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This review summarizes techniques used by operational centers to forecast tropical cyclone intensity change as presented to the International Workshop on Tropical Cyclones (IWTC-9) in Hawaii in 2018. Recent advances and major changes over the past four years are presented, with a special focus on forecasting rapid intensity changes. Although intensity change remains one of the most difficult aspects of tropical cyclone forecasting, objective guidance has shown some improvement. The greatest improvements are realized when consensus methods are utilized, especially those that blend statistical-dynamical based guidance with dynamical ocean-coupled regional models. These models become even more skillful when initialized with inner core observational data. Continued improvement and availability of intensity guidance along with associated forecaster training are expected to deliver forecasting improvements in the future. Keywords: tropical cyclone, intensity, change, rapid intensification

A Selected Multi-model CONsensus (SMCON) technique was developed and verified for the tropical cyclone track forecast in the western North Pacific. The SMCON forecasts were produced by averaging numerical model forecasts showing low 70% latest 6 h prediction errors among 21 models. In the homogeneous comparison for 54 tropical cyclones in 2013 and 2014, the SMCON improvement rate was higher than the other forecasts such as the Non-Selected Multi-model CONsensus (NSMCON) and other numerical models (i.e., GDAPS, GEPS, GFS, HWRF, ECMWF, ECMWF_H, ECMWF_EPS, JGSM, TEPS). However, the SMCON showed lower or similar improvement rate than a few forecasts including ECMWF_EPS forecasts at 96 h in 2013 and at 72 h in 2014 and the TEPS forecast at 120 h in 2013. Mean track errors of the SMCON for two year were smaller than the NSMCON and these differences were 0.4, 1.2, 5.9, 12.9, 8.2 km at 24-, 48-, 72-, 96-, 120-h respectively. The SMCON error distributions showed smaller central tendency than the NSMCON`s except 72-, 96-h forecasts in 2013. Similarly, the density for smaller track errors of the SMCON was higher than the NSMCON`s except at 72-, 96-h forecast in 2013 in the kernel density estimation analysis. In addition, the NSMCON has lager range of errors above the third quantile and larger standard deviation than the SMCON`s at 72-, 96-h forecasts in 2013. Also, the SMCON showed smaller bias than ECMWF_H for the cross track bias. Thus, we concluded that the SMCON could provide more reliable information on the tropical cyclone track forecast by reflecting the real-time performance of the numerical models.

In this study a superensemble was constructed and assessed to examine its applicability to the tropical cyclone track forecasts in the western North Pacific. The data used for this study were outputs of 20 tropical cyclone forecast models and analyzed tropical cyclone tracks by the Korea Meteorological Administration from 2011 to 2013. The annual mean track errors were analyzed at the 24-, 48-, 72-, 96- and 120-h periods for 2012 and 2013, and the superensemble forecasts showed lower annual errors than the simple mean consensus (using 20 numerical models), ECMWF_TIGG, and GFS. The superensemble track errors for individual tropical cyclone cases were lower than the simple mean consensus over 60% of the total cases, and lower than the best-performing model over 50% of the total cases for the 24-, 48-, and 72-h forecast periods. In the track error distribution, the superensemble had lower density for relatively large errors than the simple mean consensus, and higher density for smaller errors than single models. When the results are combined, the probability of the superensemble yielding lower errors than the simple mean consensus and single models becomes high, which indicates that the superensemble can serve as an objective reference for the tropical cyclone track forecasts.

Evaluation of predictability of numerical models for tropical cyclone track was performed using along-and cross-track component. The along-and cross-track bias were useful indicators that show the numerical models predictability associated with cause of errors. Since forecast errors, standard deviation and consistency index of along-track component were greater than those of cross-track component, there was some rooms for improvement in alongtrack component. There was an overall slow bias. The most accurate model was JGSM for 24hour forecast and ECMWF for 48~96-hour forecast in direct position error, along-track error and cross-track error. ECMWF and GFS had a high variability for 24-hour forecast. The results of predictability by track type showed that most significant errors of tropical cyclone track forecast were caused by the failure to estimate the recurvature phenomenon.

Typhoon Sanba was selected for describing the Korea Meteorological Administration (KMA) Global Data Assimilation Prediction System (GDAPS) model bias tendency in forecast for the interaction between mid-latitude trough and movement speed of typhoon. We used the KMA GDAPS analyses and forecasts initiated 00 UTC 15 September 2012 from the historical typhoon record using Typhoon Analysis and Prediction System (TAPS) and Combined Meteorological Information System-3 (COMIS-3). Sea level pressure fields illustrated a development of the low level mid-latitude cyclogenesis in relation to Jet Maximum at 500 hPa. The study found that after Sanba entered the mid-latitude domain, its movement speed was forecast to be accelerated. Typically, Snaba interacted with mid-latitude westerlies at the front of mid-latitude trough. This event occurred when the Sanba was nearing recurvature at 00 and 06 UTC 17 September. The KMA GDAPS sea level pressure forecasts provided the low level mid-latitude cyclone that was weaker than what it actually analyzed in field. As a result, the mid-latitude circulations affecting on Sanba`s movement speed was slower than what the KMA GDAPS actually analyzed in field. It was found that these circulations occurred due to the weak mid-tropospheric jet maximum at the 500 hPa. In conclusion, the KMA GDAPS forecast tends to slow a bias of slow movement speed when Sanba interacted with the mid-latitude trough.

This study introduces an unequally weighted technique for Multi-model Ensemble (MME) forecasting for western North Pacific Tropical Cyclone (TC) tracks. Weights are calculated by partial least square regression, and members are selected by paired t-test. The performances for shorter forecast time ranges, such as 24, 48 and 72 h, are examined in order to improve the MME model, in which the weights for members are equally assigned. For longer forecast time ranges, such as 96 and 120 h, weights for MME members are thought to be less reliable, since the modeling is more likely to be influenced by the climate variability in the data period. A combination of both techniques for the shorter and the longer forecast time ranges is suggested as an alternative MME forecast procedure in operational meteorological agencies.

[1] This paper presents a statistical model to forecast seasonal tropical cyclone activity. In order to give a comprehensive view of seasonal tropical cyclone activity, we include not only the number of total tropical cyclones but also the number of typhoons and the NTA (Normalized Typhoon Activity) index as the predictands. The model is based on a multiple linear regression model in which the final predictors are selected with respect to minimizing the prediction error rather than simply fitting with past data. The model is expanded into ensemble prediction by considering the uncertainty of the single and deterministic forecast. The probability forecast based on the ensemble model shows reasonably good skill with respect to reliability and relative operating characteristics.