1

CHAPTER 1

INTRODUCTION

This chapter will give a brief introduction of what storm surges are and what

the potential damage they can cause to a coastal area. Specifically, this study will look

back at the events during the devastation caused by the Typhoon Haiyan-spawned

storm surge which served as the motivation for this study. The main objectives, scope

and delimitations and significance of this study to society will also be presented.

I. BACKGROUND AND MOTIVATION OF THE STUDY

On November 2, 2013, a broad low-pressure area located 425 km east-southeast

of Pohnpei State, Federal States of Micronesia (FSM) become the subject of careful

monitoring by the US military’s Joint Typhoon Weather Center (JTWC). With its

numerical weather forecasting models, they predicted a full-fledged typhoon formation

within the following 72 hours. On the morning of November 3, the low-pressure area,

now located 469 km east-southeast of Chuuk State, Federal States of Micronesia,

intensified and was promptly classified as a tropical depression by both the JTWC and

the Japan Meteorological Agency (JMA). Further intensification led both the JTWC

and JMA to re-classify the weather disturbance, now at 1535 km east-southeast of Yap

State, FSM, as a tropical storm at 0000 UTC November 4, 2013, with JMA giving it

the name “Haiyan”. The storm continued to gain strength as it was aided by the

unusually warm waters at the western North Pacific Ocean having an estimated sea

surface temperature between 29.5 to 30.5 °C. The formation of an eye prompted the

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JTWC to upgrade the storm as a tropical cyclone on 0000 UTC November 5, 2013,

with one-minute maximum sustained winds estimated at 165 kilometers per hour.

Typhoon Haiyan underwent further intensification on November 6 and was

formally labelled a super typhoon by JTWC that day, reaching Category 5 status on the

Saffir-Simpson hurricane wind scale, with a 15 km-wide eye. The typhoon also entered

the Philippine area of responsibility that day and was named “Yolanda” by the

Philippine Atmospheric, Geophysical and Astronomical Services Administration. Peak

intensity was achieved at 1200 UTC November 7, 2013, with one minute sustained

winds of 315 kph and a barometric pressure of 895 hPa, making it the strongest storm

recorded in terms of wind speed. Satellite measurements by the National Oceanic and

Atmospheric Administration estimated the typhoon’s minimum central pressure

between 858 and 884 hPa.

Typhoon Haiyan first made landfall in the Philippines at 2040 UTC November

7, 2013 in Guiuan, Eastern Samar. After crossing the Leyte Gulf, it made landfall at

2300 UTC in Tacloban City, Leyte, with the northern eyewall, the strongest part of the

storm, unleashing its wrath on the city, with an estimated central pressure of 888 hPa.

The storm made further landfalls in Ormoc City at 0006 UTC November 8, 2013,

passed over the Cebu Strait and made landfall at Daanbantayan, Cebu City at 0133

UTC, Bantayan Island at 0240 UTC, and Concepcion, Iloilo at 0400 UTC, where it

weakened into a Category 4 storm, due to its interaction with the mountainous terrain

of the Eastern Philippines. Haiyan then passed over the Mindoro Strait and made its

final Philippine landfall at Busuanga, Palawan at 1200 UTC, finally leaving the

country later that day (JTWC, 2013). It made a final landfall in Haipong, Thailand on

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2100 UTC November 10, 2013, before dissipating on 1200 UTC November 11, 2013

over Guangxi Province, China (JMA, 2013; University of Wisconsin, 2013, Buchanan

et. al, 2013).

The super typhoon caused 6,340 deaths in the country, prompting both local

and international news agencies to call it the deadliest to yet hit the Philippines. An

estimated 1,472,251 families were also directly affected, with around 918,261 of these

left homeless. The national government also estimated the economic losses caused by

the storm at around $18.6 billion, with agricultural losses comprising 85%. (BBC,

2013; NDRRMC, 2013; Gotilano, 2014). Rehabilitation efforts at the area are now

ongoing, assisted by the generous financial aid given by foreign countries to the

Philippine government, aimed at restoring basic utilities, reestablish damaged

infrastructure and provide housing and employment to the displaced evacuees.

Storm surges spawned by the intense winds reached heights of 5-6 m at certain

areas according to Philippine Atmospheric Geophysical and Atmospheric Services

Administration (PAGASA), the Philippines’ national weather bureau, with the terminal

building of Tacloban Airport being obliterated by a recorded 5.2 m wave. These waves

relentlessly battered the coast of Leyte and caused extensive damage due to inland

flooding which reached as far as 1 km along Leyte’s eastern seaboard, as per the Daily

Telegraph (2013). It was also blamed as the primary reason for the loss of lives and

infrastructure, with local government units estimating that over 90% of the province

was severely affected by Typhoon Haiyan.

Analysis of storm surges is primarily done by meteorologists using numerical

methods. These models obtain data from weather satellites, and by using complex

4

programs and algorithms, give as output different weather parameters. Those that are

most crucial to storm surge formation are then used to map out the surge path, height

and inland distance through data plotting software. There are plethoras of software that

can perform this task, however, this study will use the Weather Research and

Forecasting model coupled with the Advanced Circulation Model (WRF-ADCIRC) for

the surge maps. A more detailed description is given in the chapter on methodology.

II. STATEMENT OF THE PROBLEM

Post-Haiyan, a proper characterization of the storm surge caused by the

supertyphoon was one of the utmost interests of meteorologists worldwide. NOAA

(2014) defines a storm surge as an unusually high water level rise caused by extreme

winds and low pressure from the storm that is independent of the daily solar and lunar

tides. These surges are primarily caused by intense winds from the typhoon and to a

lesser extent low pressure from the storm. The powerful wind forces the water into the

helpless coastline, causing massive flooding inland, amplifying the effects of the

astronomical tides. Surge intensity or duration is dependent on many other factors,

such as wind speed, storm size, coastline shape and coastline terrain, all of which can

starve or feed it of its power.

However, since a storm surge is a relatively uncommon occurrence, brought

about by the more powerful super typhoons, the Filipino layperson, especially those

residing near the coast who are most at risk when such a phenomena occurs, is often

unaware of the dangers they pose. They are often caught off-guard or ill-prepared,

which led to the tragic loss of life in the case of Typhoon Haiyan.

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III. OBJECTIVES OF THE STUDY

The general objective of this study is to use a coupled Weather Research and

Forecasting (WRF) – Advanced Circulation Model (ADCIRC) model to characterize

the storm surge caused by Typhoon Haiyan. The simulation displayed the wave heights

and wind speeds at specified weather recording stations and plot out time series graphs

for both parameters respectively. Additionally, water elevation and wind velocity

contour maps were shown over the entire domain. This was carried out through the

following specific objectives:

1. Carry out a WRF simulation over a two-week period spanning the duration and

aftermath of Typhoon Haiyan and extract surface wind and surface pressure data to be

used as inputs for the two-dimensional, depth-averaged hydrodynamic model

ADCIRC.

2. Interpolate the WRF wind and pressure data into ADCIRC as meteorological forcing

and simulate the storm surge caused by Typhoon Haiyan in the Philippines,

specifically over the coast of Leyte.

3. Post-process the ADCIRC output into NUMCAT’s MATLAB-based visualization

tools in order to show the water elevation and wind velocity caused by the storm surge.

