Discrepancies on Storm Surge Predictions by Parametric Wind Model and Numerical Weather Prediction Model in a Semi-Enclosed Bay: Case Study of Typhoon Haiyan

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Article Discrepancies on Storm Surge Predictions by Parametric Wind Model and Numerical Weather Prediction Model in a Semi-Enclosed Bay: Case Study of Typhoon Haiyan

Yu-Lin Tsai 1, Tso-Ren Wu 1,* , Chuan-Yao Lin 2 , Simon C. Lin 3, Eric Yen 3 and Chun-Wei Lin 1

1 Graduate Institute of Hydrological and Oceanic Sciences, National Central University, Chungli 32001, Taiwan; [email protected] (Y.-L.T.); [email protected] (C.-W.L.) 2 Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan; [email protected] 3 Institute of Physics, Academia Sinica, Taipei 11529, Taiwan; [email protected] (S.C.L.); [email protected] (E.Y.) * Correspondence: [email protected]

Received: 11 October 2020; Accepted: 24 November 2020; Published: 26 November 2020

Abstract: This study explores the discrepancies of storm surge predictions driven by the parametric wind model and the numerical weather prediction model. Serving as a leading-order storm wind predictive tool, the parametric Holland wind model provides the frictional-free, steady-state, and geostrophic-balancing solutions. On the other hand, WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) provides the results solving the 3D time-integrated, compressible, and non-hydrostatic Euler equations, but time-consuming. To shed light on their discrepancies for storm surge predictions, the storm surges of 2013 Typhoon Haiyan in the Leyte Gulf and the San Pedro Bay are selected. The Holland wind model predicts strong southeastern winds in the San Pedro Bay after Haiyan makes landfall at the Leyte Island than WRF-ARW 3 km and WRF-ARW 1 km. The storm surge simulation driven by the Holland wind model finds that the water piles up in the San Pedro Bay and its maximum computed storm surges are almost twice than those driven by WRF-ARW. This study also finds that the storm surge prediction in the San Pedro Bay is sensitive to winds, which can be affected by the landfall location, the storm intensity, and the storm forward speed. The numerical experiment points out that the maximum storm surges can be amplified by more 5–6% inside the San Pedro Bay if Haiyan’s forward speed is increased by 10%.

Keywords: Holland wind model; WRF-ARW; linear shallow water equation; 2013 Typhoon Haiyan

1. Introduction Storm surges caused by tropical storms can have a significant impact on coastal facilities [1]. In some cases, storm surges can interact with tides [2,3] and wind waves [4–6]. When a storm approaches coastal regions, winds can amplify the height of storm surges locally at least two times more than that in the open ocean [5,6]. Hence, to perform an accurate storm surge modeling, having a better understanding of storm-wind fields is critical. In the Western Pacific Ocean, more intense typhoons potentially occur in the future and the low-lying regions become more vulnerable to storm surges [7,8]. To minimize the disaster of storm surges, the accuracy of predicting storm surges needs to be improved. The uncertainties of storm surge predictions can attribute to the input data (e.g., bathymetry and winds), physics consideration (coupling with waves, tides, river flows, etc.), and numerical settings (e.g., grid size, time step, and an

Water 2020, 12, 3326; doi:10.3390/w12123326 www.mdpi.com/journal/water Water 2020, 12, 3326 2 of 27 algorithm of the model discretization). However, the most variable factor should be storm wind, which accuracy depends on the storm track, the storm intensity, and the forward speed of a storm [9]. Storm-wind fields can be acquired from either a numerical weather prediction (NWP) model or a parametric wind (PW) model to drive storm surge models. The NWP models such as WRF (weather research and forecasting [10]), CReSS (cloud resolving storm simulator [11]), and ECMWF (European Centre for Medium-Range Weather Forecasts [12]), are widely used in storm surge modeling (see, e.g., [13]). NWP models solve the 3D time-integrated, compressible, and non-hydrostatic Euler equations and consider more complicated physical mechanisms (e.g., the land topography effect). However, NWP models become extremely time-consuming once a finer grid or a larger domain is required. Besides, the grid size of NWP models is vital in the tropical storm modeling [14,15]. Besides NWP models, PW models provide other standard options in storm surge forecasting [16,17], hindcasting [18–20], or flood risk assessment [21]. Some conventional PW models are the modified Rankine vortex model [22], SLOSH (Sea, Lake, and Overland Surges from Hurricanes) wind model [23], and Holland wind model [24]. Storm pressure and wind fields are conveniently reconstructed by the parameters: locations of storm center, the central pressure of a storm, the maximum wind speed, and the radius of maximum wind [24–26]. Hence, the accuracy of reconstructed parametric storm wind fields is highly dependent on these input parameters [18,27]. Furthermore, by considering a forward storm motion, the symmetric shape of storm winds can be modified [23]. However, PW models neglect physical mechanisms such as storm-topography interactions and assume to be frictional-free. Compared to NWP models, PW models are economical solutions and can serve as first-order storm wind predictive tools. Since both NWP and PW models have their own limitations to reconstruct storm-wind fields, the trade-off solutions, hybrid-type storm wind fields, are used for storm surge [28] and wind wave modeling [29]. Storm wind fields can be acquired by combing the winds from NWP and PW models using some coefficients. However, these coefficients need to be tested and are not guaranteed to be appropriate for every typhoons’ situation. This solution is beyond the scope of this study. Despite the discrepancies of storm surge modeling using different PW models has been widely discussed [9,27,30], the differences of storm surge predictions between PW and NWP models are relatively less explored, especially in the Western Pacific Ocean. Hence, for the storm surge modeling in the Western Pacific Ocean, understanding the differences resulted from the wind fields of the PW and NWP models is necessary. To shed more light upon the discrepancies of storm surge modeling driven by NWP and PW models, a case study of Typhoon Haiyan is considered. Typhoon Haiyan, also known as its local name Yolanda, was the strongest storm in 2013, struck the Philippines with catastrophic storm surges, winds, and waves, and caused casualties more than 6300 [31]. The event of Typhoon Haiyan has three uniqueness: (1) the record-breaking wind speed; (2) the fast storm’s forward motion; and (3) the notable induced storm surges. First of all, Haiyan has a record-breaking intensity, sustained wind speed of more than 310 km/h [7]. Haiyan first made landfall at the southeast of the Samar Island at 4:40 a.m. 8 November 2013 (local Philippines time) with its peak intensity, made the second landfall at Dulag, Leyte at around 7:00 a.m. 8 November 2013 (local Philippines time), and then passed over the Leyte Island and the Cebu Island. Secondly, Haiyan’s forward speed can reach up to 41.0 km/h, which was more than twice over the average forward speed of tropical cyclones in the mid-latitude regions from 1951 to 2012 [32]. Lastly, because of the storm track and the landfall location of Haiyan, the most damaged regions by storm surges were found in the Leyte Gulf and the San Pedro Bay [33–36]. This uniqueness makes Typhoon Haiyan become a good case study for highlighting the discrepancies of storm surge predictions by storm winds in the Western Pacific Ocean. Several studies have analyzed Haiyan’s storm surges numerically. The peak frequency of storm surges caused by Haiyan is comparable to the first mode of the seiche oscillation in the Leyte Gulf, which can amplify the surge heights [13]. The wind drag coefficient and the radius of maximum wind (RMW) are sensitive to the storm surge prediction using a surge-wave coupling model [18]. Water 2020, 12, 3326 3 of 27

Three sequences of storm surges hit Tacloban in the San Pedro Bay are indicated, which correspond to the eyewitness report [35]. By using ensemble simulations with regional climate models, storm surges in the Leyte Gulf caused by the worst-case scenario can potentially be worse by 20% [37]. The storm forward speed and the storm track have been proved that they are essential to generate significant storm surges in the San Pedro Bay [19,38]. These studies pave the way on exploring the storm surges caused by Haiyan from different points of view. The shallow water equations (SWE) model has been used to simulate storm surges successfully from open ocean to coastal regions [2,5,6,30,39]. By integrating flow properties from the sea bottom to the water surface, the depth-averaged flow velocities and free surface elevations are solved in the assumption of long wave. By neglecting the advection terms in the momentum conservations [23,40], the linear shallow water equations (LSWE) model such as SLOSH [23] becomes an economic tool in predicting storm surges. Besides traditional SWE models, some sophisticated numerical models such as non-hydrostatic wave models [20,41] can be used in the storm surge modeling as well. However, they may be more time-consuming than traditional SWE models if similar spatial resolutions are required. Moreover, LSWE models have been used to study Haiyan’s storm surges [42,43]. By considering the balance between efficiency and accuracy, the LSWE storm surge model will be used to predict storm surges induced by Typhoon Haiyan in this study. In this study, by using the storm surge model solving LSWE, the discussions focus on the storm surges in the Leyte Gulf and the San Pedro Bay in the Philippines, which were extremely damaged by Haiyan. Both PW and NWP models are used to drive the storm surge model. The PW model results are from the widely-used Holland wind model [24]. The well-known Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model provides the results of the NWP model. This paper is organized as follows. In Section2, we first present the storm surge model used in this study. Then, the parametric Holland model and the WRF-ARW model are elaborated. In Section3, the discrepancies in the modeling of Haiyan between the parametric Holland model and WRF-ARW are discussed. Afterward, the numerical experiments focused on a storm forward motion is carried out. In Section4, we conclude this study and give some suggestions for future studies.

