• Atmosphere
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• v.21 no.2
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• pp.209-220
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• 2011

A new consensus algorithm for the prediction of tropical cyclone track has been developed. Conventional consensus is a simple average of a few fixed models that showed the good performance in track prediction for the past few years. Meanwhile, the consensus in this study is a weighted average of a few models that may change for every individual forecast time. The models are selected as follows. The first step is to find the analogous past tropical cyclone tracks to the current track. The next step is to evaluate the model performances for those past tracks. Finally, we take the weighted average of the selected models. More weight is given to the higher performance model. This new algorithm has been named as DYTRAP (DYnamic data-base Typhoon tRAck Prediction) in the sense that the data base is used to find the analogous past tracks and the effective models for every individual track prediction case. DYTRAP has been applied to all 2009 tropical cyclone track prediction. The results outperforms those of all models as well as all the official forecasts of the typhoon centers. In order to prove the real usefulness of DYTRAP, it is necessary to apply the DYTRAP system to the real time prediction because the forecast in typhoon centers usually uses 6-hour or 12-hour-old model guidances.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.241-253
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• 2015

In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

• Journal of Navigation and Port Research
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• v.29 no.4
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• pp.299-304
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• 2005

A simulation system is needed to train students and mariners in order that they can take suitable actions to evade typhoon's strike promptly and sufficiently. In order to make such kind of system, three kinds of models about the typhoon are necessary, typhoon prediction model to generate typhoon's track, wind & wave-field model to make sea conditions around the typhoon and evaluation model of trainee's action whether their actions were suitable or not during simulation. We have developed the prediction and wind & wave-field models of typhoon, but the evaluation model has not been developed yet. In this paper, after making a method for evaluating trainee's actions by seakeeping performance, we propose an typhoon avoidance simulation system for training mariners so that they can promote their abilities to evade the typhoons at sea.

• Atmosphere
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• v.27 no.1
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• pp.17-28
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• 2017

The sensitivity of the typhoon track and intensity simulation to physics schemes of the global model are examined for the typhoon Bolaven and Tembin cases by using the Global/Regional Integrated Model System-Global Model Program (GRIMs-GMP) with the physics package version 2.0 of the Korea Institute of Atmospheric Prediction Systems. Microphysics, Cloudiness, and Planetary boundary Layer (PBL) parameterizations are changed and the impact of each scheme change to typhoon simulation is compared with the control simulation and observation. It is found that change of microphysics scheme from WRF Single-Moment 5-class (WSM5) to 1-class (WSM1) affects to the typhoon simulation significantly, showing the intensified typhoon activity and increased precipitation amount, while the effect of the prognostic cloudiness and PBL enhanced mixing scheme is not noticeable. It appears that WSM1 simulates relatively unstable and drier atmospheric structure than WSM5, which is induced by the latent heat change and the associated radiative effect due to not considering ice cloud. And WSM1 results the enhanced typhoon intensity and heavy rainfall simulation. It suggests that the microphysics is important to improve the capability for typhoon simulation of a global model and to increase the predictability of medium range forecast.

• Atmosphere
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• v.26 no.1
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• pp.1-18
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• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.127-131
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• 1999

Typhoons were known by contributing to transporting plus heat or kinetic energy from equatorial region to midlatitude region. Due to the strong damage from typhoon, we acknowledged the theoretical study and the importance of accurate forecast about typhoon. In this study, typhoon forecasting system was developed to search the tracks of past typhoons or to display similar track of past typhoon in comparison with the path of current forecasting typhoon. It was programmed using Interactive Data Language(IDL), which was a complete computing environment for the interactive analysis and visualization of data. Typhoon forecasting system was also included satellite image and auxiliary chart. IR, Water Vapor, Visible satellite images helped users analyze an accurate forecast of typhoon. They were further refined the procedures for generating water vapor winds and gave an initial indication of their utility for numerical weather prediction(NWP), in particular for typhoon track forecasting where they could provide important information. They were also available for its utility in typhoon tracer or intensity.

• Atmosphere
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• v.17 no.2
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• pp.207-216
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• 2007

The Moving-nest Typhoon Model (MTM) was installed on the Korea Meteorological Administration (KMA)'s CRAY X1E in 2006 and started its test operation in August 2006 to provide track and intensity forecasts of tropical cyclones. In this study, feasibility of the MTM forecast is compared with the Global Data Assimilation and Prediction System (GDAPS) of the KMA and the operational typhoon forecast models in the Japan Meteorological Agency (JMA), from the sixth tropical cyclone to the twentieth in 2006. Forecast skills in terms of the storm position error of the two KMA models were comparable, but MTM showed a slightly better ability. While both GDAPS and MTM produced larger errors than JMA models in track forecast, the predicted intensity was much improved by MTM, making it comparable to the JMA's typhoon forecast model. It is believed that the Geophysical Fluid Dynamics Laboratory (GFDL) bogus initialization method in MTM improves the ability to forecast typhoon intensity.

• Journal of the Korean earth science society
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• v.35 no.3
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• pp.168-179
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• 2014

The purpose of this study focuses on the prediction time and location of turning-point of typhoon tracks using the water vapor images of Communication, Ocean and Meteorological Satellite (COMS) which has a very short observation interval. It targets a more accurate prediction of turning-point of typhoon tracks through the relationship between dry slot and northern/southern oscillations of jet stream. Jet stream moves by the position of jet streak and the ${\upsilon}$-component velocity of geostrophic wind. If the ${\upsilon}$-component of geostrophic wind gets stronger toward south, jet stream develops into a circular jet. In that condition, dry slot in satellite water vapor imagery extends toward south, and typhoon track turns as the distance of curved moisture band (CMB) gets narrowed down. If the interval of CMB is below $15^{\circ}$ of latitude, the typhoon track is turning toward north or northeast within 24 hours. As a result, typhoon track showed that when dry slot position was located less than $32^{\circ}N$, typhoon turned its track at $20-23^{\circ}N$ ($1^{th}$ Kong-Rey 2007 and $17^{th}$ Jelawt at 2012), and when in $35^{\circ}N$ above, it turned at $27^{\circ}N$ ($4^{th}$ Man-yi 2007).

• Atmosphere
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• v.28 no.3
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• pp.263-272
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• 2018

The high-resolution ocean surface wind vector produced by scatterometer was assimilated within the Local Ensemble Transform Kalman Filter (LETKF) in Korea Institute of Atmospheric Prediction Systems (KIAPS). The Advanced Scatterometer (ASCAT) on Metop-A/B wind data was processed in the KIAPS Package for Observation Processing (KPOP), and a module capable of processing surface wind observation was implemented in the LETKF system. The LETKF data assimilation cycle for evaluating the performance improvement due to ASCAT observation was carried out for approximately 20 days from June through July 2017 when Typhoon Nepartak was present. As a result, we have found that the performance of ASCAT wind vector has a clear and beneficial effect on the data assimilation cycle. It has reduced analysis errors of wind, temperature, and humidity, as well as analysis errors of lower troposphere wind. Furthermore, by the assimilation of the ASCAT wind observation, the initial condition of the model described the typhoon structure more accurately and improved the typhoon track prediction skill. Therefore, we can expect the analysis field of LETKF will be improved if the Scatterometer wind observation is added.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).