High-Resolution Tropical Channel Model Simulations of Tropical Cyclone Climatology and Intraseasonal-to-Interannual Variability Academic Article uri icon


  • Abstract We tailored a tropical channel configuration of the Weather Research and Forecasting (WRF) Model to study tropical cyclone (TC) activity and associated climate variabilities. This tropical channel model (TCM) covers from 30S to 50N at 27-km horizontal resolution, with physics parameterizations carefully selected to achieve more realistic simulations of TCs and large-scale climate mean states. We performed 15-member ensembles of retrospective simulations from 1982 to 2016 hurricane seasons. A thorough comparison with observations demonstrates that the TCM yields significant skills in simulating TC activity climatology and variabilities in each basin, as well as TC physical structures. The correlation of the ensemble averaged accumulated cyclone energy (ACE) with observations in the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NAT) is 0.80, 0.64, and 0.61, respectively, but is insignificant in the north Indian Ocean (NIO). Moreover, the TCM-simulated modulations of El NioSouthern Oscillation (ENSO) and the MaddenJulian oscillation (MJO) on the large-scale environment and TC genesis also agree well with observations. To examine the TCMs potential for seasonal TC prediction, the model is used to forecast the 2017 and 2018 hurricane seasons, using bias-corrected sea surface temperatures (SSTs) from the CFSv2 seasonal prediction results. The TCM accurately predicts the hyperactive 2017 NAT hurricane season and near-normal WNP and ENP hurricane seasons when initialized in May. In addition, the TCM accurately predicts TC activity in the NAT and WNP during the 2018 season, but underpredicts ENP TC activity, in association with a poor ENSO forecast.

published proceedings


altmetric score

  • 2.25

author list (cited authors)

  • Fu, D., Chang, P., Patricola, C. M., & Saravanan, R.

citation count

  • 5

complete list of authors

  • Fu, Dan||Chang, Ping||Patricola, Christina M||Saravanan, R

publication date

  • January 1, 2019 11:11 AM