2. Methodology

2.1. Storm Surge Model In this study, the depth-integrated storm surge model solving the set of linear shallow water equations with the Coriolis eﬀect and the bottom friction is adopted. This storm surge model is based on the COMCOT (COrnell Multi-grid COupled Tsunami) tsunami model. COMCOT has been validated by the solitary wave run-up benchmark problem [44] and been used to study tsunami events such as 1960 Chilean Tsunami [45], 2004 Indian Ocean Tsunami [46,47] and 2011 Japan Tohoku Tsunami [48]. Furthermore, COMCOT has been developed into a cloud computing platform [48]. The external forcing terms of the sea-level pressures and the wind shear stresses are added to the momentum equations of COMCOT to simulate the storm surge propagation. The mass and momentum equations of the linear shallow water equations in the Cartesian coordinate are presented as follows:

∂η ∂P ∂Q + + = 0, (1) ∂t ∂x ∂y

∂P ∂η H ∂P τ τ f Q = gH a + sx bx , (2) ∂t − − ∂x − ρw ∂x ρw − ρw

∂Q ∂η H ∂P τsy τby + f P = gH a + , (3) ∂t − ∂y − ρw ∂y ρw − ρw Water 2020, 12, 3326 4 of 27 where η is the water surface elevation, t is the time, x,y are spatial notations, (P,Q) are the depth-integrated volume fluxes per unit length, H is the total water depth (H = h + η), h is the still water depth, g is the gravitational acceleration (=9.81 m/s2), f is the Coriolis parameter ( f = 2ωsinϕ), 5 ω is the Earth angular velocity (=7.2921 10− rad/s), (τsx, τsy) are the wind shear stresses, (τbx, τby) × 3 are the bottom frictional shear stresses, ρw is the water density (=1000 kg/m ), and Pa is the sea-level air pressure. The Manning’s formula, originally from the conception of the open-channel flow, is used to model the bottom friction: gn2 q 2 2 τbx = ρw P (P + Q ), (4) H7/3 gn2 q 2 2 τby = ρw Q (P + Q ), (5) H7/3 where n is the Manning’s roughness coefficient. By introducing appropriate Manning’s coefficient, the roughness of the material can be determined in this formula. As shown in Equations (4) and (5), the bottom friction is proportional to the squared of volume fluxes per unit length and the Manning’s coefficient and inversely proportional to the water depth to the power of 7/3. Hence, the bottom friction using this formula is less important in deep waters, more significant in shallow waters, and crucial in coastal regions (including inland areas). Since the inundation calculation is not involved in this study, the Manning’s coefficient is 0.025 over the whole computational domain, which is also adopted by other studies [18,19]. In fact, the Manning’s coefficient shall be determined carefully, especially the inundation calculation involved. The use of the Manning’s roughness coefficient to storm surge modeling has been investigated [49–51]. The quadric law is used to model the wind shear stresses on the water surface:

τsx = ρaCdu10u10x, (6)

τsy = ρaCdu10u10y, (7) in which Cd is the wind drag coefficient between the water surface and the air, u10 is the 10-m wind speed, (u10x, u10y) are the components of the 10-m wind speed in the x- and y-directions. The wind drag coefficient of Wu (1982) is used [52]: ( 1.2875 10 3, u < 7.5, C = × − 10 (8) d (0.8 + 0.065) 10 3, u 7.5. × − 10 ≥ The boundary conditions along the computational domain are given by the free surface elevations caused by the pressure drop (i.e., the difference between the sea-level pressure and the ambient pressure) in wet cells: ∆p η = . (9) − ρg This storm surge model is discretized by the explicit finite difference method in time and space. The leap-frog scheme is used to discretize the free surface elevation at (n + 1/2)∆t and the volume fluxes per unit length at (n + 1)∆t [53,54]. The forward finite difference method is used to solve the mass equation and the momentum equations. The staggered grid system, Arakawa C grid, is used [55]. Hence, the volume fluxes are defined at the boundaries of cells and the free surface elevations are in the center of cells. The boundary conditions between wet and dry cells are non-flux boundary conditions. It implies the volume fluxes of water cannot go across the coastline (i.e., the inundation calculation is not considered and the coastline is fixed in the simulation). Hence, the mass conservation is solved only in wet cells. The tide effect and its interaction with storm surges are not considered in this study because the predicted tidal level was 0.15 m (about 2–3% of the peak surge) when the peak surge occurred at Water 2020, 12, 3326 5 of 27

00:00 UTC 8 November 2013 [13,18] and the maximum tidal range was within 1.0 m in the San Pedro Bay [35]. The wave radiation stresses are not considered since they are insigniﬁcant in generating storm surges along the coasts of the Leyte Gulf [13,56] and the maximum wave-enhanced storm surge is about 0.3 m (approximately 6% of the peak storm surges) at Tacloban.

2.2. Parametric Holland Wind Model In this study, the well-known parametric Holland wind model (hereafter, HWM), is adopted to reconstruct the meteorological ﬁelds of Typhoon Haiyan. HWM has been widely used in storm surge modeling and wind-wave modeling [30,57]. The exponential distribution of the atmospheric pressure proposed by Schloemer (1954) [58] is extended by Holland (1980) [24]:

" B# Rmax Pa(r) = Pc + ∆P exp , (10) × − r where Pc is the central storm pressure, ∆P is the pressure drop (∆P = Pn Pc), Pn is the ambient − pressure, Rmax is the radius of maximum wind, r is the distance from the specified grid to the storm center, and B is the scaling parameter. Note that Pn can vary in the regions from about 1010 to 1013.25 hPa [59]. For the sake of simplicity, Pn = 1013.25 hPa is used in this study. By using the gradient wind balance equation, the solution of the radial wind profile with the Coriolis effect is yielded [24,60]: v u B t Rmax B B∆P exp !2 Rmax × − r r f r f Vg(r) = + . (11) r × ρa 2 − 2

Vg is the computed gradient Holland wind speed. Note that this solution is steady axisymmetric and geostrophic. The maximum gradient wind speed exists where r = Rmax where the Coriolis force is relatively insigniﬁcant compared to the pressure gradient and the centrifugal force, s B∆P Vg,max = . (12) ρae

As shown in Equation (12), the maximum gradient wind speed is proportional to the square root of the scaling parameter B, and the pressure drop ∆P. The 1999 Harper and Holland’s scaling parameter B for the computed pressure and wind profiles is shown below [60]: Pc 900 B = 2 − , (13) − 160 where the scaling parameter B is limited from 1.0 to 2.5. Note that if the scaling parameter B = 1, the pressure distribution proposed by Schloemer (1954) [58] is assumed. The radial distribution of the wind speed from the storm center is already given by Equations (10)–(13). Now, the wind directions are needed to define. As the assumption of a steady axisymmetric storm, the wind direction is cyclonic toward the storm center. However, while the storm is moving forward, the wind flows are crossing the isobars [60,61], which indicates an inward angle shall exist. In this study, the inward angle of 25 degrees is used [62]. By adopting the assumption of the cyclonic winds toward a storm center and the inward angle deviating from the isobars, the vector form of the gradient winds Vg is yielded from the function form of Equation (11). The correction factor is introduced here to convert the computed gradient Holland wind speed to the wind speed on the standard level, the 10-m height above the mean sea-level:

Vm = KmVg, (14) Water 2020, 12, 3326 6 of 27

where Km is based on the logarithmic law,

ln(10/z0) Km = , (15) ln zg/z0 in which z0 is the roughness length and zg is the height of the gradient wind. However, z0 and zg are hard to deﬁne during intense tropical storms. Alternatively, Km can be approximately used as 0.7 for the 10-m wind conversion of the Holland wind model [60]. Further discussions of Km on storm surge and wind wave modeling can be found in the studies such as Chueng et al. (2003) [30] and Phadke et al. (2003) [57]. While a storm is moving, the wind pattern becomes asymmetric because winds are ampliﬁed on the right and weaken on the left in a storm forward direction. The correction term for an asymmetric wind pattern is proposed [23]: R r U (r) = max V . (16) cor 2 2 f Rmax + r

Ucor is the correction term and Vf is the forward velocity of a storm. The maximum corrected term exists where r = Rmax and is equivalent to the half of the forward storm velocity. Finally, by assuming the exponential pressure distribution with the scaling parameter, using the gradient wind balance equation, and considering the eﬀect of the storm forward motion, the 10-m winds are expressed by U10 = KmVg + Ucor. (17) Note again that the 10-m winds come from the parametric wind model used in this study are frictional-free, steady-state, and geostrophic-balancing solutions.

2.3. WRF-ARW Model In this study, the 10-m wind ﬁelds and sea-level pressures performed by the WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) model are involved. WRF-ARW solves the 3D time-integrated, compressible, and non-hydrostatic Euler equations [10]. The model results of WRF-ARW version 3.8.1 used in this study are Cases 3 km F2 and 1 km F2 of Kueh et al. (2019) [15]. They will be denoted as WRF-ARW 3 km and WRF-ARW 1 km in the following discussion. The horizontal domain of WRF-ARW is from (117.88◦ E–147.12◦ E) and (0.0◦ N–19.62◦ N), and 45 vertical levels are employed. More modeling details and numerical settings of WRF-ARW can be found in Kueh et al. (2019) [15].

2.4. Storm Track and Parameters of Haiyan The storm track of Haiyan is illustrated in Figure1a, and the best-track parameters from the JMA (Japan Meteorological Agency) database [63] are listed in Table1. The JMA best-track database provides the best-track parameters of a storm every 6 h, such as the central pressures, the maximum sustained wind speeds, and the locations of a storm center. As shown in Figure1a, Typhoon Haiyan passed over the central regions of the Philippines and made landfall at the Samar and Leyte Islands between 18:00 UTC 7 November 2013 and 00:00 UTC 8 November 2013. In Figure1b, Haiyan passed over the Leyte Gulf and the San Pedro Bay. From the best-track parameters listed in Table1, Haiyan has the lowest central pressure of 895 hPa and sustained wind speed of about 64.30 m/s at 12:00 UTC and 18:00 UTC 7 November 2013. It implies that Haiyan made landfall in the Philippines while it has a peak storm intensity. Water 2020, 12, 3326 7 of 27 Water 2020, 12, x FOR PEER REVIEW 7 of 27

FigureFigure 1.1. ((aa)) StormStorm tracktrack ofof TyphoonTyphoon HaiyanHaiyan fromfrom thethe JapanJapan MeteorologicalMeteorological AgencyAgency (JMA)(JMA) best-trackbest-track databasedatabase over the Philippines Philippines;; (b (b) )storm storm track track of of Haiyan Haiyan in inthe the Leyte Leyte Gulf Gulf and and the theSan SanPedro Pedro Bay. Bay.The Thegrey grey shading shading depicts depicts the land. the land. The solid The solidred circles red circles indicate indicate the locations the locations of the of storm the storm center center in every in every6 h. The 6 h. detailed The detailed information information on the on storm the storm track track refers refers to Table to Table 1. The1. The dashed dashed blue blue box box in inPanel Panel (a) (denotesa) denotes the the area area showing showing in inPanel Panel (b). (b ).