4. Compare the model data with that of previous studies that used numerical analysis of

the Haiyan storm surge, as well as with standard tidal databases that used data recorded

from existing meteorological stations.

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IV. SCOPE AND DELIMITATIONS OF THE STUDY

The study is primarily focused on characterizing the storm surge caused by

Typhoon Haiyan off the eastern seaboard of the Philippines, especially around the

province of Leyte. For the WRF simulation, input data will be taken from the National

Center for Environmental Protection (NCEP) Final (FNL) reanalysis meteorological

data. The resolution chosen for the FNLs were of the one degree by one degree (110

km by 110 km) type for a more accurate depiction of meteorological conditions.

The time frame chosen for the study was over a period two weeks from 0000

UTC 1 November 2013 until 0000 UTC 16 November 2013. The two-week period

covers a week prior to the storm, the four-day landfall, and three days post-Haiyan. A

resolution of 5 m was also used in the WRF simulation. The extra time period prior to

and after the storm was selected to verify the reliability of the coupled WRF-ADCIRC

model in measuring the pre and post-Haiyan weather conditions. The WRF output

surface wind and pressure data were used as meteorological forcing for ADCIRC.

For the ADCIRC simulations, wave forcing was not included in the simulation

since the storm surge heights are the primary data of interest in this study. Coastal

inundation due to Typhoon Haiyan was also not included in the study, although a brief

introduction is presented in Chapter 2. The bathymetric data used for this study was

obtained from NOAA’s ETOP01 Global Relief Model, having a resolution of 1-arc

minute (2 km), while the high resolution coastline data for this study was obtained

from the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG)

database, which was the basis for the formation of the finite element mesh (fort.14)

defining the domain, which in this study encompassed the Eastern Philippine region.

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Three primary input files are required by ADCIRC for a basic storm surge

simulation: the Grid and Boundary Information File (fort.14), the Model Parameter

And Boundary Condition File (fort.15) and the Single File Meteorological Forcing

Input File (fort.22). The output files of interest that will be analyzed in this study

comprise the Water Elevation At Specified Stations (fort.61), Depth-Averaged Wind

Velocity At Specified Stations (fort.62), Water Elevation At All Nodes In The Grid

(fort.63) and Wind Velocity At All Points Of The Grid (fort.64) (Luettich &

Westernink, 2012). Other ADCIRC output files, such as the harmonic analysis files,

are beyond the scope of this study. A detailed explanation of the various input and

output files will be presented in the Methodology chapter.

To facilitate the creation of the input files, as well as the post-processing and

visualization of these output files, a MATLAB-based software provided by the US

Navy called Navy Unstructed Mesh Creation And Editing Toolkit (NUMCAT) was

utilized in this study (Blain et. al 2008). However, NUMCAT is only limited to these

function, as the actual ADCIRC execution (./adcirc and ./padcirc) was performed on

the Ubuntu Terminal window using source code obtained from Crystal Fulcher of the

University of North Carolina (Luettich & Westernink, 2012).

V. SIGNIFICANCE OF THE STUDY

This research is important for a more complete understanding of storm surges

that occur in the Philippine archipelago, which is prone to devastating storms due to its

location. By understanding the meteorological factors that cause a surge, scientists

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such as Amadore (2013) opine that coastal areas are often all too ill-equipped to

withstand the might of a surge. Understanding storm surge intensity, maximum height

and potential extent of inland flooding can aid in the re-engineering and reinforcing

existing or future infrastructure, thus saving countless properties from being destroyed

in the future. Other ideas that are being tossed around to minimize surge damage are

mangrove plantations or concrete seawalls at the coast that can be strategically located

using data obtained from related research being conducting on vulnerable coastal areas.

Although several studies (Lee & Yamashita, 2013; Paringit, 2014) have been

done post-Haiyan in an attempt to characterize storm surge heights, very few studies

have evaluated the performance of ADCIRC as a viable numerical weather prediction

model for Philippine bathymetry and topography. Another reason for the importance of

this study is that it can also help validate the use of numerical weather prediction

models, such as ADCIRC, for the Philippine setting.

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CHAPTER 2

THEORETICAL BACKGROUND

A proper understanding of storm surges entails a proper understanding of the

meteorological factors that cause it. A successful run of WRF will provide over 90

different output files, each corresponding to a particular meteorological condition. The

primary factors causing storm surge, namely surface winds and surface pressure, must

be extracted for meteorological forcing on ADCIRC (Heo, 2009; Ning et al.; 2010;

Lee, 2013). A concise discussion on the physical nature of surface winds and surface

pressure will be presented in this chapter, as well as the mathematical equations that

generalize their behavior.

Furthermore, Laplace’s dynamic tidal theory will also be discussed, including

their mathematical representations. Laplace’s tidal equations has important

implications in the computation of tidal elevation and a proper understanding of tides is

important to understand what astronomical and oceanic forces affect tides. The

Generalized Wave Continuity Equation, which ADCIRC solves in order to provide

water elevation and wind speeds at the u and v directions will also be given a brief

treatment. Equations describing the factors affecting storm surge height and

development will also be presented in this chapter.

I. SURFACE WINDS

A realistic representation of the equations behind wind flow will end up being

quite complex. The reason for this is that there are many factors that affect observable

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wind patterns. Fortunately, a simplified equation containing the most essential

interactions between the pressure gradient force, Coriolis force and a retarding friction

force that opposes the wind, decelerating it in the process. These equations are:

𝑢 =−𝑘𝑃𝑥−𝑓𝑃𝑦

𝜌(𝑘2+𝑓2) (2.1)

𝑣 =−𝑘𝑃𝑥+𝑓𝑃𝑦

𝜌(𝑘2+𝑓2) (2.2)

where u is the zonal wind, defined as the wind speed with unit ms-1

moving in the

west-to east direction, v is the meridional wind, defined as the wind speed with unit

ms-1

moving in the south-to north direction, k is the surface resistance (frictional force

against the wind, with a value of 1.5x105

s-1

at the low-latitude tropical ocean), f is the

Coriolis force (given by 2Ωsin(Φ), where Ω=7.292x10-5

rad s-1

and Φ is any latitude on

Earth), ρ is the air density (taken to be 1.225 kg m-3

at the surface), Px is the pressure

gradient in the west-to east direction, and Py is the pressure gradient in the south-to-

north direction.

It must be noted that these equations for zonal and meridional winds are

generally true only for areas in the lower altitudes, specifically tropical oceans (Ward,

Indeje, Ndiaye & Sun, 2003). As such, these mathematical representations are

particularly applicable in the Philippine setting. These meteorological factors that

influence wind flow will be discussed in a little more detail in the succeeding sections.

A. CORIOLIS FORCE

A storm surge is a result of a cyclone, which in its simplest sense is a more

intensified low pressure area. This low pressure area tends to attract wind towards it.

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However, as a result of the Coriolis Effect, this wind will be directed perpendicular to

its original direction. Equilibrium is obtained by the pressure gradient force, which acts

from higher pressure to the low pressure area, opposing the Coriolis force, which acts

away from the low pressure area. (Barry & Chorley, 2003) Since these counteracting

forces come in from all directions, the equilibrium will be circular in shape. It is also

interesting to note that the Coriolis force always deflects velocity perpendicularly and

to the right. Since the earth rotates to the east, the cyclones have a clockwise motion in

the Northern Hemisphere, and counterclockwise in the South (the South being a mirror

image of the North) (Taylor, 2005).

