Introduction

Synoptic weather systems, consisting both of extra-tropical cyclones and of anticyclones

have horizontal length scales that range from 1,000 to 6,000 km (King and Turner, 2007).

They drive a variety of hydrodynamic processes in the coastal ocean (Greatbatch, 1983;

Mattocks and Forbes, 2008), influencing both sediment dynamics (e.g., Stone et al.,

2004; Kineke et al., 2006) and ecosystem structures (e.g., Paerl et al., 2001; Peierls et al.,

2003). Damages from hurricanes are the most severe and have been widely studied from

a variety of perspectives (e.g., Hebert et al., 1996; Muller and Stone, 2001; Hall and

Sobel, 2013). Storm surges and the associated coastal floods and coastal erosion could

cause multi-levels of destructions to people and property. In addition, they also have

ecological impacts like the abrupt, pulse-like discharge of nutrient materials, enhancing

the potential of bottom water hypoxia and displacement of marine organisms (Paerl et al.,

2001; Piehler et al., 2004). Generally, hurricanes, although powerful and destructive in

nature, are relatively infrequent and their long-term effects on coastal zone evolution may

be of limited importance (Stone et al., 2004). However, smaller energetic storms,

occurring more frequently, impact the coastline continuously and their cumulative action

is believed to be an important driver of long term morphological evolution (Moeller et al.,

1993; Ferreira, 2005; Splinter et al., 2014), especially under a scenario of continuously

rising sea level.

Nearshore hydrodynamics are driven mainly by tidal and/or wind and wave forcings.

The deterministic nature of the barotropic tidal forcing makes it relatively easy to

quantify and be incorporated into numerical studies. On the contrary, wind variability is

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wind climate analysis, where wind data are analyzed in terms of wind speed and direction

(e.g., wind rose diagrams), describes statistically the variation of wind velocity within a

given period but it does not capture the time history of the processes. The time history is

particularly important for atmospheric events (storms) that exhibit some specific temporal

patterns both in terms of intensity and directionality, especially in areas like the inner

shelf, where a strong correlation between synoptic weather systems and meteorological

forcing has been documented (e.g., Austin and Lentz, 1999). Thus, an accurate

characterization of the storms associated with these synoptic weather systems will

potentially allow for a better evaluation of long-term storm-induced coastal impacts.

The cumulative effect of synoptic storms is important in sediment transport and

morphological evolution studies. For tide-dominated environments, long-term

morphological simulations are carried out using the concept of a morphological factor.

According to this concept, simulation results for a typical period (e.g., a tidal cycle, or a

neap - spring cycle) are extended forward in time through a time multiplication factor

(e.g., Lesser et al., 2004; Roelvink, 2006; Ganju et al., 2009; Ranasinghe et al., 2011).

This methodology, although efficient in reducing computational time, cannot be applied

in wind-driven environments due to a lack of deterministic characterization of the wind

forcing. Thus, there is a need for developing appropriate methodology that preserves the

internal, temporal variability of the events (speed and direction) as these determine the

ocean response in coastal regions. The overall impacts of storms can be evaluated by

quantifying the impacts of these representative storm events first, and then multiplying its

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Along the US east coast and in locations extending from the South Atlantic Bight

(SAB) to the coast of New York, three types of storms have been identified (Willett and

Sanders, 1959; Austin and Lentz, 1999; Warner et al., 2012): cold fronts (CF), warm

fronts (WF) and tropical storm (TS). Each type of storm is associated with a typical

weather map as described in Austin and Lentz (1999) and Warner et al. (2012), amongst

others. Development of CF and WF requires a strong polar jet stream and a discontinuity

(front) between air masses with different temperatures (Hoskins and West, 1979; Davis

and Dolan, 1993). Over the SAB, a CF is associated with a low-pressure system

travelling from west to east (or from southwest to northeast) over the area of interest. The

trailing cold front extends southward (southwestward) of the low-pressure center.

Similarly, WF events are associated with low-pressure systems propagating from west to

north and the trailing warm front passes over the area from south to north. Finally, TS is a

low pressure, warm-cored system that evolves from cyclogenesis (Willett and Sanders,

1959); it develops offshore and moves in a northward direction. It is worth noting that

extratropical low-pressure systems propagating from south to north and offshore of the

area of interest are included in our classification as TS, consistent with the classification

of Warner et al. (2012).

The South Carolina coast is a typical storm prone environment of the SAB. For

example, during the period 1958 – 2000, about 50% of the cyclogenesis occurrence along

the US east coast took place here (Bradbury et al., 2003). A number of experimental and

numerical studies have been undertaken to consider the influences of storm events on the

nearshore circulation and sediment dynamics over this area (i.e., Kumar et al., 2011;

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coastline depend on the storm type and that any long-term study of coastal evolution

requires a better representation of the storm forcing that captures its time history. In this

work, we use meteorological data recorded off South Carolina to develop a

climatological analysis method that identifies individual storm events and captures

information regarding their temporal variability.

The organization of the remainder of the chapter is as follows. In Section 2.2, we

develop and present an algorithm for storm classification. This is followed by an

assessment of the algorithm’s efficiency in Section 2.3. Section 2.4 describes the results

of the statistical analysis and lays out the characteristics of each type of storm, including

wind patterns and wave action. Verification of the analysis method utilizes a wave

generation/propagation model forced with the wind patterns identified. In Section 2.5 the

simulated wave characteristics are compared with the results of the wave statistical

analysis. Section 2.6 presents a discussion of further applications of the method while the

conclusions of the study are presented in Section 2.7.