Water 2020, 12, 3326 8 of 27

Table 1. Strom track parameters of 2013 Typhoon Haiyan from the JMA best-track database, provided in every 6 h. In this table, the maximum wind speeds of Haiyan are converted to the SI unit, m/s. The time zone of this table is UTC + 0 time. Pc indicates the central pressures of Haiyan, and Vmax depicts the maximum sustained wind speeds.

Year Month Day Hour Longitude (◦ E) Latitude (◦ N) Pc (hPa) Vmax (m/s) 2013 11 6 18 134.4 8.2 905 59.16 2013 11 7 00 132.8 8.7 905 59.16 2013 11 7 06 131.1 9.3 905 59.16 2013 11 7 12 129.1 10.2 895 64.30 2013 11 7 18 126.9 10.6 895 64.30 2013 11 8 00 124.8 11.0 910 56.58 2013 11 8 06 122.5 11.4 940 46.30 2013 11 8 12 120.5 11.9 940 46.30 2013 11 8 18 118.0 12.2 940 46.30 2013 11 9 00 116.6 12.3 940 46.30

The parameters required in HWM have been described in Table1, except for the radius of the maximum wind (RMW, Rmax). RMW is an important parameter which decides the scale of the storm winds. The effect of Rmax to storm surge modeling has been investigated by Kim et al. (2015) [18] and Rmax of 50 km with the leveling-off drag coefficient fulfills the survey data. However, smaller Rmax (accurately, Rmax = 23 and 30 km) is used to perform the storm surge simulation of Haiyan by Kumagai et al. (2016) [19]. However, the pressure distributions used in Kim et al. (2015) and Kumagai et al. (2016) are different from the one in this study. Therefore, RMW shall be examined again. In the following section, the effect of RMW will be discussed and verified against the observed weather data at the Guiuan weather station.

2.5. Computational Setup of Storm Surge Modeling The computational domain of the storm surge model is shown in Figure2a. The domain is designed to study the storm surges induced by Haiyan in the Leyte Gulf and the San Pedro Bay where had been impacted dramatically. The model domain is from (124.9◦ E–126.1◦ E) and (10.0◦ N–11.4◦ N). As shown in Figure2a, the coastline along the Samar Island to the Leyte Island is similar to the funnel shape, which makes this region more susceptible to storm surges [35]. In the San Pedro Bay, the water depths are less than 30 m, which depicts that the wind shear stresses can play an important role in intensifying storm surges locally. The source of the bathymetry is from GEBCO (the GEneral Bathymetric Chart of the Oceans) 2014 grid [64]. The grid size of the computational domain is 277.7 m over the whole computational domain. The model time step is 0.5 s to fulfill the Courant–Friedrichs–Lewy (CFL) condition in the explicit finite difference model [65]. The storm surge simulations are performed from 00:00 UTC 7 November 2013 to 12:00 UTC 8 November 2013, a total of 129,600 s. In our study, because of insufficient information of bathymetry and land data for the San Juanico Strait, our simulations end in the frontal part of the strait (see Figure2a) and the boundary condition used is non-flux. The storm surges induced by Haiyan around the San Juanico Strait have been discussed by Bricker et al. (2014) [66]. They indicated that Haiyan’s storm surges can extend up from the San Pedro Bay through the San Juanico Strait to Bogulibas and rapidly dissipate where the strait narrows. Note that the results reached by Bricker et al. (2014) [66] were based on the simulations using rough bathymetry from GEBCO and topography from SRTM (Shuttle Radar Topography Mission) despite some local detailed data such as the nautical charts were adopted. The results are expected to be better if more accurate bathymetry and land data can be acquired in future works. WaterWater2020 2020,,12 12,, 3326 x FOR PEER REVIEW 99 ofof 2727

Figure 2. (a) The water depths over the computational domain. The color shading indicates the water Figure 2. (a) The water depths over the computational domain. The color shading indicates the water depth in m. The dashed black line depicts the contour line of the 30 m water depth. (b) A map for the depth in m. The dashed black line depicts the contour line of the 30 m water depth. (b) A map for the numerical gauges set up in the storm surge model (solid green circles) and the Guiuan weather station numerical gauges set up in the storm surge model (solid green circles) and the Guiuan weather station (solid red circle). The numerical gauges can be found in Table2. (solid red circle). The numerical gauges can be found in Table 2. Table 2. Locations of the numerical gauges setting in the storm surge model. Table 2. Locations of the numerical gauges setting in the storm surge model. Station Name Longitude (◦ E) Latitude (◦ N) BalangigaStation Name Longitude 125.3775 (° E) Latitude 11.0934(° N) MarabutBalangiga 125.1968125.3775 11.0934 11.1108 TaclobanMarabut 125.0199125.1968 11.1108 11.2440 TanauanTacloban 125.0356125.0199 11.2440 11.1014 Basey 125.0518 11.2657 Tanauan 125.0356 11.1014 Dulag 125.0500 10.9458 AbuyogBasey 125.1089125.0518 11.2657 10.7137 PaloDulag 125.0168125.0500 10.9458 11.1560 MacarthurAbuyog 125.0132125.1089 10.7137 10.8221 Palo 125.0168 11.1560 The time histories of stormMacarthur surges are provided125.0132 at the specified10.8221 numerical gauges in the storm surge model. Figure2b shows the map of these numerical gauges, and Table2 lists their locations. Note3. Results again and that Discussions the boundary conditions between wet and dry cells are non-flux (i.e., the volume fluxes of water are not allowed to cross the coastline and set to zero along the coastline). Hence, the locations 3.1. Validation of Parametric Holland Wind Model of the numerical gauges are moved two to three grids away from the coastline to avoid the land effect causedBefore by the performing boundaries. the storm surge simulation, the results of HWM shall be verified. Hence, in this section, the 10-m winds and the sea-level pressures of HWM are compared to the observed data 3.at Results the Guiuan and Discussions weather station, East Samar. In the Guiuan weather station, the wind speeds are measured on the vertical level of 60 m and are converted to the standard level of 10 m using the 3.1. Validation of Parametric Holland Wind Model power-law formula [67]. The observation data of the Guiuan weather station are digitalized from Kim Beforeet al. (2015) performing [18]. This the stormstudy surgechooses simulation, the RMW the of results30 km following of HWM shall Kumagai be verified. et al. (2016) Hence, [1 in9] thisand section,50 km from the 10-m Kim windset al. (2015) and the [18]. sea-level The study pressures of Kumagai of HWM et al. are (2016) compared [18] used to the Rmax observed = 23 and data 30 atkm the to Guiuanreconstruct weather the storm station, winds East of Samar. Haiyan. In theThis Guiuan study fixes weather smaller station, RMW the in wind the storm speeds wind aremeasured modeling onto have the vertical a fair comparison level of 60 and m and analyze are converted the differenc to thees in standard the use of level RMW. of 10 m using the power-law formulaIn Figure [67]. The3, the observation results of dataHWM of using the Guiuan the pressure weather distribution station are scaled digitalized by the from parameters Kim et al.of (2015)Harper [18 and]. This Holland study (1999) chooses [60] the are RMW compared of 30 km to followingthe weather Kumagai data measured et al. (2016) at [the19] Guiuan and 50 kmweather from Kimstation. et al. In (2015) Figure [18 3].a, Thethe observed study of Kumagai sea-level et pressure al. (2016)s start [18] usedto decreaseRmax = gradually23 and 30 kmfrom to about reconstruct 04:00 UTC 7 November 2013, drop dramatically from 12:00 UTC 7 November 2013, and then reach the

Water 2020, 12, x FOR PEER REVIEW 10 of 27 minimum values of 910 hPa at 22:00 UTC 7 November 2013. Unfortunately, the measurement was interrupted because the weather station was damaged by Haiyan [68]. Using 1999 Harper and Holland’s pressure distribution with Rmax = 50 km, the peak value of predicted sea-level pressures is close to the observation. For the results using Rmax = 30 km, they decrease at 15:00 UTC 7 November 2013, which changes are slower than the ones using Rmax = 50 km. Both of them slightly over-predicted theWater sea2020-level, 12, 3326pressures from 06:00 UTC to 15:00 UTC on 7 November 2013 and give earlier arrival10 of 27 time of the peak sea-level pressure, 1 h before the observation. In Figure 3b, using Rmax = 50 km over- predicts wind speeds. Compared to the use of Rmax = 50 km, the predicted winds using Rmax = 30 km matchthe storm closer winds to the of converted Haiyan. This 10-m study winds. ﬁxes smaller RMW in the storm wind modeling to have a fair comparisonWithout andusing analyze the 1999 the Harper diﬀerences and in Holland’s the use of scaling RMW. parameter, the pressure distribution of SchloemerIn Figure (1954)3, the[58] resultsis assumed. of HWM As shown using thein Figure pressure 4a, distributionthe results using scaled Rmax by = the 30 parameterskm match the of trendHarper of the and observed Holland (1999)sea-level [60 pressures] are compared closer tothan the the weather ones using data measured Rmax = 50 km at the. However, Guiuan weatherboth of themstation. underestimate In Figure3a, t thehe peak observed value sea-level of the seapressures-level pressures start to decrease measured gradually at the fromGuiuan about weather 04:00 station.UTC 7 The November comparison 2013, against drop dramatically the observed from winds 12:00 is illustrated UTC 7 November in Figure 2013, 4b. In and Figure then reach4b, both the resultsminimum do not values match of the 910 observed hPa at 22:00 wind UTC pattern, 7 November which increases 2013. Unfortunately,quickly from 14:00 the measurementUTC 7 November was 2013.interrupted The predicted because winds the weather increase station gently wasfrom damaged 00:00 UTC by 7 Haiyan November [68]. 2013 Using and 1999 reach Harper the peak and valuesHolland’s at about pressure 21:00 distribution UTC 8 Nov withemberR max2013.= 50 km, the peak value of predicted sea-level pressures is closeDespite to the observation.using the 1954 For Schloemer the results’s usingscalingR maxparameter= 30 km, with they R decreasemax = 30 km at 15:00seems UTC to have 7 November a better comparison2013, which with changes the observation are slower than data the in onesterms using of theR maxsea-=level50 km. pressures, Both of the them predicted slightly over-predictedwinds cannot matchthe sea-level the dramatic pressures increase from from 06:00 15:00 UTC toUTC 15:00 7 November UTC on 7 November2013. By verifying 2013 and against give earlier the observation arrival time dataof the at peakthe Guiuan sea-level weather pressure, station 1 h before, the theuse observation. of the pressure In Figure distribution3b, using scaledRmax =by50 the km 1999 over-predicts Harper andwind Holland speeds.’s parameters Compared towith the R usemax = of 30R maxkm =has50 the km, best the agreement predicted windsin the wind using comparison.Rmax = 30 km Hence, match thecloser results to the of HWM converted presented 10-m winds. in the following sections will use this configuration.