B. PRESSURE GRADIENT

The pressure gradient is the rate of pressure change, as well as the direction of

said variation around a point or location in question. For the Earth’s atmosphere, it is a

two-dimensional vector that points downward. In the Earth’s surface, especially at sea-

level, the direction is more specifically from high pressure to low pressure. It is also

one of the primary forces that influence wind circulation and direction (Lorenz, 1967;

Fleagle & Businger, 1980; Wallace & Hobbs, 2006). Mathematically, it is represented

by the equation:

𝛻𝑃 = 𝜌𝑔 (2.3)

where P=pressure gradient, ρ=ρ(r) is the fluid density at gravitational equipotential

over a point r, g=g(r) is the gravitational field strength at point r, and r= position with

respect to the fluid density and gravitational field.

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C. SURFACE RESISTANCE

In the context of thermodynamics, surface resistance is also known as fluid

resistance, which is friction exerted by a fluid against the movement of another object

(which in this study is wind). In the case of wind travelling at high speeds, this force is

known as drag and increases approximately proportional to the square of velocity

(Young, Freedman & Ford, 2006). In equation form:

FD =1

2𝜌𝑣2𝐶D𝐴 (2.4)

where FD is the drag force, ρ is the fluid density, v is the velocity of the object with

respect to the fluid, A is the object’s cross-sectional area, and CD is the dimensionless

drag coefficient. The drag coefficient is not constant, and in the case of tropical

cyclones, has been shown in previous studies by Powell, Vickery & Reinhold (2003)

and Smith, Montgomery & Thomsen (2013) to be dependent on wind speed and

surface roughness.

D. ZONAL WINDS

The zonal wind is a west-to-east wind that is dependent on the vorticity

(rotating motion of a fluid relative to itself) throughout the Earth’s atmosphere. Zonal

winds are also affected by the latitude at which it is located and was mathematically

related by Arakawa (1952) to its speed. They play a major role in general wind

circulation, specifically quasi-stationary waves and the generation of transient

disturbances. It also influences the El Niño Southern Oscillation phenomenon and day

length. This is due to the fact that zonal winds are partially responsible for atmosphere

angular momentum variations (Nigam, 1990).

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E. MERIDIONAL WINDS

Meridional winds, also known as Rossby-gravity waves, have been shown in a

study by Dunkerton & Baldwin (1995) to be dependent on outgoing longwave

radiation and convection in the tropospheric level. Tropical depression disturbances

were also revealed as another source of these waves. Solar flux, which is heavily

influenced by ionosphere activity, also plays an important role in meridional wind

direction and velocity. The meridional wind drifted northward at a more pronounced

pattern with a stronger solar flux (Abdu et al, 2010). The northward flow is due to

magnetic interaction in the upper atmosphere, aligning it with the magnetic north pole

of the earth (Siscoe & Finley, 1969).

II. SURFACE PRESSURE

A. BAROMETRIC EQUATION

Surface pressure is described by the atmospheric pressure over a particular

geographical location in the Earth and is directly correlated to the air mass at the height

of the particular location in question. It is mathematically represented by the

barometric formula, given as follows:

𝑃 = 𝑃𝑜𝑒−𝑀𝑔𝑧

𝑅𝑇 (2.5)

where PO is the sea-level pressure, M is the molar mass of Earth's air, g is the

gravitational acceleration, z is the elevation height (above sea level, in m) R is the gas

constant (8.31432 N·m /(mol·K)) and T is the temperature. This equation can be

derived from the ideal gas law by assuming pressure is hydrostatic (without stress) and

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integrating the resulting differential equation using separation of variables over the

altitude z (Berbaran-Santos, Budonov & Pogliani, 1997).

B. SEA-LEVEL PRESSURE

Sea level pressure, according to Shodor (1996), is often the desired surface

pressure quantity in modelling a storm surge as they generally occur at an altitude near

or exactly sea level. One can solve for sea level pressure by simply rearranging the

equation above as follows:

𝑃𝑜 = 𝑃 ∗ 𝑒𝑥𝑝 (𝑀𝑔𝑍𝑔

𝑅𝑇) (2.6)

where it is now convenient to represent p as the surface pressure and po is now the sea-

level pressure. Since surface pressure and sea-level pressure are inversely proportional

to each other, pressure reports in weather forecasts are standardized using sea-level

pressure. This is because weather stations are often at varying altitudes and

temperatures which can affect their accuracy.

III. LAPLACE’S TIDAL EQUATIONS

A mathematical description of astronomical tides was formulated by Pierre-

Simon Laplace during 1776 using linear partial differential equations (presented below

in Equations 2.7-2.9), describing them as barotropic two-dimensional sheet flow. The

Coriolis force and lateral gravitational forcing were included by Laplace in his

formulation. The solutions of these equations yield the vertical tidal elevation, as well

as the u and v components of horizontal velocity.

𝛿𝜉

𝛿𝑡+

1

𝑎 cos(𝜑)[

𝛿

𝛿𝜆(𝑢𝐷) +

𝛿

𝛿𝜑(𝑣𝐷𝑐𝑜𝑠(𝜑))] = 0 (2.7)

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𝛿𝑢

𝛿𝑡− 𝑣(2𝛺 sin(𝜑)) +

1

acos(𝜑)

𝛿

𝛿𝜆(𝑔𝜉 + 𝑈) = 0 (2.8)

𝛿𝑣

𝛿𝑡+ 𝑢(2𝛺 𝑠𝑖𝑛(𝜑)) +

1

𝑎

𝛿

𝛿𝜑(𝑔𝜉 + 𝑈) = 0 (2.9)

where D is fluid sheet of average thickness, ς the vertical tidal elevation, as well as the

u & v horizontal velocity components, φ & λ are the latitude and longitude directions,

Ω is the angular frequency of the planet's rotation, g is the planet's gravitational

acceleration at the mean ocean surface, a is the planetary radius, and U is the external

gravitational tidal-forcing potential (Randall, 2007).

These equations form the mathematical backbone of Laplace’s dynamic theory

of tides, which was a more realistic explanation of oceanic interactions with

astronomical tidal forces. The dynamic theory of tides is an improvement of the

Newtonian equilibrium (or static) theory of tides. In the equilibrium theory, Newton

assumed that the spheroid Earth was uniformly covered by water, with the tidal

“bulges” considered evidence of solar and lunar interaction. Laplace explained the

tides more realistically by considering friction, resonance, bathymetry and coastline

shape, as well as the dominance of certain tidal constituents in certain areas

(amphidromic circulation). Satellite measurements have confirmed the accuracy of the

dynamic theory, providing the capability to measure tides up to a few centimeters

(Boyce, 2003).

IV. STORM SURGE PHYSICS

A variety of factors affect the formation and duration of a storm surge, such as

surface pressure, wind speed, wind strength and duration, bottom stress (friction), wave

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fetch toward the coast, wave breaking and bathymetry. For a basin with uniform

bathymetry, the mathematical representation of the sea-level elevation that takes into

account all the aforementioned meteorological factors is:

𝜕𝜂

𝜕𝑥=

1

𝜌𝑔ℎ(𝜏𝑤 + 𝜏𝐵) (2.10)

Δ𝜂 =𝐾Δ𝑥𝜏𝑤

𝑝𝑔ℎ (2.11)

where η is the sea level height, x is the distance perpendicular to the shore, h is the

depth of the water, τw is the surface (wind) stress and τb is the bottom stress, and K is

the bottom friction, and 𝜌 is equivalent to the sum of the bottom friction and wind

stress over the average bathymetric water depth. Equation 2.10 also assumes a steady

state and ignores momentum advection terms. Equation 2.11 is a representation of Eqn.

