**Author details**

Wen‐Zhou Zhang\* , Sheng Lin and Xue‐Min Jiang

\*Address all correspondence to: zwenzhou@xmu.edu.cn

Department of Physical Oceanography, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China

### **References**


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continental shelf waters, the biological responses are more complex because of pre‐existing upwelling, TC‐enhanced freshwater plume, riverine mixing, terrestrial runoff, and sediment

Since we focus on the influence of TCs in the WNP, some important aspects are not included here, such as the feedback of ocean to TCs. The accumulative effects of TCs on climate are not considered, either, because they are global and indirect, not limited to the WNP, in a long time scale. Regarding the complexity and extensiveness of ocean responses to TCs, some questions remain open and more observations and investigations are necessary to explore their answers

This work was jointly funded by the National Natural Science Foundation of China (Grants

41276007, U1305231 and 41076002) and the President Research Award (2013121047).

Department of Physical Oceanography, College of Ocean and Earth Sciences, Xiamen

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Wen‐Zhou Zhang\*

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#### **Diagnosing Tropical Cyclone Rapid Intensification Through Rotated Principal Component Analysis of Synoptic-Scale Diagnostic Fields Diagnosing Tropical Cyclone Rapid Intensification Through Rotated Principal Component Analysis of Synoptic-Scale Diagnostic Fields**

Alexandria Grimes and Andrew E. Mercer Additional information is available at the end of the chapter

Alexandria Grimes and Andrew E. Mercer

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/63988

#### **Abstract**

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DOI: 10.1016/j.ecss.2015.10.026

24 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

j.csr.2011.06.017

0137863

Forecasts of rapid intensification (RI) within tropical cyclones continue to be a major challenge, primarily due to difficulty in determining the processes that distinguish RI and non-RI storms. In this study, the aim was to identify the most important RI/non-RI discriminatory variables in the North Atlantic basin, not only by level, but also spatial location relative to the tropical cyclone center. These important variables, identified using rotated principal component analysis on one-dimensional and three-dimensional GEFS reforecast base-state variables from 1985 to 2009, led to the identification of diagnostic fields with the largest variability between RI and non-RI events. Hierarchical clustering techniques performed on rotated PC loadings provided map types of RI and non-RI cyclones. Analysis of these composite map types, as well as composite derived fields including divergence, relative vorticity, equivalent potential temperature, static stability, and vertical shear, revealed interesting distinguishing characteristics between RI and non-RI events. Results suggested that vorticity in the mid-levels, divergence in the upper-levels, equivalent potential temperature, and specific humidity play critical roles in successfully discriminating between RI and non-RI storms. These findings give key insights to which variables should be used in developing a prognostic classification scheme to assist with operational forecasts of tropical cyclone RI.

**Keywords:** tropical cyclone, rapid intensification, principal component analysis, cluster analysis, kinematic and thermodynamic tropical meteorology

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **1. Introduction**

Modern statistical and dynamic forecast models continue to demonstrate low forecast skill in identifying the onset of rapid intensification (hereafter known as RI) within tropical cyclones (hereafter known as TCs). Even though storms which rapidly intensify can cost governments billions of dollars in damage upon landfall (e.g. by destroying property through flooding–as with Katrina in 2005) and RI forecasting is considered one of the top priorities for the National Hurricane Center [1], little advancement has been made in improvement of probabilistic tropical cyclone RI forecasting. While previous studies have examined the intensification patterns between RI and non-RI TCs [2, 3], the technological impairments, coupled with the complexity of these systems, have left gaps in understanding the large-scale structures associated with RI storms [1–4]. These gaps result in poor statistical forecast model accuracy, which requires prior knowledge of relevant RI variables. While recent research shows modest improvements in RI forecasts, and global models have steadily improved in their ability to predict the large-scale environmental conditions of TCs [3], forecast skill scores still remain inadequate.

Current statistical forecast models blend both thermodynamic and kinematic variables in attempts to increase the skill, emphasizing meteorological processes deemed more crucial to RI prediction [1, 4–6]. Improvements to the Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII) continue to be added regularly since the original implementation by the National Hurricane Center (NHC) in 2004 for the North Atlantic [1]. The latest enhanced SHIPS-RII consists of 10 predictors, including previous 12-hour intensity change, vertical shear, divergence at 200 hPa, total precipitable water, GOES-IR imagery, potential intensity, oceanic heat content, max sustained wind, and an inner-core dry air predictor [1]. Despite the addition of new predictors, Brier skill scores (BSS) relative to climatology for Atlantic RI forecasts remain below 20% [1]. Additionally, verification of all operational consensus intensity forecast models for the NHC, including their official intensity forecast, showed only limited improvement as Peirce skill scores remained below 0.2 [1]. Other studies have included predictors that resolve the inner-core environment more effectively, utilizing microwave passive imagery predictors in a probabilistic logistic regression (LR) model. Despite this effort, BSS values only improved to roughly 22% with either simulated real-time LR models or LR models utilizing reanalysis data [4]. Additionally, using a baseline peak wind speed of 25 knot intensity (at all RI thresholds) severely reduces skill to below 15% when compared to a probabilistic LR model utilizing current SHIPS parameters previously developed in [7].

In order to improve statistical model prediction of the onset of TC RI, the ability to identify distinguishing meteorological characteristics of the storm structure between RI and non-RI TCs with 24 hours lead time is crucial. While research of this nature is not new [2, 3], the approaches have differed (e.g. data selection, data reduction, meteorological variables chosen, compositing approaches). For example, Kaplan and DeMaria [2] and Kaplan et al. [8] noted that RI was more likely to occur for TCs that were situated over regions of higher than average sea surface temperature (SST), strong upper-level divergence, large low- to mid-tropospheric moisture, and weaker than average vertical wind shear [2, 8]. Other research (see [3]) also observed that RI events occurred in environments with weaker deep-layer shear (as was found in [2, 8]) and greater conditional instability in the Atlantic basin than non-RI events. This research also noted that TCs moving over a warm ocean anomaly were found to be equally likely to intensify slowly or rapidly given other assumptions are met [3], a result in contrast with the work shown in [2, 8]. While recent research suggests environmental, internal dynamic processes, and oceanic conditions [2, 3, 8] all play a role in RI, research performed in [3] concluded RI is mostly controlled by internal dynamical processes, provided a pre-existing favorable environment exists. Research performed in [4] reiterated this sentiment suggesting Atlantic basin forecasts benefit more from the inclusion of storm structure information (more than the Pacific basins), which has yet to be explained.

In an effort to continue improving the understanding of the internal dynamics of TCs undergoing RI, the current study sought to identify important diagnostic variables in the North Atlantic basin, looking not only at which levels, but also at which spatial points in proximity to the cyclone are distinguishable between the two types of systems. The primary research question being considered is: What meteorological parameters discriminate RI from non-RI storms most effectively, and what spatial location in the TC domain provide the largest differences in these fields? These findings give key insights to which variables should be used in future development of a prognostic artificial intelligence classification scheme to assist with operational forecasts of RI. Section 2 provides a description of the data and methodology, while Section 3 presents the results of the work and Section 4 provides a discussion and conclusions.
