Using mesoscale simulations to train statistical models of tropical cyclone intensity over land

Augustin Colette, Nadja Leith, Vincent Daniel, Enrica Bellone, David S. Nolan

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

The decay of tropical cyclones after landfall is a key factor in estimating the extent of the hazard overland. Yet our current understanding of this decay is challenged by the low frequency of past events. Consequently, one cannot rely solely upon the historical record when attempting to quantify robustly the inland penetration of tropical cyclones. Thus, a framework designed to complement the historical record of landfalling storms by means of numerical modeling is introduced. Historical meteorological situations that could potentially have led to a landfall on the coast of the Gulf of Mexico are targeted and, using a bogus vortex technique in conjunction with a mesoscale model, a large number of landfalling hurricanes are simulated. The numerical ensemble constitutes a more comprehensive sample of possible landfalling hurricanes: it encompasses the range of events observed in the past but is not constrained to it. This allows us to revisit existing statistical models of the decay of tropical cyclones after landfall. A range of statistical models trained on the numerical ensemble of storms are evaluated on their ability to reproduce the inland decay of historical storms. These models have more skill at predicting tropical cyclone intensity over land than similar models trained exclusively on historical data.

Original languageEnglish (US)
Pages (from-to)2058-2073
Number of pages16
JournalMonthly Weather Review
Volume138
Issue number6
DOIs
StatePublished - Jun 1 2010

Keywords

  • Mesoscale models
  • Statistical forecasting
  • Tropical cyclones

ASJC Scopus subject areas

  • Atmospheric Science

Fingerprint Dive into the research topics of 'Using mesoscale simulations to train statistical models of tropical cyclone intensity over land'. Together they form a unique fingerprint.

  • Cite this