2.10 in finite difference terms.

Factors affecting the height of the storm surge are the continental shelf’s width

(x) and depth (h), as these two factors are inversely proportional to each other with

respect to water elevation. For a long, shallow shelf, the storm surge will be higher, but

if the shelf is steep and narrow, the storm surge will be smaller. Differences in friction

and bottom stress (wetlands and open oceans) also affect the formation and duration of

the storm surge (Di Liberto, 2009).

Di Liberto (2009) also noted that wave forcing must also be considered in order

to accurately represent a storm surge using numerical methods. The momentum carried

over from ocean waves has a positive effect on surge height. The mathematical

representation is presented in Equation 2.12 below:

17

𝜕𝑆𝑥𝑥

𝜕𝑥= 𝜌𝑔(�̅� + ℎ)

𝜕ℎ

𝜕𝑥= 0 (2.12)

where Sxx is the radiation stress, x is distance perpendicular to the shoreline, ρ is the

density of water, g is gravity, is the difference between still-water level and the water

level in presence of waves and h is the water depth. Wave momentum is also kept

balanced by the concepts of wave set-up and wave set-down. Wave set-down refers to

the condition where 𝜕𝑆𝑥𝑥

𝜕𝑥> 0 and �̅� decreases, assuming that wave heights increase.

On the other hand, wave set-up refers to the condition where 𝜕𝑆𝑥𝑥

𝜕𝑥< 0 and �̅� increases,

assuming that the waves break, resulting in a height decrease. Wave set-up results in a

sea-level height increase and correspondingly, an increase in storm surge heights.

V. GENERALIZED WAVE CONTINUITY EQUATION

ADCIRC obtains the water elevation at all nodes of the finite element mesh by solving

the Generalized Wave Continuity Equation (Eqn. 2.13). Computationally, this is done

by the usage of a consistent or a lumped mass matrix (which are specified in the

compiler flags), as well as implicit or explicit time stepping scheme using variable time

weighting coefficients. A matrix solver is not required if a lumped, fully explicit

formulation is desired for the computations.

∂2 ζ

𝜕𝑡2 + τ0∂ζ

𝜕𝑡+

∂ 𝐽�̃�

𝜕𝑥+

∂ 𝐽�̃�

𝜕𝑦+ (𝑈𝐻)

𝜕𝜏0

𝜕𝑥+ (𝑉𝐻)

𝜕𝜏0

𝜕𝑦= 0 (2.13)

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where

𝐽�̃� = −Qx𝜕𝑈

𝜕𝑥– 𝑄𝑦

𝜕𝑈

𝜕𝑦+ 𝑓𝑄𝑦 −

𝑔

2

∂ζ2

𝜕𝑥− 𝑔𝐻

𝜕

𝜕𝑥[

𝑃𝑠

gρ0− αη] +

(My − Dy) +τsx;wind +τsx;waves−τbx

ρ0+ (Mx − Dx) + 𝑉

∂ζ

∂t+

τ0Qx − 𝑔𝐻∂ζ

∂x (2.14)

𝐽�̃� = −Qx𝜕𝑉

𝜕𝑥– 𝑄𝑦

𝜕𝑉

𝜕𝑦+ 𝑓𝑄𝑋 −

𝑔

2

∂ζ2

𝜕𝑦− 𝑔𝐻

𝜕

𝜕𝑦[

𝑃𝑠

gρ0− αη] +

(My − Dy) +τsy;wind +τsy;waves−τby

ρ0+ (My − Dy) +

𝑉∂ζ

∂t+ τ0Qy − 𝑔𝐻

∂ζ

∂y (2.15)

On the other hand, the depth-averaged velocity of the current is obtained from the

solution of the vertically-integrated momentum equations; either in 2DDI or 3DDI.

The 2DDI momentum equation is lumped, rendering a matrix solver unnecessary. For

3D, the vertical mass matrix is not lumped, while vertical diffusion is implicitly

treated. This requires that a complex, tri-diagonal matrix problem be solved over the

vertical of each horizontal node of the finite element mesh. The water levels and

current velocities U and V of the finite element mesh are solved by ADCIRC through

the application of a linear, Lagrange interpolation and computing for three degrees of

freedom for every vertex in the mesh.

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𝜕𝑈

𝜕𝑡+ 𝑈

𝜕𝑈

𝜕𝑥+ 𝑉

𝜕𝑈

𝜕𝑦− 𝑓𝑉 =

𝑔𝜕

𝜕𝑋[ζ +

𝑃𝑠

gρ0− αη] +

τsx;wind +τsx;waves−τbx

ρ0𝐻+

𝑀𝑥−𝐷𝑥

𝐻 (2.16)

𝜕𝑉

𝜕𝑡+ 𝑈

𝜕𝑉

𝜕𝑥+ 𝑉

𝜕𝑉

𝜕𝑦− 𝑓𝑉 =

𝑔𝜕

𝜕𝑦[ζ +

𝑃𝑠

gρ0− αη] +

τsy;wind +τsy;waves−τby

ρ0𝐻 +

𝑀𝑦−𝐷𝑦

𝐻 (2.17)

where 𝑈, 𝑉 ≡ 1

𝐻∫ 𝑢, 𝑣 𝑑𝑧

𝜁

−ℎ are depth-averaged velocities in the x,y direction, 𝑢, 𝑣 are

vertically-varying velocities in the x,y directions, 𝐻 ≡ 𝜁 + ℎ = total water depth, ℎ =

bathymetric depth (distance from the geiod to the bottom), ζ = free surface departure

from the geoid; Qx=UH and Qy=VH are fluxes per unit width; f is the Coriolis

parameter; g is the gravitational acceleration; Ps is the surface atmospheric pressure; ρ0

is the density of water density (used as a standard); η is the Newtonian equilibrium

tidal potential and α is the effective earth elasticity factor; τs,winds and τs,waves are surface

stresses due to winds and waves, respectively; τb is the bottom stress; M are lateral

stress gradients; D are momentum dispersion terms; and τ0 is the GWCE weighting

factor that optimizes the phase propagation properties (Luettich & Westernink, 2004;

Dietrich, 2010).

20

CHAPTER 3

REVIEW OF RELATED LITERATURE

Storm surges are not a novel area of interest and research by meteorologists.

The effects of a surge on a coastal area are often catastrophic, and as such, researchers

have been prompted to investigate what causes such a natural disaster in order to

prevent a tragic loss of life and property. Numerical methods, such as WRF, are a

powerful tool in obtaining a more complete understanding of storm surges. A

collection of these past researches, both in a Philippine and international setting,

involving the use of numerical analysis will be treated briefly in this chapter.