Figure 3. Comparisons of the Holland wind model (HWM) results between Rmax = 30 and 50 km Figure 3. Comparisons of the Holland wind model (HWM) results between Rmax = 30 and 50 km with thewith observation the observation data at data the atGuiuan the Guiuan weather weather station station:: (a) sea (-alevel) sea-level pressure pressure;; and (b and) wind (b) windspeed. speed. The The pressure distribution scaled by the 1999 Harper and Holland’s parameters is assumed. Note that pressure distribution scaled by the 1999 Harper and Holland’s parameters is assumed. Note that the the wind speed is measured on the level of 60 m [18], and the 10-m wind speed is converted using the wind speed is measured on the level of 60 m [18], and the 10-m wind speed is converted using the power-law formula [67]. power-law formula [67].

Without using the 1999 Harper and Holland’s scaling parameter, the pressure distribution of Schloemer (1954) [58] is assumed. As shown in Figure4a, the results using Rmax = 30 km match the trend of the observed sea-level pressures closer than the ones using Rmax = 50 km. However, both of them underestimate the peak value of the sea-level pressures measured at the Guiuan weather station. The comparison against the observed winds is illustrated in Figure4b. In Figure4b, both results do not match the observed wind pattern, which increases quickly from 14:00 UTC 7 November 2013. Water 2020, 12, 3326 11 of 27

WaterThe predicted2020, 12, x FOR winds PEER increase REVIEW gently from 00:00 UTC 7 November 2013 and reach the peak values11 of 27 at about 21:00 UTC 8 November 2013.

Figure 4. Comparisons of HWM results between Rmax = 30 and 50 km with the observation data at Figure 4. Comparisons of HWM results between Rmax = 30 and 50 km with the observation data at the the Guiuan weather station: (a) sea-level pressure; (b) wind speed. The 1954 Schloemer’s pressure Guiuan weather station: (a) sea-level pressure; (b) wind speed. The 1954 Schloemer’s pressure distribution is assumed. Note that the wind speed is measured on the level of 60 m [18], and the 10-m distributionwind speed is assumed. converted Note using that the the power-law wind speed formula is measured [67]. on the level of 60 m [18], and the 10- m wind speed is converted using the power-law formula [67].

Despite using the 1954 Schloemer’s scaling parameter with Rmax = 30 km seems to have a better 3.2.comparison Haiyan’s withStorm the Surge observation Simulation data by HWM in terms of the sea-level pressures, the predicted winds cannot matchAf theter examining dramatic increase the 10-m from winds 15:00 and UTC sea- 7level November pressures 2013. against By verifying the observation against thedata observation at Guiuan weatherdata at thestation, Guiuan the storm weather surge station, modeling the use driven of the by pressure HWM are distribution explored in scaled this section. by the 1999 Harper andIn Holland’s storm surge parameters modeling, with windsRmax = can30 kmamplify has the the best surge agreement heights inin the coastal wind regions comparison. when Hence,wind directionsthe results are of HWMmore or presented less perpendicular in the following to coasts. sections Besides, will usethe thisintensity conﬁguration. of winds is relative to the wind shear stresses over a water surface. Hence, before performing storm surge predictions, the wind 3.2. Haiyan’s Storm Surge Simulation by HWM fields on the standard level of 10 m shall be examined. In Figure 5, the snapshots of the 10-m winds predictedAfter by examining HWM are the presented. 10-m winds In Figure and sea-level 5a, the storm pressures center against of Haiyan the observation just enters datathe Leyte at Guiuan Gulf andweather passes station, over the the small storm island surge in modeling the south driven of the bySamar HWM Island are explored. In a while, in thisthe value section. of 10-m winds blowingIn stormto the surgeSamar modeling, Island is higher winds than can amplify 60 m/s, theand surge the strong heights offshore in coastal winds regions occur when in the wind San Pedrodirections Bay. areAs moreshown or in less Figure perpendicular 5b, Haiyan to moves coasts. to Besides, the central the intensityregions of of the winds Leyte is Gulf, relative and to the the HWMwind shear-predicted stresses winds over hit a waterthe southern surface. regions Hence, of before the Samar performing Island, storm such as surge Giporlos predictions, and Balangiga. the wind Whenﬁelds onHaiyan the standard makes landfall level of at 10 the m shallLeyte be Island, examined. the severe In Figure onshore5, the winds snapshots towards of the the 10-m San windsPedro Baypredicted can be byfound HWM in Figure are presented. 5c. The winds In Figure of 5morea, the than storm 60 centerm/s hit of the Haiyan regions just in enters particular the Leyte Tacloban, Gulf Basey, and Palo. After Haiyan makes landfall at Leyte Island, the southeastern winds keep blowing from the Leyte Gulf to the San Pedro Bay (see Figure 5d). As shown in Figure 5d, after the landfall, the cyclonic winds still maintain the wind speeds of more than 45 m/s.

Water 2020, 12, 3326 12 of 27 and passes over the small island in the south of the Samar Island. In a while, the value of 10-m winds blowing to the Samar Island is higher than 60 m/s, and the strong oﬀshore winds occur in the San Pedro Bay. As shown in Figure5b, Haiyan moves to the central regions of the Leyte Gulf, and the HWM-predicted winds hit the southern regions of the Samar Island, such as Giporlos and Balangiga. When Haiyan makes landfall at the Leyte Island, the severe onshore winds towards the San Pedro Bay can be found in Figure5c. The winds of more than 60 m /s hit the regions in particular Tacloban, Basey, and Palo. After Haiyan makes landfall at Leyte Island, the southeastern winds keep blowing from the LeyteWater 20 Gulf20, 1 to2, x the FOR San PEER Pedro REVIEW Bay (see Figure5d). As shown in Figure5d, after the landfall, the cyclonic12 of 27 winds still maintain the wind speeds of more than 45 m/s.

Figure 5. Snapshots of the 10-m winds predicted by HWM at: (a) 21:30 UTC 7 November 2013; (b) 22:30 UTCFigure 7 November 5. Snapshots 2013; of (thec) 23:30 10-m UTC winds 7 November predicted 2013;by HWM (d) 00:30 at: ( UTCa) 21:30 8 November UTC 7 November 2013. The 2013; dashed (b) red22:30 line UTC denotes 7 November the JMA best2013; track. (c) 23:30 The UTC color 7 shading November indicates 2013; the (d) 10-m 00:30 wind UTC speed 8 November in m/s. The2013. 10-m The windsdashed shown red line in thisdenotes figure the have JMA been best interpolated track. The color to the shading grids ofindicates the storm the surge 10-m model. wind speed The arrows in m/s. depictThe 10 the-m windwinds direction shown in per this every figure 15 gridshave overbeen theinterpolated domain. Theseto the snapshotsgrids of the have storm been surge zoomed model in. fromThe arrows the original depict computational the wind direction domain. per every 15 grids over the domain. These snapshots have been zoomed in from the original computational domain. In Figure6, the computed storm surges driven by HWM are illustrated. Due to the strong o ffshore windsIn in Figure the San 6, Pedro the computed Bay and the storm Leyte surges Gulf, thedriven negative by HWM storm are surges illustrated are generated. Due to accordingly the strong (seeoffshore Figure winds6a,b). Asin the shown San inPedro Figure Bay6c, and when the Haiyan Leyte makesGulf, the landfall negati atve the storm Leyte surges Island, are the generated cyclonic windsaccordingly push up (see the Figure water 6a hitting,b). As the shown regions in suchFigure as 6 Tacloban,c, when Haiyan Palo, and makes Tanauan. landfall Storm at the surges Leyte of Island, more thanthe cyclonic 3.5 m are winds observed push in up the the simulation. water hitting After the Haiyan regions makes such as landfall Tacloban, at the Palo, Leyte and Island, Tanauan. the winds Storm insurges the San of Pedromore than Bay become 3.5 m are southeastern observed in (see the Figure simulation.6d). Because After ofHaiyan these winds,makes thelandfall waters at arethe piledLeyte upIsland, inside the the winds bay andin the generate San Pedro the Bay significant become storm southeastern surges of (see more Figure than 6d). 4.0 Because m, which of hit these Tacloban, winds, Palo,the waters and Basey. are piled up inside the bay and generate the significant storm surges of more than 4.0 m, which hit Tacloban, Palo, and Basey.

Water 2020, 12, 3326 13 of 27 Water 2020, 12, x FOR PEER REVIEW 13 of 27

FigureFigure 6.6. SnapshotsSnapshots ofof thethe computedcomputed stormstorm surgessurges drivendriven byby HWMHWM at:at: ((aa)) 21:3021:30 UTCUTC 77 NovemberNovember 2013;2013; ((bb)) 22:3022:30 UTCUTC 77 NovemberNovember 2013;2013; ((cc)) 23:3023:30 UTCUTC 77 NovemberNovember 2013;2013; ((dd)) 00:3000:30 UTCUTC 77 NovemberNovember 2013.2013. TheThe colorcolor shadingshading indicatesindicates thethe stormstorm surgesurge inin mm andand thethe blackblack lineline depictsdepicts thethe coastlinecoastline ofof thethe stormstorm surgesurge model.model. TheThe arrowsarrows depictdepict thethe 10-m10-m windswinds ofof HWMHWM perper everyevery 1515 gridsgrids overover thethe domain.domain. TheseThese snapshots snapshots have have been been zoomed zoomed in in from from the the original original computational computational domain. domain.

