers for Environmental Prediction's Global Ensemble Forecast System version 9.0.1 (GEFS) reforecasts. The hindcast results indicate that SPIKE2 performs significantly better than both persistence and climatological benchmarks.