Heo et al. (2009) investigated factors that cause storm surges, specifically sea

level pressure and sea surface wind stress, for more accurate surge forecasts in the

future. Their study, motivated by the storm surge that occurred in Yeonggwang, off the

western coast of Korea, made use of the WRF, MM5 and COAMPS models to

simulate meteorological conditions conducive for a storm surge. The simulations

revealed that high winds were primarily the cause of the storm surge, and were aided in

their development by mesocyclones. Low-level warm advection was hypothesized as

the root of the mesocyclone development and was simulated using the aforementioned

models. WRF was shown to be more consistent in terms of wind speed and

mesocyclone strength, though there was an underestimation of the warm tongue. The

researchers concluded that ocean effects play as important a role in storm surge

development as mesocyclones.

21

I. WRF STORM SURGE STUDIES

A more complete understanding of storm surges in Japan, specifically off its

west coast, was the main objective of Kim et al. (2010). This study was motivated by a

15-hour delay of the maximum surge after Typhoon Songda in 2004 that Japanese

meteorologists inaccurately predicted. Two models were used to simulate the surge:

FM, which largely ignored terrain effects, and the more realistic WRF, which took into

account the terrain in question during the simulation. Two drag coefficients were also

utilized for the simulation, which were the wave dependent drag (WDC) and empirical

drag coefficient (EDC), with the latter used to estimate wind stress. WRF’s

measurement of maximum pressure depression was shown to be relatively close to on-

site measurements, although it was slightly underestimated. Surge height measurement

of WRF was also consistent with those on-site, though the secondary surge heights

were underestimated. The Coriolis force also played a key role for more realistic storm

surge forecasts.

Drew & Han (2009) studied the effect of a storm surge on the Philippines,

especially its two major coastal bays Laguna de Bay and the Manila Bay, and the

potential distance for inland surging, especially in the densely populated Metropolitan

Manila. Using the Weather Research and Forecasting (WRF) model, winds moving

unidirectionally with strength comparable to that of a Category 3 typhoon were applied

in order to simulate surge direction. In a separate simulation, Hurricane Katrina was

numerically made to pass over Manila Bay in place of the more recent Typhoon

Rosing, due to unavailability of data for the latter storm. Both were executed in 1 km

horizontal resolution grids, to provide more accurate surge forecasts. The two

22

simulations were shown to be consistent with their results, although wind direction

analysis projected higher surge heights due to the longer time interval of the wind at a

particular direction. Overall, the typhoon simulation was shown to be sufficient in the

absence of wind analysis. It was noted that tidal effects were not taken into account for

all intents and purposes of the study, but they play an important role, especially in the

Philippines.

A case study on Hurricane Katrina was performed by Ning, Smith, Villarini,

Marchok & Baeck (2010) with an emphasis placed on storm evolution after landfall.

These simulations were performed using WRF, the output of which was coupled with

2D, depth-averaged Advanced Circulation Model (ADCIRC), to investigate the

resulting storm surge at the Chesapeake Bay. The WRF output used for the coupling

was the surface winds and surface pressure data from 18-20 September, 2003. There

was a significant underestimation of the surge at the upper part of the bay, which was

partially due to astronomical tides not being accurately factored into the run. Terrain

effects and winds from the outer rainbands were also plausible reasons for the

miscalculation of the inland surge.

Di Liberto (2009) used Weather Research and Forecasting (WRF) Model

Version 2.1 to simulate the storm surge caused by Hurricane Gloria over New York

City-Long Island. His thesis was motivated by the coastal flooding caused by cyclones

during the hurricane season. A more realistic simulation of Gloria using WRF was

performed using the method of “bogusing” utilizing the observed parameters from the

September 26, 1985 storm instead of the preprogrammed parameters. The wind stress

and surface pressure output was then coupled with ADCIRC to simulate water

23

elevation and current during the storm. The results of the simulation were shown to be

generally consistent with those of NOAA, although surge heights were underestimated

by up to 1.0 m, possibly due to wave forcing not being factored into the runs.

The storm surge caused by Hurricane Ivan over the northeastern Gulf of

Mexico was simulated by Sheng, Zhang & Paramygin (2010). The software used for

the modelling was CH3D (Curvilinear-grid Hydrodynamics in 3D) coupled with the

coastal wave model SWAN, a subprogram of ADCIRC. Data used for the input was

obtained from NOAA, and consisted of the H+ (10 m) wind, and a wind model that

accounted for terrain effects on cyclone wind. The results showed that the coastal surge

height was around 2-3 m, peaking at around 3.5 m. This was also shown to be

consistent with that of observed data, with roughly a 10% error. Wave-induced surge

(secondary surge caused by the initial winds and pressure of the first) was the cause of

around 30% of the peak surge.

` In order to predict and simulate storm surges more accurately, Mattocks et al.

(2010) combined the use of parametric wind models with non-hydrostatic NWP

models (GWAVA parametric wind model fields and H*Wind analyses) to form the

basis of the wind data that would then be interpolated into the Advanced Hurricane

WRF Model. This was done due to the inherent disadvantages present with each wind

generation model, with parametric wind models showing decreased effectivity over

longer-range areas, and the NWP model’s poor typhoon track representation. Gradient

wind asymmetric vortex analysis (GWAVA) was chosen as one of the wind models

because of its ability to account for asymmetry in a storm, as well as surface friction.

Due to problems associated with hurricane path and intensity predictions using

24

numerical simulations, an idealized symmetric Rankine/Holland vortex replaced the

real-time vorticity, geopotential height and velocity perturbations of a storm. The

improved wind data was then forced into ADCIRC, where more accurate storm surge

predictions were made as per the surface water height.

Ebersole et al. (2010) examined the storm surge, as well as the subsequent

flooding, that occurred in St Bernard Polder, Louisiana during the 2005 hurricane

season. The variation in wave height and intensity at different locations at the region

was also among their primary interests. The hurricane winds and storm surge was

modelled using ADCIRC while WAM and STWAVE was used for wave simulation.

The wind speed were shown to increase rapidly as the storm approached land, peaking

at 45 m/s at the height of the storm, up from 10 m/s 24 hours prior to landfall. The

simulation also showed that inland surge heights were around 3.2-5.7 m, peaking at 6.6

m at the coastline. A comparison of these results with previous researches by the Army

revealed a minute 0.47 m deviation, thus corroborating the statistical accuracy of the

study.

East Coast lows off Gold Coast of Australia was analyzed by Golshani,

Thurston, Abbs, Stuart & Tominson (2011), which are intense low pressure areas

primarily caused by warm water from the East Australian current. These ECLs were

capable of spawning hurricane-type winds and flash flooding during the 2009 storm

season at the Gold Coast. The researchers used WRF and Regional Atmospheric

Modelling System (RAMS) to cover an area of 1900 x 1700 km with a horizontal grid

spacing of 4.5 km for the meteorological modelling. The storm surge modelling was

done using MIKE21 HD and MIKE 21 SW, D with a mesh resolution of 20km to

25

0.1km and a mesh size of 2 m to 20 m. WRF predictions from the pressure and wind

output for wave height (including the peak) and direction of the surge were shown to

be accurate than that of RAMS. Tidal forces were also considered in the study, with the

peak tidal height at 2 m and peak surge height at 5 m.