The maximum computed storm surges driven are shown in Figure7, and the maximum computed The maximum computed storm surges driven are shown in Figure 7, and the maximum surge heights along the coasts of the Leyte and Samar Islands are compared to the measured field computed surge heights along the coasts of the Leyte and Samar Islands are compared to the survey data as well. In Figure7a,b, the field survey data of Tajima et al. (2014) [ 33] with the Reliability A measured field survey data as well. In Figure 7a,b, the field survey data of Tajima et al. (2014) [33] (clear mark and small error of survey) are presented and the tide e ect has been removed. The profiles with the Reliability A (clear mark and small error of survey) areff presented and the tide effect has ofbeen maximum removed. computed The profiles surge of heights maximum are acquired computed one gridsurge away heights from are the acquired coasts to one avoid grid the away boundary from etheffect. coasts In Figure to avoid7a,b, the simulationboundary effe resultsct. In agree Figure well 7a with,b, the the simulation patterns of results the measured agree well water with levels the alongpatterns the ofcoasts the ofmeasured the Leyte water Island levels and the along Samar the Island coasts generally. of the Leyte Besides, Island relatively and the higherSamar storm Island surgesgenerally. are foundBesides, near relatively the inner higher of the storm San Pedro surges Bay are in found both modelnear the results inner and of the the San measured Pedro Bay water in levels.both model However, results the and measured the measured water levels water of levels. more However, than 6 m canthe bemeasured found near water the levels innermost of more part than of the6 m San can Pedro be found Bay (see near Figure the innermost7a), where thepart wave of the overtopping San Pedro and Bay the (see up-wash Figure of7a), wind where waves the could wave beovertopping severe [19]. and The the model up-wash results of may wind give waves an underestimation could be severe of [ water19]. The levels model near results the bay. may Moreover, give an theunderestimation comparisons areof water expected levels to near be better the bay. if the Moreover, inundation the comparisons calculation can are be expected performed to be with better the if detailedthe inundation land elevation calculation model can and be performed the shape of with coastline the detailed in the Leyteland elevation Gulf and model the San and Pedro the Bayshape [18 of]. Ascoastline shown in in the Figure Leyte7c, Gulf inside and the the San San Pedro Pedro Bay, Bay the [18 maximum]. As shown storm in Figure surges 7 ofc, moreinside than the 4.5San m Pedro are foundBay, the in Basey,maximum Tacloban, storm Palo,surges and of Tanauan.more than These 4.5 m prominentare found in maximum Basey, Tacloban, storm surges Palo, areand attributed Tanauan. toThese the southeastern prominent maximum winds from storm the Leytesurges Gulf are toattributed the San Pedro to the Bay, southeastern making the winds water fr pileom upthe inside Leyte theGulf bay to afterthe San Haiyan Pedro makes Bay, making landfall the at thewater Leyte pile Island up inside (see the Figure bay6 afterd). Haiyan makes landfall at the Leyte Island (see Figure 6d).

WaterWater2020 20,2012, ,1 33262, x FOR PEER REVIEW 1414 of of 27 27

Figure 7. (a,b) Comparison between maximum computed storm surges and field survey data on along theFigure coasts; 7. ( c(a), maximumb) Comparison computed between storm maximum surges driven computed by HWM. storm Insurges Panels and (a ,fieldb), the survey black data dots on imply along thethe measured coasts; (c water) maximum level by computed field survey storm [33 surges], and driven the blue by lines HWM. indicate In Panels numerical (a,b), the results black along dots imply the coasts.the measured The y-axis water of Panel level( aby) andfield the survey x-axis [33 of], Panel and the (b )blue correspond lines indicate to the numerical geographical results coordinate along the ofcoasts. Panel (Thec). Iny-axis Panel of (Panelc), the (a color) and shading the x-axis indicates of Panel the (b) maximum correspond storm to the surge geographical distribution coordinate in m, andof solidPanel black(c). Inlines Panel present (c), the thecolor coastline. shading indicates The dashed the blackmaximum lines storm show surge the contour distribution of maximum in m, and computedsolid black storm lines surges. present This the figure coastline. has been The zoomed dashed in from black its lines original show computational the contour domain. of maximum computed storm surges. This figure has been zoomed in from its original computational domain. In Figure8, the time series of computed storm surges at numerical gauges are presented. In all stations,In the Figure records 8, the show time the series declines of computed of the water-level storm surges first and at numerical then following gauge quicks are risespresented. within In 3 h. all Thisstations, phenomenon the records corresponds show the to declines the witness of the reports water about-level the first “tsunami-like” and then following wavefront quick hit rises Tacloban within and3 h Basey. This after phenomenon the water-level corresponds receded to [32 the,35 ].witness Maximum reports storm about surges the of “tsunami more than-like” 4.0 mwavefront are found hit inTacloban Tacloban, and Basey, Basey Tanauan, after the andwater Palo-level (see receded Figure [83a,d,e,h),2,35]. Maximum which corresponds storm surges to of the more maximum than 4.0 m computedare found surges in Tacloban, and field Basey, survey Tanauan, shown and in Figure Palo 7 (see. There Figure are 8a,d two,e, water-levelh), which corresponds measurements to the atmaximum Tacloban and computed Guiuan surges tidal stations and field during survey the passage shown ofin HaiyanFigure [719. ].There The areobservation two water of- thelevel Taclobanmeasurements tidal station at Tacloban was interrupted and Guiuan before tidal the peak stations storm during surge occurred; the passage hence, of thisHaiyan observation [19]. The didn’tobservation record theof the complete Tacloban time tidal series station of water-level.was interrupted In the before Guiuan the peak tide stationstorm surge which occurred; is located hence, at thethis southeastern observation part didn’t of the record Samar the Island complete not the time San series Pedro of Bay,water the-level. wind In wave the Guiuan and storm tide surgestation interactionswhich is located could be at significant.the southeastern Hence, part we onlyof the use Samar the field Island survey not the data San of TajimaPedro etBay, al. the (2014) wind [33 wave] to compareand storm the surge maximum interactions computed could storm be significant. surges as theHence, model we validation.only use the More field discussionsurvey data on of theseTajima observedet al. (2014) water-levels [33] to compar can be founde the maximum in Kumagai comp et al.uted (2016) storm [19 ]surges and Soria as the et al. model (2016) validation. [35]. More discussion on these observed water-levels can be found in Kumagai et al. (2016) [19] and Soria et al. (2016) [35].

Water 20202020,, 1212,, 3326x FOR PEER REVIEW 15 of 27

Figure 8. (a–i) Time series of the computed storm surges driven by HWM at the numerical gauges. TheFigure information 8. (a–i) Time of numerical series of the gauges computed can be storm found surges in Figure driven2b and by TableHWM2. at The thex numerical-axis indicates gauges the. timeThe information from 12:00 UTC of numerical 7 November gauges 2013 can to 12:00be found UTC in 8 Figure November 2b and 2013. Table 2. The x-axis indicates the time from 12:00 UTC 7 November 2013 to 12:00 UTC 8 November 2013. 3.3. Haiyan’s Storm Surge Simulation by WRF-ARW 3.3. Haiyan’s Storm Surge Simulation by WRF-ARW In this section, the results of storm surge modeling driven by WRF-ARW are explored. The 10-m windsIn and this sea-levelsection, the pressure results from of storm WRF-ARW surge modeling 3 km and driven WRF-ARW by WRF 1 km-ARW with are the explored. 1-h time intervalThe 10- arem winds used and to drive sea-level the storm pressure surge from model. WRF- ToARW reach 3 km the and storm WRF intensity-ARW 1 ofkm Haiyan with the in 1 the-h time WRF-ARW interval modeling,are used to the drive nudging the storm scheme, surge four-dimensional model. To reach data the assimilation storm intensity (FDDA), of Haiyan is used in from the 00:00WRF UTC-ARW 4 Novembermodeling, the 2013 nudging to 00:00 scheme, UTC 5 four November-dimensional 2013 (24 data h). assimilation Afterward, (FDDA), the simulation is used is from carried 00:00 out UTC by WRF-ARW4 November only 2013 without to 00:00 the UTC further 5 Nov useember of data 2013 assimilation. (24 h). Afterward, Hence, the translationsimulation speedis carried of Haiyan out by predictedWRF-ARW by only WRF-ARW without is the slower further than use the of JMA data best-track assimilati dataon. Hence, (see Figure the 9translation). As illustrated speed in of Figure Haiyan9, thepredicted storm by landfall WRF- locationsARW is slower between than the the JMA JMA best-track best-track anddata WRF-ARW (see Figure 39). km As areillustrated more or in less Figure the same9, the andstorm WRF-ARW landfall locations 1 km predicted between more the JMA south best landfall-track and location WRF at-ARW the Leyte 3 km Island.are more The or landfallless the timessame and predicted WRF- byAR WRF-ARWW 1 km predicted 3 km and more WRF-ARW south landfall 1 km atlocation the Leyte at the Island Leyte are Island. 4–5 h lateThe thanlandfall the best-track.times predicted The stormby WRF centers-ARW of 3 WRF-ARWkm and WRF 3- kmARW and 1 WRF-ARWkm at the Leyte 1 km Island and their are 4 distance–5 h late fromthan the best-trackbest-track.are The tabulated storm centers in Table of3 WRF. More-ARW discussions 3 km and about WRF the-ARW WRF-ARW 1 km and modeling their distance can be foundfrom the in Kuehbest-track et al. are (2019) tabulated [15]. in By Table the deviations 3. More discussions of landfall about locations the WRF and- theARW translation modeling speed can be of found Haiyan in fromKueh WRF-ARW,et al. (2019) the [15 results]. By the of stormdeviations surge of prediction landfall locations driven by and WRF-ARW the translation are anticipated speed of toHaiyan show difromﬀerent WRF patterns-ARW, fromthe results those of driven storm by surge HWM. prediction driven by WRF-ARW are anticipated to show different patterns from those driven by HWM.

