• Journal of the Korean earth science society
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• v.33 no.7
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• pp.583-595
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• 2012

The potential for tropical cyclogenesis in a given oceanic and atmospheric environments can be represented by genesis potential index (GPI). Using the 18 Coupled Model Inter Comparison Project phase 5 (CMIP5) models, the annual cycle of GPI and interannual variability of GPI are analyzed in this study. In comparison, the annual cycle of GPI calculated from reanalysis data is revisited. In particular, GPI differences between CMIP5 models and reanalysis data are compared, and the possible reasons for the GPI differences are discussed. ENSO (El Nino and Southern Oscillation) has a tropical phenomenon, which affects tropical cyclone genesis and its passages. Some dynamical interpretations of tropical cyclogenesis are suggested by using the fact that GPI is a function of four large-scale parameters. The GPI anomalies in El Nino or La Nina years are discussed and the most contributable factors are identified in this study. In addition, possible dynamics of tropical cyclogenesis in the Northern Hemisphere Pacific region are discussed using the large-scale factors.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.561-571
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• 2019

To understand the genesis of tropical cyclones (TC), we computed TC genesis probability (GPr) by partitioning a highly localized genesis frequency (GFq) into nearby grid boxes in proportion to the spatial coherence of genesis potential index (GPI). From the analysis of TCs simulated by the Seoul National University Atmosphere Model Version 0 and the observed TCs, it was shown that GPr reasonably converges to GFq when averaged over a long-term period in a decent grid size, supporting its validity as a proxy representing a true TC GPr. The composite anomalies of the gridded GPr in association with the Asia summer monsoon, El Nino-Southern Oscillation (ENSO), and the Madden-Julian Oscillation (MJO) are much less noisy than those of GFq, and consequently are better interpretable. In summary, GPr converges to GFq, varies more smoothly than GFq, represents the spatiotemporal variations of GFq better than GPI, and depicts GFq with greater spatial details than other spatially smoothed GFqs.

• Spatial Information Research
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• v.22 no.4
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• pp.49-58
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• 2014

Natural disasters of large scale such as typhoon, heat waves and snow storm have recently been increased because of climate change according to global warming which is most likely caused by greenhouse gas in the atmosphere. Increase of greenhouse gases concentration has caused the augmentation of earth's surface temperature, which raised the frequency of incidences of extreme weather in northern hemisphere. In this paper, we present spatial analysis of future typhoon genesis based on IPCC AR5 RCP 8.5 scenario, which applied latest carbon dioxide concentration trend. For this analysis, we firstly calculated GPI using RCP 8.5 monthly data during 1982~2100. By spatially comparing the monthly averaged GPIs and typhoon genesis locations of 1982~2010, a probability density distribution(PDF) of the typhoon genesis was estimated. Then, we defined 0.05GPI, 0.1GPI and 0.15GPI based on the GPI ranges which are corresponding to probability densities of 0.05, 0.1 and 0.15, respectively. Based on the PDF-related GPIs, spatial distributions of probability on the typhoon genesis were estimated for the periods of 1982~2010, 2011~2040, 2041~2070 and 2071~2100. Also, we analyzed area density using historical genesis points and spatial distributions. As the results, Philippines' east area corresponding to region of latitude $10^{\circ}{\sim}20^{\circ}$ shows high typhoon genesis probability in future. Using this result, we expect to estimate the potential region of typhoon genesis in the future and to develop the genesis model.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.454-464
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• 2010

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Ni$\tilde{n}$o-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.