Bowman (2013) conducted a research on the feasibility of ADCIRC in

simulating Hurricane Sandy over the New York Bight coastal areas. Surface wind and

sea level pressure data were acquired from the Weather Research and Forecasting

Model (WRF) and MM5 using a 36 and 12 km grid spacing. The data from the 12 km

nested grid was then used to drive ADCIRC. The model could then be “hot started” to

use the most recent forecast as the basis of the initial water level, however, this often

caused unrealistic predictions. Tides from the 24-hour WRF/MM5 forecast prior to the

intended period of study were used to set the initial water level instead. The WRF

pressure and wind data covering a 12-hour interval were then interpolated into the

ADCIRC grid. The study was concluded with a note that a large-scale study showed

more accurate surge predictions and pressed the need for incorporating data from

weather stations.

Using the Advanced Research Weather and Forecasting Model (WRF-ARW),

Klausmann (2014) proposed that numerical analysis of past storms can provide

invaluable insight for future storm surge studies. To this end, the wind field brought

about by Hurricane Irene during August 27-29, 2011, was simulated using WRF. A

key advantage of WRF in simulating storm surges include its incorporation of physics

into a run, this enables it to generate far field winds, spiral rainband structures, and

supergradient flow, making for more realistic predictions, a fact often left unexploited

26

by researchers. Three grids were employed in the simulation, a 12 km resolution

general grid that used the Kain-Fritsch cumulus parameterization scheme over

northeastern United States, while a 4 km nested grid and a 2 km resolution grid used

convection over North Carolina, which was directly affected. The simulations were

shown to be consistent with observed data for Hurricane Irene, although the 10 m wind

field was higher than that recorded by HWIND. Klausmann also suggested that

experimenting with the different physics options and data assimilation might provide

more accurate forecasts that can be used in further storm surge studies.

II. SWAN+ADCIRC STORM SURGE MODEL

Bacopoulos et al. (2011) conducted a simulation of the winds and storm surge

caused by Hurricane Jeane along the east coast of Florida. Wind speed and direction,

water elevation and current speed and direction were collected at Spessard Holland

North Beach Park and Trident Pier from NOAA. The wind and pressure fields forced

on ADCIRC were from the MORPHOS project using the Interactive Objective

Kinematic Analysis (IOKA) system, with additional wind measurements interpolated

from local weather observation stations. The resolution for the finite element mesh was

500 m at Spessard and 100 m at Trident Pier and was heavily influenced by coastal

topography and bathymetry. The researchers opted not to change any of the default

settings for the initial and boundary conditions, instead leaving them as is. The water

elevation and nearshore currents obtained as ADCIRC output are consistent with those

from observed data, which is remarkable considering that standard settings were used.

The effects of domain size on the accuracy of the ADCIRC storm surge model

was the subject of the study by Blain et al. (2011). Simulations were performed on

27

three domains along the region of the Gulf of Mexico, which was primarily affected by

Hurricane Kate. The first domain covered the area where the most intense storm surge

occurred, while the other two domains comprised the Gulf of Mexico and the

contiguous basins in addition to the Gulf of Mexico, respectively. Results of the

simulation showed that the small domain is insufficient in capturing the storm surge,

with the best results coming from the largest domain encompassing the Gulf of

Mexico. Accuracy was also shown to be increased when the domain included the

western North Atlantic Sea and the Caribbean Sea along with the Gulf of Mexico. The

discrepancies in the data could be explained by the model not adequately capturing the

dependency between the basin resonant modes and the Gulf of Mexico.

Dietrich et. al (2011) described the coupling process involved in the

SWAN+ADCIRC model. SWAN solves for the wave action density spectrum (which

is dependent on the wave frequency and wave direction), while ADCIRC computes the

Generalized Wave Continuity Equation in order to determine the water elevation. Both

models use the same triangular mesh and computations, and are fed with data from

each other. Specifically, SWAN’s radiation stress gradients are forced on ADCIRC,

while the wind speed, water elevation and current direction are forced upon SWAN.

Care was also taken to synchronize the time steps of ADCIRC and SWAN, since

SWAN requires larger time steps compared to ADCIRC. For the purposes of this

study, the researchers performed a validation of the coupled model using the prior

cases of Hurricane Katrina and Rita, with the results showing great accuracy and

efficiency when compared to previous data obtained from the two hurricanes.

28

Ferriera et al. (2014) incorporated the effects of sea-level rise (SLR) to

hurricane-induced storm surges in the lower Texas coast bays. For their study, the

researchers used the two-dimensional depth-integrated Advanced Circulation

(ADCIRC) model coupled with the spectral wave model SWAN, which has the ability

to compute random, short-crested wind-generated waves, with both making use of the

same mesh. For the region of interest, a high resolution mesh (30 m) was used for the

Texas coast, containing 1.3 million nodes and 2.5 million elements. Tidal forcing and

river inflows were neglected for simplification purposes. Wind and pressure field data

was obtained from the planetary boundary layer (PBL). A sea-level rise of 0.5, 1.0, 1.5

and 2.0 m was then interpolated into the storm surge results in order to test how coastal

topography and land cover are affected by SLR. Their study showed that while land

cover only contributes an average increase of 3% surge for SLR in excess of 1.0 m,

this could increase to up to 10% for more intense hurricanes and larger bays.

Kennedy et al. (2014) estimated the possible coastal inundation that may be

brought about by a hurricane for the Hawaiian volcanic islands of Kauai and Oahu,

which feature no continental shelves. The lack of continental shelves, combined with

the rarity of hurricanes in the Hawaiian Islands in general, make it difficult to predict

storm surges, which necessitated this study in order to identify hurricane patterns.

SWAN+ADCIRC was used for the determination of waves and circulation and the 1D

phase-resolving Boussinesq surf zone model for coastal inundation, with the hurricane

data obtained from the Central Pacific Hurricane Center. Although SWAN+ADCIRC

provides wave elevation heights that are consistent with those from weather

observation stations, they overestimate the inland inundation, which is a serious

29

problem for an area like the Hawaiian islands which lacks a continental shelf causing

amplified inundation effects. To correct this, a Boussinesq model was used, in order to

model each of the crests and troughs for more complicated topographies. The results

show that the greatest possible run-up that Kauai may experience would be a few

hundred meters, while the inundation is more widespread for Oahu, placing much

infrastructure in peril.

Sebastiana et al. (2014) characterized storm surge behavior in the Galveston

Bay, Texas region using the coupled SWAN+ADCIRC model. A validation of

Hurricane Ike was first performed covering a 10-day period starting on September 5,

2008, with wind forcing data obtained from NOAA's Hurricane Research Division

Wind Analysis System (H*WIND). The simulation results showed that surge-induced

wave heights reached 7.5 m at the Gulf of Mexico, while for Galveston Bay it was a

considerably lower 2-2.5 m. The researchers also varied the conditions of Hurricane

Ike by increasing wind speed and moving landfall location further inland, both

eastward and westward. The westward shift was combined with the 30% increase in

wind speed caused the highest surge elevation (6.93 m at Galveston Bay and 8.92 m at

Houston Ship Channel). The study successfully highlighted the areas in the Galveston

Bay area most vulnerable to a storm surge which could be used by local authorities in

providing solutions in order to minimize life and property loss.

