A Multi-Model Consensus Forecast Technique for Tropical Cyclone Intensity Based on Model Output Calibration

Abstract

ABSTRACT We analyzed the errors associated with forecasts of tropical cyclone (TC) intensity from 2010-2012 in the western North Pacific region made by seven operational numerical weather prediction models. The results show that the forecast error is significantly related to the initial error as well as the initial TC intensity, size, and translation speed. Other factors highly related to the forecast error include the environmental sea surface pressure, vertical wind shear and maximum potential intensity. We used stepwise regression to set up model forecast error estimation equations, which were used to calibrate the model output. Independent experiments showed that the calibrated model forecasts have significant skill compared to the original model output. Finally, a multimodel consensus forecast technique for TC intensity was developed based on the calibrated model output; this technique has 28% (15-20%) skill at 12 h (24-72 h) compared to the climatology and persistence forecasts of TC intensity. This consensus technique has greater skill than the consensus forecast based on the original model output and therefore it has the potential to be applied in operation.