Water 2020, 12, 3326 16 of 27 Water 2020, 12, x FOR PEER REVIEW 16 of 27

Figure 9. Comparison of storm tracks among JMA best-track (black line), weather research and Figure 9. Comparison of storm tracks among JMA best-track (black line), weather research and forecasting-advanced research WRF (WRF-ARW) 3 km (blue line), and WRF-ARW 1 km (red line). forecasting-advanced research WRF (WRF-ARW) 3 km (blue line), and WRF-ARW 1 km (red line). The storm centers are illustrated every 6 h. The temporal resolution of storm center’s location: The storm centers are illustrated every 6 h. The temporal resolution of storm center’s location: JMA JMA best-track—6 h; WRF-ARW 3 km and 1 km—1 h. best-track—6 h; WRF-ARW 3 km and 1 km—1 h. Table 3. The storm centers of WRF-ARW 3 km and WRF-ARW 1 km and their distances apart from thoseTable of 3. theThe JMA storm best-track. centers of The WRF distance-ARW between 3 km and the WRF predicted-ARW storm 1 km center and their of WRF-ARW distances andapart that from of thethose JMA of the best-track JMA best is calculated-track. The by distance the great-circle between distancethe predicted formula. storm center of WRF-ARW and that of the JMA best-track is calculated by the great-circle distance formula. Date WRF-ARW 3 km WRF-ARW 1 km Date WRF-ARW 3 km WRF-ARW 1 km Longitude Latitude Distance Longitude Latitude Distance Year Month Day Hour Longitude Latitude Distance Longitude Latitude Distance Year Month Day Hour (◦ E) (◦ N) (km) (◦ E) (◦ N) (km) (° E) (° N) (km) (° E) (° N) (km) 2013 11 7 00 133.22 9.02 58.38 133.40 8.73 66.48 2013 11 7 00 133.22 9.02 58.38 133.40 8.73 66.48 2013 11 7 06 131.45 9.32 38.55 131.60 9.13 57.74 20132013 1111 77 1206 129.63131.45 9.729.32 38.55 78.60 131.60 130.04 9.13 9.46 57.74 132.22 20132013 1111 87 0012 126.06129.63 10.689.72 141.6078.60 130.04 126.23 10.409.46 132.22 169.78 20132013 1111 88 0600 124.15126.06 11.2210.68 141.60 180.81 126.23 124.36 10.40 10.85 169.78 211.54 20132013 1111 88 1206 121.97124.15 11.9911.22 180.81 160.03 124.36 122.27 10.85 11.58 211.54 195.66 2013 11 8 12 121.97 11.99 160.03 122.27 11.58 195.66 In Figures 10 and 11, the snapshots of 10-m winds of WRF-ARW 3 km and WRF-ARW 1 km are In Figures 10 and 11, the snapshots of 10-m winds of WRF-ARW 3 km and WRF-ARW 1 km are presented respectively. As shown in Figure 10a,b, the storm center of Haiyan predicted by WRF-ARW presented respectively. As shown in Figure 10a,b, the storm center of Haiyan predicted by WRF- 3 km just arrives at the small island in the south of East Samar and then passes over it. The locations of ARW 3 km just arrives at the small island in the south of East Samar and then passes over it. The the storm center are similar to the JMA’s best track (see Figure6a). At these moments, the winds hit locations of the storm center are similar to the JMA’s best track (see Figure 6a). At these moments, southeastern regions of the Samar Island and oﬀshore winds occur in the San Pedro Bay. In Figure 10c, the winds hit southeastern regions of the Samar Island and offshore winds occur in the San Pedro the storm center predicted by WRF-ARW 3 km tends to land near Mayorga, which is relatively Bay. In Figure 10c, the storm center predicted by WRF-ARW 3 km tends to land near Mayorga, which south than the one predicted by HWM (see Figure6c). After making landfall at the Leyte Island, is relatively south than the one predicted by HWM (see Figure 6c). After making landfall at the Leyte the southeastern winds occur in the San Pedro Bay (see Figure6d); however, the wind intensity is not Island, the southeastern winds occur in the San Pedro Bay (see Figure 6d); however, the wind as strong as HWM. intensity is not as strong as HWM. In Figure 11a, the storm center of Haiyan predicted by WRF-ARW 1 km passes over Dinagat islands, which is further south than the ones predicted by WRF-ARW 3 km and the JMA best track. Compared to wind speeds of WRF-ARW 3 km, WRF-ARW 1 km’s results are much more potent, about 20% stronger than that. As shown in Figure 11b, the winds of WRF-ARW 1 km are more parallel to the southern regions of the Samar Island. The wind speeds of more than 55 m/s are found in the east and northeast part to the storm center. WRF-ARW 1 km gives a further south landfall location than others; hence, the strong onshore winds hit the regions such as Macarthur, Dulag, and Tolosa (see Figure 11c). After making landfall at the Leyte Island, the southeastern winds blowing from the Leyte Gulf to the San Pedro Bay occur (see Figure 11d). Moreover, the stronger winds of WRF-ARW 1 km hit the regions such as Tolosa and Dulag and the maximum 10-m wind speed of more than 45 m/s is still found near the east coasts of the Leyte Island.

Water 2020, 12, x FOR PEER REVIEW 17 of 27

Water 2020, 12, x FOR PEER REVIEW 17 of 27 In the comparison between WRF-ARW 3 km and WRF-ARW 1 km, the significant discrepancy is theIn the storm comparison track, which between affects WRF the-ARW predicted 3 km and landfall WRF -ARW location 1 km at, t thehe significant Leyte Island discrepancy and the issoutheastern the storm winds track, blowing which affectsfrom the the Leyte predicted Gulf to landfallthe San Pedro location Bay at. the Leyte Island and the Water 2020, 12, 3326 17 of 27 southeastern winds blowing from the Leyte Gulf to the San Pedro Bay.

Figure 10. Snapshots of the 10-m winds by WRF-ARW 3 km at (a) 01:00 UTC 8 November 2013; (b) FigureFigure02:00 UTC 10. 10.Snapshots Snapshots 8 November of of the the2013; 10-m 10 -(mc winds) 03:00winds by UTC by WRF-ARW W 8 RFNovember-ARW 3 km 3 atkm2013 (a at) 01:00( d(a)) 04:0001:00 UTC UTCUTC 8 November 88 NovemberNovember 2013; 2013 (2013;b) 02:00. The(b) UTC02:00dashed 8 UTC November red 8 line November denotes 2013; ( c 2013;the) 03:00 JMA (c UTC) 03:00best 8 track. NovemberUTC The8 November color 2013 shading (d )2013 04:00 indicates(d UTC) 04:00 8 November UTCthe 10 8- mNovember wind 2013. speed The 2013 dashed in. m/s.The reddashedThe line 10- denotesmred winds line thedenotes shown JMA bestthein this JMA track. figure best The track.have color beenThe shading color interpolated indicates shading thetoindicates the 10-m grids wind the of 10 speedthe-m stormwind in m speedsurge/s. The inmodel. 10-m m/s. windsTheThe 10arrows shown-m winds depict in this shown the ﬁgure wind in havethis direction figure been interpolated perhave every been 15 interpolated to grids the gridsover the ofto the domain. stormgrids of surge the model.storm surge The arrowsmodel. depictThe arrows the wind depict direction the wind per direction every 15 per grids every over 15 the grids domain. over the domain.

Figure 11. Snapshots of the 10-m winds by WRF-ARW 1 km at (a) 02:00 UTC 8 November 2013; (b ) 03:00 UTCFigure 8 November 11. Snapshots 2013; of (c the) 04:00 10-m UTC winds 8 November by WRF- 2013ARW ( d1) km 05:00 at UTC(a) 02 8:00 November UTC 8 November 2013. The 2013; dashed (b) redFigure03:00 line UTC denotes11. Snapshots 8 November the JMA of bestthe2013; 10 track. -(mc) 04:00winds The colorUTC by W shading8 RFNovember-ARW indicates 1 km2013 at the ( d(a) 10-m) 05:0002:00 wind UTCUTC speed 88 NovemberNovember in m/s. The 20132013; 10-m. The(b) winds03:00dashed UTC shown red 8 line November in thisdenotes figure 2013;the have JMA (c been) 04:00best interpolated track. UTC The8 November color to the shading grids 2013 of indicates(d the) 05:00 storm UTCthe surge 10 8- mNovember model. wind speed The 2013 arrows in. m/s.The depictdashedThe 10 the- mred windwinds line directiondenotes shown thein per this JMA every figure best 15 gridstrack.have overbeenThe color theinterpolated domain. shading toindicates the grids the of 10 the-m stormwind speedsurge inmodel. m/s. TheThe 10arrows-m winds depict shown the wind in this direction figure perhave every been 15 interpolated grids over theto the domain grids. of the storm surge model. InThe Figure arrows 11 depicta, the the storm wind center direction of per Haiyan every predicted15 grids over by the WRF-ARW domain. 1 km passes over Dinagat islands, which is further south than the ones predicted by WRF-ARW 3 km and the JMA best track. Compared to wind speeds of WRF-ARW 3 km, WRF-ARW 1 km’s results are much more potent, Water 2020, 12, 3326 18 of 27 about 20% stronger than that. As shown in Figure 11b, the winds of WRF-ARW 1 km are more parallel to the southern regions of the Samar Island. The wind speeds of more than 55 m/s are found in the east and northeast part to the storm center. WRF-ARW 1 km gives a further south landfall location than others; hence, the strong onshore winds hit the regions such as Macarthur, Dulag, and Tolosa (see Figure 11c). After making landfall at the Leyte Island, the southeastern winds blowing from the Leyte Gulf to the San Pedro Bay occur (see Figure 11d). Moreover, the stronger winds of WRF-ARW 1 km hit the regions such as Tolosa and Dulag and the maximum 10-m wind speed of more than 45 m/s is still found near the east coasts of the Leyte Island. In the comparison between WRF-ARW 3 km and WRF-ARW 1 km, the significant discrepancy is theWater storm 2020, track,12, x FOR which PEER aREVIEWffects the predicted landfall location at the Leyte Island and the southeastern18 of 27 winds blowing from the Leyte Gulf to the San Pedro Bay. TheThe computedcomputed stormstorm surgessurges drivendriven byby WRF-ARWWRF-ARW 33 kmkm andand WRF-ARWWRF-ARW 11 kmkmare are illustratedillustrated inin FiguresFigures 12 12 and and 13 13, respectively., respectively. As As shown shown in Figure in Figure 12a,b, 12 duea,b, todue the to cyclonic the cyclonic winds, winds, the negative the negative storm surgesstorm occur surges in occur the Leyte in the Gulf Leyte and the Gulf San and Pedro the Bay. San When Pedro the Bay. storm When center the of storm Haiyan center approaches of Haiyan the Leyteapproaches Island, the the Leyte positive Island, storm the surges positive of more storm than surges 1 m areof more generated than (see1 m Figureare generated 12c). In Figure(see Figure 12d, after12c). Haiyan In Figure makes 12d, landfallafter Haiyan at the makes Leyte Island,landfall the at the storm Leyte surges Island, of more the storm than 2.0surges m pile of upmo inre thethan San 2.0 Pedrom pile Bay up andin the hit San Tacloban, Pedro Palo,Bay and and hit Tanauan. Tacloban The, Palo, pattern and of Tanauan computed. The storm pattern surges of computed is similar to storm that predictedsurges is bysimilar HWM to (seethat Figurepredicted6d) butby HWM lower. In(see the Figure results 6d) driven but lower. by WRF-ARW In the results 1 km, driven because by of WRF the - moreARW south 1 km predicted, because of storm the track,more south the storm predicted surges s oftorm more trac thank, the 2.0 storm m first surges hit Dulag of more and Macarthurthan 2.0 m (seefirst Figure hit Dulag 13b,c). and After Macarthur Haiyan (see makes Figure landfall 13b, atc). theAfter Leyte Haiyan Island, makes the storm landfall surges at the of Leyte more thanIsland, 2.0 the m occurstorm in surges the San of Pedromore than Bay (see2.0 m Figure occur 13 ind). the San Pedro Bay (see Figure 13d).

