Ensemble Statistics for Diagnosing Dynamics: Tropical Cyclone Track Forecast Sensitivities Revealed by Ensemble Regression

JMA’s Five-day Tropical Cyclone Track Forecast

Since 22 April 2009, the Japan Meteorological Agency (JMA) has issued five-day tropical cyclone (TC) track forecasts. This paper introduces these forecasts, including the method used, interpretation of the results obtained, forecast cases and future challenges. One of the forecast’s unique aspects is its determination of the radius of a 70% probability circle (PC) using the level of forecast un...

Ensemble Prediction of Tropical Cyclone Genesis

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, includ...

Stochastic Exchange Coefficients on Ensemble Tropical Cyclone Intensity

Tropical Cyclone (TC) intensity forecasts are impacted by errors in atmosphere and ocean initial conditions and the model formulation, which motivates using an ensemble approach. This study evaluates the impact of uncertainty in atmospheric and oceanic initial conditions, as well as stochastic representations of the drag (Cd) and enthalphy (Ck) exchange coefficients on ensemble Advanced Hurrica...

Relating Tropical Cyclone Track Forecast Error Distributions with Measurements of Forecast

Bagging Ensemble Selection for Regression

Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have shown that using random trees as base classifiers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, super...