Skill, Predictability, and Cluster Analysis of Atlantic Tropical Storms and Hurricanes in the ECMWF Monthly Forecasts

In this paper we analyze Atlantic Ocean hurricane activity in the European Centre for Medium-Range Weather Forecasts (ECMWF) monthly hindcasts for the period 1998-2017. The main climatological characteristics of Atlantic tropical cyclone (TC) activity are considered at different lead times and across the entire ECMWF ensemble using three diagnostic variables: the number of tropical cyclones, the number of hurricanes, and the accumulated cyclone energy. The impacts of changing horizontal resolution and stochastic parameterization are clear in these diagnostic variables. The model skill scores for the number of tropical cyclones and accumulated cyclone energy by lead time are also computed. Using cluster analysis, we compare the characteristics of the forecast TC tracks with observations. Although four of the ECMWF clusters have similar characteristics to observed ones, one of the ECMWF clusters does not have a corresponding one in observations. We consider the predictability of each of these clusters, as well the modulation of their frequency by climate modes, such as the El Nino-Southern Oscillation and the Madden-Julian oscillation, taking advantage of the very large sample size of TC datasets in these hindcasts.

Automated summary

This study analyzes Atlantic Ocean hurricane activity in ECMWF monthly hindcasts from 1998 to 2017, considering climatological characteristics, the impacts of changing resolution and parameterization, model skill scores, and cluster analysis comparing forecasted tracks with observations. The research explores the predictability of different clusters and their modulation by climate modes, utilizing a large sample size of TC datasets in the hindcasts.