A Physical Basis for the Probabilistic Prediction of the Accumulated Tropical Cyclone Kinetic Energy in the Western North Pacific
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- A Physical Basis for the Probabilistic Prediction of the Accumulated Tropical Cyclone Kinetic Energy in the Western North Pacific
- Kim, Hye-Mi; Lee, Myong-In; Webster, Peter J.; Kim, Dongmin; Yoo, Jin Ho
- Tropical cyclones; ENSO; Seasonal forecasting; Climate variability; Interannual variability
- Issue Date
- AMER METEOROLOGICAL SOC
- JOURNAL OF CLIMATE, v.26, no.20, pp.7981 - 7991
- The relationship between El Nino-Southern Oscillation (ENSO) andtropical storm (TS) activity over the western North Pacific Ocean is examined for the period from 1981 to 2010. In El Nino years, TS genesis locations are generally shifted to the southeast relative to normal years and the passages of TSs tend to recurve to the northeast. TSs of greater duration and more intensity during an El Nino summer induce an increase of the accumulated tropical cyclone kinetic energy (ACE). Based on the strong relationship between the TS properties and ENSO, a probabilistic predictionfor seasonal ACE is investigated using a hybrid dynamical-statistical model. A statistical relationship is developed between the observed ACE and largescale variables taken from the ECMWF seasonal forecast system 4 hindcasts.The ACE correlates positively with the SST anomaly over the central to eastern Pacific and negatively with the vertical wind shear near the date line.The vertical wind shear anomalies over the central and western Pacific are selected aspredictors based on sensitivity tests of ACE predictive skill. The hybrid model performs quite well in forecasting seasonal ACE with a correlation coefficient between the observed and predicted ACE at 0.80 over the30-yr period. A relative operating characteristic analysis also indicates that the ensembles have significant probabilistic skill for both the above-normal and below-normal categories. By comparing the ACE prediction over theperiod from 2003 to 2011, the hybrid model appears more skillful than the forecast from the Tropical Storm Risk consortium.
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