Situation-dependent intensity skill metric and intensity spread guidance for western North Pacific tropical cyclones

Abstract

A situation-dependent intensity prediction (SDIP) technique is developed for western North Pacific tropical cyclones that is based on the average of the intensity changes from the 10 best historical track analogs to the Joint Typhoon Warning Center best-tracks. The selection of the 10 best track analogs is also conditioned on the current intensity, and it is demonstrated that for a subsample of current intensities less than or equal to 35 kt the intensity mean absolute errors (MAEs) and biases are smaller than for the greater than 35 kt intensity subsample. The SDIP is demonstrated to have advantages as an intensity skill measure at forecast intervals beyond 36 h compared to the current climatology and persistence technique that uses only variables available at the initial time. The SDIP has significantly smaller intensity MAEs beyond 36 h with an almost 20% reduction at 120 h, has significantly smaller intensity biases than the present skill metric beyond 12 h, and explains 36% of the intensity variability at 120 h compared to 20% explained variance for the current technique. The probability distributions of intensities at 72 h and 120 h predicted by the SDIP are also a better match of the distribution of the verifying observations. Intensity spread guidance each 12 h to 120 h is developed from the intensity spread among the 10 best historical track analogs. The intensity spread is calibrated to ensure that the SDIP forecasts will have a probability of detection (PoD) of at least 68.26%. While this calibrated intensity spread is specifically for the SDIP technique, it would provide a first-order spread guidance for the PoD for the official intensity forecast, which would be useful intensity uncertainty information for forecasters and decision-makers.