Mattocks & Forbes (2008) designed a storm surge forecasting system (North

Carolina Forecasting System) to be applied for the North Carolina region in order to

aid the local government and emergency services in evacuation and disaster

management services. They used the ADCIRC model to achieve this purpose, with the

30

input grid comprised of 227,240 nodes and 440,904 elements and bathymetry obtained

from the East coast 2001 grid. Wind data obtained from the National Hurricane Center

was forced into ADCIRC, in order for near real-time (up to 10-20 minutes prior to the

storm) storm surge forecasts. The researchers used real-time nowcasts, 6-hour forecasts

and 12-hour forecasts and compared them to recorded weather station data for the

storm surge height. The simulations showed that a maximum surge height of 0.57 m,

0.74 m, and 0.99 m for the 12, 6 and 0-hour forecasts, with the 0-hr forecast showing

just a slight difference from the 1.22 m recorded from the weather station at

Wrightsville Beach. Further cross-checking of the NCFS wind speed and water

elevation results with that of NOAA proved to be consistent, showing that NCFS is a

reliable storm surge prediction system.

Bhrakasan et.al (2014) attempted to validate field measurements and data from

the Cyclone Phalin storm surge off India’s east coast using the coupled wave and surge

hydrodynamic SWAN+ADCIRC model. The wind field was obtained from the

Jelesnianski model, while the bathymetry was obtained from GEBCO with a 30-arc

second resolution. The unstructured mesh, encompassing the entire east coast of India

(a distance of roughly 750 km), was generated using the Surface Modeling System

(SMS), with a grid resolution of 1 km. A comparison of wave heights generated by the

coupled model, one with the Indian Ocean swells included and one without them,

showed that the wave heights with the swells included showed a greater consistency

with the field data by up to 0.5 m. The maximum surge height at the Ganjam station

using ADCIRC astronomical and meteorological forcing was 2.3 m, compared to 3.0 m

with the radiation stress gradient forced with SWAN. The study verified the accuracy

31

of the coupled ADCIRC+SWAN model with observed data compared to the standalone

ADCIRC data, showing that such coupled models are fit for use for operational

forecasting purposes.

A better understanding of inundation or coastal flooding was the primary

objective of Bhrakasan et.al (2014)’s study, in order to better prepare the appropriate

local authorities and residents of these low-lying areas should such a phenomenon

occur in their area. To this end, they simulated the coastal inundation from Cyclone

Thane, which made landfall at the Bay of Bengal along the Tamil Nadu Coast. A

depth-averaged version (2DDI) of ADCIRC was used, with bathymetry for the region

obtained from GEBCO, having a resolution of 30 arc seconds. The finite rectangular

mesh used for the input file was a rectangular one, as opposed to the semi-circular

domain, in order to avoid computational instability at corner node points, although it is

more computationally intensive. A coarse mesh resolution was used, with a spatial

resolution of 20 km, consisting of 68,896 nodes and 136,809 triangular elements. Wind

fields for Cyclone Thane were obtained from the Holland dynamic wind model, with

the computed wind speed (38.55 m/s) consistent with the IMD’s value (40.3 m/s). Prior

to cold starting ADCIRC, the researchers noted that it is important to turn on the

wetting and drying of grid elements option in order to simulate coastal inundation. The

maximum storm surge height predicted by the model was at 1.2 m, while the longest

inundation occurred at the town of Cuddalore at 349 m. However, at the coastal areas

with mild beach inclinations, the computational value was significantly lower than the

recorded field measurements, which may be attributed to the inability of GEBCO

bathymetry to accurately capture the coastal beach topography. Although the ADCIRC

32

simulation produced consistent results with that of the station data, a recommendation

was made for the improvement of beach topography, since beaches play a significant

role in storm surge inundation.

An application of various sophisticated numerical weather prediction models was

done by Bhaskaran et.al (2011) for Cyclone Thane which made landfall at the Bay of

Bengal during December 2011. The typhoon path was taken from the Joint Typhoon

Weather Center’s best forecasted track, while the wind and pressure data was taken

from WRF. The Kain-Fritzch parameterization scheme was used for the WRF

simulation run time covering the duration of the cyclone’s landfall. An unstructured

fine structure mesh covering the Bay of Bengal region was then inputted into the

coupled, parallel mode SWAN+ADCIRC model. Water elevation heights obtained

from SWAN+ADCIRC were validated by comparison with three satellite typhoon

forecast tracks, as well as observations from the Pondicherry weather station. WRF

data forced on SWAN_ADCIRC was shown to be crucial in improving accuracy with

the standard observed data and for real-time operational forecasting needs.

III. NUMERICAL ANALYSIS OF TYPHOON HAIYAN STORM SURGE

The Haiyan-induced storm surge was the subject of analysis by Japanese

researchers Lee & Yamashita (2013) of Hiroshima University. The numerical methods

used in their study involved the use of WRF for the basic meteorology, with the waves

then simulated using WaveWatch and SWAN.

Takagi et.al (2015) made on-site field surveys in order to validate storm surge

elevation results obtained from numerical simulations. The researchers used the

SWAN model, which computes random, short-crested, wind-generated waves. Their

33

results showed that Leyte and Samar Island were hit by the highest storm surge, which

was corroborated by their field surveys wherein they measured the maximum storm

surge at Tacloban to be about 7 m. The numerical simulations also agreed with the

water elevation measurements made by observation stations. This showed that sea-

level heights rapidly increased an hour after a momentary decrease was first reported.

Several Filipino researchers, prompted by the sudden demand of the Filipino

public for correct information on storm surges, have also begun storm surge analysis

using various numerical simulation methods. One such research is being led by Dr.

Enrico Paringit (2014) of the University of the Philippines-Disaster Risk and Exposure

Assessment for Mitigation (UP-DREAM), who have successfully developed a state-of-

the-art, LIDAR-based flood mapping and hydrological modelling system that aims to

provide early and accurate flood warnings and advisories to residents of areas

forecasted to be plagued by typhoon-induced flooding. It is interesting to note that a

storm surge model has been developed previously by Nilo (1995), a Filipino professor

from the University of the Philippines.

The effects of varying physical parameters on the accuracy of WRF simulations

on typhoon track, intensity and rainfall prediction were investigated by Ramos et.al

(2014). Using Typhoon Haiyan as a case study, four different simulations were

performed using the Dudhia scheme for shortwave radiation, RRTM scheme for

longwave radiation, Unified NOAH Land Surface Model, and the YSU scheme for

PBL. Different microphysics schemes were also tested, such as the Lin et.al scheme,

WRF single-moment class 5 Ferrier, and WRF single-moment class 6, with three

cumulus physics schemes also being varied, namely, Kain-Fritsch, Betts-Miller-Janjic

34

and Grell-Devenyi. Their results showed that the Kain-Fritsch, Betts-Miller-Janjic and

Grell-Devenyi cumulus schemes are more sensitive to rainfall; intensity and typhoon

track prediction, respectively, while the variation of the microphysics schemes was

shown to have a negligible effect on the accuracy of the simulations.

The coupled SWAN+ADCIRC model was utilized by Kim et.al (2013) to

simulate the Typhoon Haiyan storm surge, using a mesh that covered the entire

Northwest Pacific Ocean, with a finer refinement at the Leyte Gulf. The simulations

were performed over a 4.5 day run period (November 5-10, 2013) using 128 cores for a

2-hour computational period. Two sources of meteorological forcing were used in their

study, the Korean Meteorological Agency (KMA) data, which generally

underpredicted the storm surge, and the Holland Model data, which provided surge

heights with significantly lesser deviation from recorded data at observation stations,

with the exception of Ormoc at Borongan.