FigureFigure 12.12. Snapshots of of the the computed computed storm storm surges surges driven driven by by WRF WRF-ARW-ARW 3 km 3 km at atthe the times times of: of:(a) (a01:00) 01:00 UTC UTC 8 November 8 November 2013; 2013; (b) ( b02:00) 02:00 UTC UTC 8 November 8 November 2013; 2013; (c) (03:00c) 03:00 UTC UTC 8 November 8 November 2013; 2013; (d) (d04:00) 04:00 UTC UTC 8 November 8 November 2013. 2013. The The color color shading shading indicates indicates the thestorm storm surge surge in m, in and m, and the theblack black line linedepicts depicts the thecoastline coastline of the of storm the storm surge surge model. model. The arrows The arrows depict depict the 10 the-m 10-m winds winds of WRF of WRF-ARW-ARW 3 km 3per km every per every 15 grids 15 grids over over the the domain. domain. These These snapshots snapshots have have been been zoomed zoomed in in from the the original original computationalcomputational domain. domain.

WaterWater2020 2020, ,12 12,, 3326 x FOR PEER REVIEW 1919 of of 27 27

Figure 13. Snapshots of the computed storm surges driven by WRF-ARW 1 km at the times of: Figure 13. Snapshots of the computed storm surges driven by WRF-ARW 1 km at the times of: (a) (a) 02:00 UTC 8 November 2013; (b) 03:00 UTC 8 November 2013; (c) 04:00 UTC 8 November 2013; 02:00 UTC 8 November 2013; (b) 03:00 UTC 8 November 2013; (c) 04:00 UTC 8 November 2013; (d) (d) 05:00 UTC 8 November 2013. The color shading indicates the storm surge in m, and the black 05:00 UTC 8 November 2013. The color shading indicates the storm surge in m, and the black line line depicts the coastline of the storm surge model. The arrows depict the 10-m winds of WRF-ARW depicts the coastline of the storm surge model. The arrows depict the 10-m winds of WRF-ARW 1 km 1 km per every 15 grids over the domain. These snapshots have been zoomed in from the original per every 15 grids over the domain. These snapshots have been zoomed in from the original computational domain. computational domain. In Figures 14 and 15, the maximum computed storm surges driven by WRF-ARW 3 km and WRF-ARWIn Figure 1 kms 14 are and illustrated 15, the maximum and the maximum computed computed storm surges surge driven heights by along WRF the-ARW coasts 3 km of theand LeyteWRF- IslandARW 1 and km the are Samar illustrated Island and are comparedthe maximum to the computed measured surge field surveyheights dataalong [33 the]. In coasts Figure of 14 thec, theLeyte maximum Island and storm the surgesSamar ofIsland more are than compared 2 m driven to the by measured WRF-ARW field 3 km survey are found data [ in33 Tacloban,]. In Figure Palo, 14c, Tanauan,the maximum and Basey,storm whichsurges of more them than are inside 2 m driven the San by PedroWRF-ARW Bay. The3 km pattern are found is similar in Tacloban, to the Palo, one drivenTanauan, by HWM;and Basey, however, which the of amplitudethem are in ofside maximum the San computed Pedro Bay storm. The surgespattern inside is similar the Santo the Pedro one Baydriven is almost by HWM; twice however, lower. Since the amplitude lower maximum of maximum computed computed storm storm surges su arerges predicted, inside the the San results Pedro cannotBay is matchalmost perfectly twice lower. with Since the field lower survey maximum data (see computed Figure 14 storma,b). In surges the result are drivenpredicted, by WRF-ARW the results 1cannot km, the match prominent perfectly computed with the maximum field survey storm data surges (see ofFigure more 14 thana,b). 2 In m the are result also found driven on by the WRF east- coastARW of 1 Leytekm, the Island prominent such as computed Dulag, Macarthur, maximum and storm Abuyog surges (see of Figuremore than 15b). 2 Asm are expected, also found the results on the cannoteast coast match of Leyte well with Island the such field as survey Dulag, data Macarthur, as well (see and Figure Abuyog 15a,c). (see Figure 15b). As expected, the resultsIn Figurecannot 16match, the well time with series the offield computed survey data storm as surgeswell (see in Figure the numerical 15a,c). gauges are shown. Since both of WRF-ARW 3 km and WRF-ARW 1 km predict slower storm forward motion than the best track, the arrival time of peak storm surges occur behind those in the HWM’s results. In the stations in the inner of the San Pedro Bay such as Tacloban and Basey, the results driven by WRF-ARW 3 km and WRF-ARW 1 km have the similar patterns. In these stations, the peak predicted storm surges driven by WRF-ARW 3 km are slightly higher than those by WRF-ARW 1 km; however, both of them are still lower than those driven by HWM. For stations such as Abuyog, Macarthur, and Dulag, because WRF-ARW 1 km predicts the further south landfall location at the Leyte Island, the winds blow toward these regions and cause storm surges about 0.8–1.0 m higher than those in WRF-ARW 3 km. Moreover, in these stations, the differences of maximum storm surges driven between HWM and WRF-ARW are not as prominent as those located in the San Pedro Bay.

Water 2020, 12, 3326 20 of 27 Water 2020,, 12,, xx FORFOR PEERPEER REVIEWREVIEW 20 of 27

Figure 14. (a,b) comparison between maximum computed storm surges and field survey data on Figure 14. ((a,,b)) comparison between maximum computed storm surges and field survey data on along the coasts; (c) maximum computed storm surges driven by WRF-ARW 3 km. In Panels (a) and along the coasts; (c)) maximum computed storm surges driven by WRF--ARW 3 km.. InIn Panels (a)) andand (b), the black dots imply the measured water level by field survey [33], and the blue lines indicate ((b),), thethe blackblack dotsdots implyimply thethe measuredmeasured waterwater levellevel byby fieldfield surveysurvey [[33],], andand thethe blueblue lilines indicate numericalnumerical results results along along the the coasts. coasts. The The y-axis y--axis ofof Panel (a (a)) )andand and thethe the x--axis x-axis of Panel of Panel (b)) correspondcorrespond (b) correspond toto to the geographicalthethe geographicalgeographical coordinate coordinatecoordinate ofofof Panel Panel ((c ().).c). InIn In Panel Panel (c),), (tthec), color the color shading shading indicates indicates the maximum the maximum storm storm surgesurge distribution in in m, m, and and solid solid blac blackk lines lines present present the coastline. the coastline. The dashed The dashed black lines black show lines the show the contourcontour of of maximum maximum computed computed storm storm surges. This This figure figure has has been been zoomed zoomed in in from from its its original original computationalcomputational domain. domain..

FigureFigure 15. (a ,b 15.) comparison((a,,b)) comparison between between maximum maximum computed computed storm storm surges surges and and ﬁeld field survey survey data data on on along the coasts;along (c the) maximum coasts; (c)) computedmaximum computed storm surges storm driven surges by driven WRF-ARW by WRF 1--ARW km. 1 In km Panels.. InIn Panels (a,b), (a the)) andand black dots imply((b),), thethe the blackblack measured dotsdots implyimply water thethe level measuredmeasured by ﬁeld waterwater survey levellevel [33 byby], fieldfield and thesurveysurvey blue [[33 lines]],, andand indicate thethe blueblue numerical lineslines indicateindicate results along thenumerical coasts. results The y-axis along the of Panelcoasts..( Thea) and y--axis the of x-axis Panel of(a)) Panel andand thethe (b x)-- correspondaxis of Panel to(b)) the correspondcorrespond geographical toto the geographical coordinate of Panel (c). In Panel (c), the color shading indicates the maximum storm coordinatethe geographical of Panel (c). coordinate In Panel (ofc), Panel the color (c). In shading Panel (c), indicates the color theshading maximum indicates storm the maximum surge distribution storm surge distribution in m, and solid black lines present the coastline. The dashed black lines show thethe in m, and solid black lines present the coastline. The dashed black lines show the contour of maximum

computed storm surges. This ﬁgure has been zoomed in from its original computational domain. Water 2020, 12, x FOR PEER REVIEW 21 of 27

contour of maximum computed storm surges. This figure has been zoomed in from its original computational domain.

In Figure 16, the time series of computed storm surges in the numerical gauges are shown. Since both of WRF-ARW 3 km and WRF-ARW 1 km predict slower storm forward motion than the best track, the arrival time of peak storm surges occur behind those in the HWM’s results. In the stations in the inner of the San Pedro Bay such as Tacloban and Basey, the results driven by WRF-ARW 3 km and WRF-ARW 1 km have the similar patterns. In these stations, the peak predicted storm surges driven by WRF-ARW 3 km are slightly higher than those by WRF-ARW 1 km; however, both of them are still lower than those driven by HWM. For stations such as Abuyog, Macarthur, and Dulag, because WRF-ARW 1 km predicts the further south landfall location at the Leyte Island, the winds blow toward these regions and cause storm surges about 0.8–1.0 m higher than those in WRF-ARW Water3 km2020. Moreover,, 12, 3326 in these stations, the differences of maximum storm surges driven between 21HWM of 27 and WRF-ARW are not as prominent as those located in the San Pedro Bay.

