Discussion

2.6.1 Efficiency of the identification algorithm

In this work, inspection of the daily weather maps is applied to remove the false

detections of the algorithm and add the missed detections. Here we further examine the

efficiency of the algorithm by comparing the storm climatology calculated from the

uncorrected detections of the algorithm with that created using the corrected ones. Figure

2.15 shows the results of the representative wind velocity at onshore station (B1) and the

9-year averaged wave bulk parameters (i.e., significant wave height and mean wave

direction) at site P0 for both cases. Consistently with our previous findings, the

representative wind velocity is defined by the mean plus one std for frontal events and the

mean value for TS. The results show that, the storm climatology of frontal events is

satisfactorily reproduced by even using the uncorrected detections as derived by the

algorithm. The rms errors of wind speed are 0.26 and 0.41 m/s, while the rms errors of

Hsig are 0.07 and 0.08 m for the CF and WF events, respectively (see Table 2.7). The

results become less accurate for the TS events, as the rms errors of wind speed and Hsig

increases to 0.55 m/s and 0.25 m, respectively. In terms of percentage of occurrence,

analysis of the algorithm derived, uncorrected events results in 25.6%, 16.6% and 23.3%

for the CF, WF and TS events, respectively. These values are close to those found using

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WF, respectively) but overestimate the occurrence of TS (9.9%, see Figure 2.4). The

mean durations of all the events identified by the algorithm are 77, 83 and 86 h for the

CF, WF and TS events, respectively. The differences from the durations estimated from

the weather maps are within 10% for the CF and WF, and 16% for the TS events.

Overall, the algorithm can be used directly to identify CF and WF from the

meteorological records and can provide an accurate climatology for the fronts, despite of

the false and missed detections. Reducing the number of false detections for the

identification of TS events is more challenging.

2.6.2 Accuracy of wind stress averaging process

The analysis presented here provides a methodology for expressing individual storm type

climatology for a location while maintaining the time evolution of the events. Our

analysis has shown that the method, when used for forcing numerical models, requires

adjustment depending on the storm type. As presented earlier, the best agreement is

obtained when the wind velocity is assigned the mean value plus one std for frontal

events, and the corresponding mean value for TS. Such difference may be attributed to

the fact that the averages of wind stress are obtained following a duration normalization

process using the corresponding developing and decaying event durations. The temporal

distribution of wind speed may be skewed either positively or negatively, which can lead

to an underestimation of the mean maximum speed as individual maximum values are

distributed over different normalized time steps. Moreover, by neglecting the duration

variation, storm events with relatively strong wind stress may be underrepresented in the

averaging process as they tend to be longer in duration. Certainly, the method presented

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averaging the wind stress, so that the events of longer duration contribute more to the

final averaged wind stress. In the absence of such scaling, the increase of the mean value

by one std, which corresponds to the 84.1% percentile of the recorded wind (assuming a

normal distribution), can provide the desired results. In the case of TS, the temporal

distribution of the wind stress may be less skewed (see Figure 2.2 for an example) and

the variation of storm duration does not appear to affect the averaging process too much,

as relatively strong wind may still take place during a shorter event.

It should be noted that the mean conditions were estimated using 287 and 155 CF

and WF events, respectively (see Table 2.2); a value corresponding to one standard

deviation of the wind stress was added to the mean to establish the representative storm

condition. Although the number of frontal events is adequate for the averaging process,

for the reconstruction of the TS event, 119 individual events were considered. This lower

number of events, might reduce slightly the confidence in the averaging process but not

significantly, especially when compared with the number of events used for WF. Overall,

the 10-year mean wind patterns proposed should be representative for the study site, and

are not limited to the specific period. For the application of our method to other areas,

further investigation is required to check whether the inconsistency among the three types

of storms is specific in site or not.

2.6.3 Method application

In this study, we applied the method to demonstrate its application to long term studies by

quantifying the net annual wave energy flux. This was achieved by multiplying the

number of events per year of each storm type by the wave power obtained from the

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intensity index (i.e., Splinter et al., 2014), to some extent analogous to the scale system

introduced by Davis and Dolan (1993) and Butman et al. (2008). For the three storm

types, when examined per individual event, TS is the most intense; but annually the

frontal events provide comparable or even exceeding values of wave power. These

findings indicate that for long-term studies, in wind-dominated environments,

incorporation of the frontal events is as important as the tropical storms.

As shown in Section 2.4.1, the occurrence of three types of storms account for 53.5%

of the total period, while the remaining period is dominated by northeastward winds (see

Figure 2.4d) due to the strengthening of Azores high during the summer. By using the

same wave data from the WWIII simulations, we calculate the 10-year total wave power

at site P0 (see Figure 2.1 for the location). The results find that, 67.0% of the total wave

power is associated with the storm events, with a contribution of 28.3%, 17.9% and

20.8% from CF, WF and TS respectively. For the period that experiences relatively

energetic wave actions (defined as Hsig > 2 m at site P0), which accounts for 21.0% of the

total period, 75.0% of the total wave power is associated with the storm events. Thus,

application of our method at the study area could approximately capture 2/3 of the total

wave power, or 3/4 of the wave power under energetic wave conditions. To fully capture

the total wave power, incorporation of the periods not associated to storm events is

required.