Predictability of tropical cyclone intensity evaluated through 5-yr forecasts with a convection-permitting regional-scale model in the atlantic basin

Yunji Zhang, Zhiyong Meng, Fuqing Zhang, Yonghui Weng

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The practical predictability of tropical cyclone (TC) intensity in terms of mean absolute forecast error with respect to different conditions at forecast initialization was explored through convection-permitting hindcasts of all Atlantic storms during the 2008-12 hurricane seasons using the Weather Research and Forecasting (WRF) Model. Averaged over a total of 2190 simulations, the day 1-5 performance of these WRF hindcasts was comparable to two operational regional-scale hurricane prediction models used by the National Hurricane Center (NHC) but was slightly inferior to the NHC official forecasts. It was found that the prediction accuracy of TC intensity, both at the initialization time and the targeted forecast hours, was strongly correlated with the TC intensity. On average, for both the WRF hindcasts and the NHC official forecasts, stronger intensities and larger intensity variations led to larger forecast errors. A number of synoptic-scale environmental parameters, such as vertical wind shear, sea surface temperature (SST), and the underlying surface condition (land vs sea), affected the intensity forecast errors of TCs, in part due to their influence on intensity changes, while other thermodynamic environmental parameters, such as moisture and instability, had relatively minor effects. The accuracy of the intensity prediction was also found to be sensitive to the translation speed of the TCs. A moderate TC translation speed of 11-15 knots (kt; 1 kt 5 0.51ms-1) corresponded to the largest intensity errors during forecast lead times less than 60 h, while the slowest translation speed (>7 kt) was associated with the largest errors after the 60-h forecast lead time.

Original languageEnglish (US)
Pages (from-to)1003-1023
Number of pages21
JournalWeather and Forecasting
Volume29
Issue number4
DOIs
StatePublished - Aug 2014

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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