Figure 16. (a–i) Time series of the computed storm surges driven by WRF-ARW 3 km (blue lines), Figure 16. (a–i) Time series of the computed storm surges driven by WRF-ARW 3 km (blue lines), WRF-ARW 1 km (red lines) and HWM (dashed black lines) in the numerical gauges. The detailed informationWRF-ARW of1 km numerical (red lines) gauges and canHWM be found (dashe ind Figureblack 2lines)b and in Table the 2numerical. The x-axis gauges indicates. The thedetailed time frominformation 12:00 UTC of numerical 7 November gauges 2013 can to 12:00 be found UTC in 8 NovemberFigure 2b and 2013. Table 2. The x-axis indicates the time from 12:00 UTC 7 November 2013 to 12:00 UTC 8 November 2013. Storm surge modeling driven by both WRF-ARW 3 km and WRF-ARW 1 km cannot reproduce obviousStorm storm surge surges modeling in the Sandriven Pedro by both Bay. ForWRF WRF-ARW-ARW 3 km 3 km,and theWRF predicted-ARW 1 stormkm cannot track reproduce is close to theobvious JMA’s storm best track; surges however, in the San WRF-ARW Pedro Bay. 3 km For underestimates WRF-ARW 3 thekm, wind the predicted intensity. storm Despite track WRF-ARW is close 1to km the givesJMA’s comparable best track; however, higher wind WRF intensity,-ARW 3 km it predictsunderestimates relatively the southwind intens stormity. track Despite of Haiyan, WRF- whichARW 1 makes km gives the underestimationcomparable higher of stormwind intensity, surges in it the predicts San Pedro relatively Bay. Moreover, south storm both track of WRF-ARW of Haiyan, 3which km and makes WRF-ARW the underestimation 1 km predict slowerof storm storm surges forward in the motion, San Pedro which Bay. also Moreover, aﬀects the both storm of surgeWRF- calculation.ARW 3 km and Next, WRF the-ARW eﬀects 1 ofkm the predict storm slower track andstorm the forward storm forwardmotion, which speed also on generating affects the stormstorm surgessurge calculation. in the San Pedro Next, Bay the andeffects the Leyteof the Gulfstorm will track be explored.and the storm forward speed on generating storm surges in the San Pedro Bay and the Leyte Gulf will be explored. 3.4. Experiment for Storm Forward Speed

In previous sections, the storm surge modeling driven by HWM, WRF-ARW 3 km, and WRF-ARW 1 km presents the prominent diﬀerences due to the deviations of predicted storm tracks and the forward speed of storm. Tajima et al. (2016) [38] and Kumagai et al. (2016) [19] have conducted a series of sensitivity analysis on the variations of storm track and the storm forward speed to the storm surges in the San Pedro Bay. Tajima et al. (2016) [38] found that Haiyan’s storm track seems to be the worse one for the regions near Palo and Tanauan. Besides, they pointed out that maximum storm surge at Tacloban will be reduced by about 45% if the forward speed of Haiyan is decreased by 50%. Kumagai et al. (2016) [19] indicated that the storm surges near Dulag will increase if Haiyan’s storm track become more south, but storm surges in the San Pedro Bay will reduce. Both of these studies correspond to our ﬁndings in the storm surge modeling driven by HWM, WRF-ARW 3 km, and WRF-ARW 1 km. Water 2020, 12, 3326 22 of 27

In this section, the numerical experiment focused on a storm forward speed is carried out since only few stations have been explored [38] and a constant forward speed was assumed [19]. Because Haiyan’s storm surge simulation affected by the storm-track variations [19,38] and the radius of maximum winds have been explored carefully [18], these discussions will not be included here. In this numerical experiment, besides the original storm forward speed, this study includes two comparable cases: forward speed increased by 10% and decreased by 10%. The 10-m winds and the sea-level pressures in this experiment are from HWM. To analyze the hydrodynamical response of storm surges to a storm forward speed, the wind asymmetry modified by a storm forward motion is considered by the original forward speed for the sake of simplicity. The time series of storm surges in the numerical gauges are illustrated in Figure 17. In the stations located inside the San Pedro Bay such as Tacloban, Basey, Palo, and Tanauan, the maximum computed storm surges are intensified by a faster storm forward speed (see Figure 17a,d,e,h). On the other hand, by considering the slower storm forward speed, the maximum computed storm surges are reduced. However, in the stations outside the San Pedro Bay such as Balangiga and Abuyog, the maximum computed storm surges are reduced in the case with the faster storm forward speed (see Figure 17b,g). The statistical results of the maximum computed storm surges are tabulated in Table4. From Table4, considering the storm forward speed increased by 10%, the extreme values of storm surges in the stations inside the San Pedro Bay such as Tacloban and Tanauan are intensified by 4.5–7.0%; however, the reductions of 5.3–6.8% on the maximum values are shown when considering Water 2020, 12, x FOR PEER REVIEW 23 of 27 the slower forward speed.

Figure 17. (a–i) Time series of the computed storm surges in the numerical experiment on a storm Figure 17. (a–i) Time series of the computed storm surges in the numerical experiment on a storm forward motion: original speed (black lines), increased forward speed (red lines), and decreased forward motion: original speed (black lines), increased forward speed (red lines), and decreased forward speed (blue lines) in the numerical gauges. The detailed information of numerical gauges can forward speed (blue lines) in the numerical gauges. The detailed information of numerical gauges can be found in Figure2b and Table2. The x-axis indicates the time from 12:00 UTC 7 November 2013 to 12:00be found UTC in 8 Figure November 2b and 2013. Table 2. The x-axis indicates the time from 12:00 UTC 7 November 2013 to 12:00 UTC 8 November 2013.

4. Conclusions This study has explored the discrepancies of the storm surge predictions driven by a parametric wind (PW) model and a numerical weather prediction (NWP) model. By shedding light upon the discrepancies, the storm surges of 2013 Typhoon Haiyan in the Leyte Gulf and the San Pedro Bay have been studied. The frictional-free, steady-state, and geostrophic-balancing solutions on the storm winds and sea-level pressures given by the Holland wind model (HWM) have been first verified against the observed data at the Guiuan weather station. The 10-m winds predicted by WRF-ARW (Weather Research and Forecasting-Advanced Research WRF), which solves the 3D time-integrated, compressible, and non-hydrostatic Euler equations, have been compared with the ones by HWM. In the results of computed storm surges driven by HWM, the storm surges of more than 4.5 m have been found in the San Pedro Bay due to the water piling up and the pattern is agreed well with the field survey data. However, in the results by both WRF-ARW 3 km and WRF-ARW 1 km, the maximum storm surge in the San Pedro Bay is almost half than that driven by HWM. The significant differences among HWM, WRF-ARW 3 km, and WRF-ARW 1 km are caused by the predicted landfall location of Haiyan, the storm intensity, and the storm forward speed. The numerical experiment focused on the storm forward speed has pointed out that the extreme values of storm surges inside the San Pedro Bay can be intensified by 4.5–7.0% when the storm forward speed is increased by 10%. This study hopes to pave the way for future research in understanding the discrepancies in predicting storm surges driven by either of the PW or NWP models and thus benefiting storm surge predictions in a semi-enclosed bay.

Water 2020, 12, 3326 23 of 27

Table 4. Maximum computed storm surges in the numerical experiment using original, faster, slower storm forward speed.

Faster Storm Forward Motion Slower Storm Forward Motion ηmax (10% Increased) (10% Decreased) Station Name (m) ηmax,f ηmax,s (ηmax f/ηmax) 1 (ηmax,s/ηmax) 1 (m) , − (m) − Tacloban 4.487 4.701 4.77% 4.259 5.08% − Balangiga 1.985 1.870 5.79% 2.030 2.27% − Marabut 2.55 2.644 3.69% 2.387 6.39% − Basey 4.202 4.498 7.04% 3.914 6.85% − Tanauan 4.069 4.264 4.79% 3.840 5.63% − Dulag 2.798 2.865 2.39% 2.652 5.22% − Abuyog 1.495 1.407 5.89% 1.534 2.61% − Palo 4.804 5.039 4.89% 4.547 5.35% − Macarthur 2.225 2.164 2.74% 2.208 0.76% − −

4. Conclusions This study has explored the discrepancies of the storm surge predictions driven by a parametric wind (PW) model and a numerical weather prediction (NWP) model. By shedding light upon the discrepancies, the storm surges of 2013 Typhoon Haiyan in the Leyte Gulf and the San Pedro Bay have been studied. The frictional-free, steady-state, and geostrophic-balancing solutions on the storm winds and sea-level pressures given by the Holland wind model (HWM) have been first verified against the observed data at the Guiuan weather station. The 10-m winds predicted by WRF-ARW (Weather Research and Forecasting-Advanced Research WRF), which solves the 3D time-integrated, compressible, and non-hydrostatic Euler equations, have been compared with the ones by HWM. In the results of computed storm surges driven by HWM, the storm surges of more than 4.5 m have been found in the San Pedro Bay due to the water piling up and the pattern is agreed well with the field survey data. However, in the results by both WRF-ARW 3 km and WRF-ARW 1 km, the maximum storm surge in the San Pedro Bay is almost half than that driven by HWM. The significant differences among HWM, WRF-ARW 3 km, and WRF-ARW 1 km are caused by the predicted landfall location of Haiyan, the storm intensity, and the storm forward speed. The numerical experiment focused on the storm forward speed has pointed out that the extreme values of storm surges inside the San Pedro Bay can be intensified by 4.5–7.0% when the storm forward speed is increased by 10%. This study hopes to pave the way for future research in understanding the discrepancies in predicting storm surges driven by either of the PW or NWP models and thus benefiting storm surge predictions in a semi-enclosed bay.

Author Contributions: Y.-L.T. performed the storm surge simulations, analyzed the data, and wrote this manuscript. T.-R.W. organized this study and coordinate with all co-authors. C.-Y.L. provided the WRF-ARW results of Haiyan and the insights of the atmospheric background. E.Y. and S.C.L. provided the insights of storm surges. C.-W.L. helped to revise the manuscript and gave comments on this study. All co-authors joined the discussion of this study. All authors have read and agreed to the published version of the manuscript. Funding: The authors would like to thank the project supports from the European Union’s Horizon 2020 Research and Innovation Programme (Grand No. 777536) and Asi@Connect (Grant No. Asi@Connect-18-066). Acknowledgments: The authors would like to thank the four anonymous reviewers about their comments and insights on this study. Conﬂicts of Interest: The authors declare no conﬂict of interest. Water 2020, 12, 3326 24 of 27

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