Historical case studies on typhoons having similar tracks to Typhoon Haiyan that

also passed through the Leyte Gulf were conducted by Kawai (2014) from 1951-2013.

They used a two-dimensional parametric typhoon model and a one-layer long wave

model over a 2-km bathymetry of Eastern Philippines, with the wave field of Typhoon

Haiyan simulated using the SWAN model. The researchers noted that due to its

exceptionally low pressure (895 hPa) upon landfall, Typhoon Haiyan produced a

significantly higher surge height compared to its historical counterparts.

Lee & Yamashita (2013) made used of a coupled atmosphere-waves-ocean

model to simulate the Typhoon Haiyan storm surge, with meteorological forcing

obtained from WRF and wave forcing from SWAN and WaveWatchIII being used to

35

drive the Princeton Ocean Model (POM). The simulation covered a period of 9 days,

from November 3-12, 2013. 4-step nesting was utilized for WRF, with the output 10-m

U wind, 10-m V wind and surface pressure forced into WaveWatchIII and SWAN in

order to obtain the wave height and wind-induced dissipation stress. The wave-induced

dissipation stress was then forced into POM as the basis of the wave set-up, along with

the WRF meteorological forcing, in order to re-create the tidal and storm surge heights

during Typhoon Haiyan. WRF was able to capture the 895 hPa central pressure

measured when Typhoon Haiyan made landfall on the Philippines, but underestimated

its wind speed. SWAN results showed a maximum sea-level height of 13-16 m at the

Leyte Gulf, while water elevation time series graphs of Tacloban showed a sea-level

height (comprised of tidal, wind, pressure and wave set-up forcing) of 2.5 m, while the

storm surge was estimated at 2 m. The results were also compared with those making

use of the Holland model meteorological forcing, which estimated a 3.5 m sea-level

height and 3-m storm surge for Tacloban.

Using the JMA storm surge model, Lagmay (2013) modelled the storm surges

caused by Typhoon Haiyan, with the ETOPO2 2-arc minute bathymetry data and JMA

maximum winds, central pressure and storm track used as input. Results of the

numerical simulation showed that the highest predicted storm surge was 5.3 m for

Matarinao Bay, Eastern Samar, followed by 4.7 m in Poro Island, Biliran and 4.5 m in

Tacloban. Although the predicted surge was late by up to 4 hours, generally the JMA

model was able to sufficiently capture the Tacloban storm surge.

36

CHAPTER 4

METHODOLOGY

The numerical methods that will be used in this study will be discussed in this

chapter. This study will use numerical analysis to model the storm surge spawned by

Typhoon Haiyan as it made landfall in the Philippines, specifically Leyte Island, and

determined the wave heights at the different points of the Leyte coastline. To this end,

Weather Research and Forecasting will be used to provide the primary meteorological

conditions that caused this surge (a more complete discussion is in Chapter 2). The

storm surge will then be simulated using the Advanced Circulation Model (ADCIRC).

Weather Research and Forecasting is a numerical weather prediction system

that is designed for the needs of a new generation of meteorologists and researchers. It

is capable of supporting both meteorological research and operational purposes.

Several government and academic institutions that were primarily involved in the

development of this mesoscale model include the National Center for Atmospheric

Research (NCAR), the National Oceanic and Atmospheric Administration (represented

by the National Centers for Environmental Prediction (NCEP) and the (then) Forecast

Systems Laboratory (FSL)), the Air Force Weather Agency (AFWA), the Naval

Research Laboratory, the University of Oklahoma, and the Federal Aviation

Administration (FAA). WRF has been shown to be more accurate in its simulation of

surface wind and pressure that is an important prerequisite for the storm surge model

(Kim et al., 2010).

37

The Advanced Circulation (ADCIRC) Model is a highly advanced network of

computer programs that can solve time-dependent equations of fluid mechanics

relative to the Earth in motion. Hydrostatic pressure and Boussinesq approximations

form the basis of the equations that the software uses. The numerical technique behind

ADCIRC involves the use of the finite element method, which is the approximation of

a solution to particular differential equations given certain boundary conditions.

Variational methods (such as calculus of variation) are often employed in the finite

element method (Reddy, 2005). The finite element method enables the usage of highly

flexible, unstructured grids by the program (Luettich & Westerink, 2012).

I. PRINCIPAL COMPONENTS OF THE WEATHER RESEARCH AND

FORECASTING MODEL VERSION 3.5 (WRFV3.5)

WRF is a state-of the-art meteorological forecasting and simulation system that

is compatible with and can be effectively and efficiently run using parallel

programming methods. The abundance of physics options offered by the software

make the simulations more realistic by incorporating physical principles and equations.

WRF can be used for large-scale and small-scale operations, as its nesting options can

cover a distance as short as a few meters to thousands of kilometers. Some of its

applications include forecast research, idealized simulations, data assimilation

research, climate research and coupled model applications, which will be used in this

38

study (WRF User's Guide, 2014)..

Figure 4.1 Schematic Flowchart for WRF Input and Output Data

A. WRF PREPROCESSING SYSTEM (WPS)

The WRF Preprocessing System (WPS) is comprised of three executable

programs that prepare input data (such as FNLs, which will be discussed in more detail

in succeeding sections) for simulation by the WRF software. A graphical user interface

called WRF Portal is advised for an easier and faster execution of the programs. Note

that the network common data format (NETCDF) must be first installed for a

successful run, as well as all the files required by WRF.

1. Geogrid.exe

Geogrid sets the simulation domain, which is the geographical area that will be

covered in the run, as prompted in the namelist.input file. It computes the longitude

39

and latitude at every grid point, and incorporates the effects of terrestrial data like soil

categories, land use category, terrain height, annual mean deep soil temperature,

monthly vegetation fraction, monthly albedo, maximum snow albedo, and slope

category into the grid. Data sets for Geogrid are provided in the WRF download page,

with varying degrees of resolution to fit the researcher’s needs.

2. Ungrib.exe

Ungrib reads the grib files, “ungribs” them and rewrites them in a new format

called the intermediate format. They contain time-independent meteorological fields

sourced from other meteorology models. In the ungribbing process, GRIB identifies

which files are needed for WRF execution, from codes known as variable tables (or

simply VTables) and extracts them in preparation for the rewriting process. There are

three options for the intermediate format—WPS, SI and MM5—but WPS is the default

and preferred option for the WRF run.

3. Metgrid.exe

After rewriting the meteorological fields in the intermediate format, metgrid

will interpolate these into the simulation domain set by the user in Geogrid. This output

is required to run the real.exe program. The date ranges for the data sets to be used will

be prompted at this stage. Due to its time-dependence, metgrid will always prompt the

user for the date after the current run. Its output, like Geogrid’s, is in a NetCDF format

(Skamarock et al. 2005).

B. WEATHER RESEARCH AND FORECASTING (WRF) SYSTEM

EXECUTION

40

There are two major types of simulation in WRF, the ideal.exe and real.exe.

Ideal.exe is used to run standard test case scenarios. On the other hand, real.exe is

executed to obtain and analyze near-real time regional or global forecasts, which are

slightly delayed from the time of occurrence. Wrf.exe, executed after running ideal.exe

or real.exe, is used to complete the WRF simulation and write the output files as

defined by the user in the namelist.input file. WRF can also be run in parallel in order

to cut back on computational time, this is done by executing the following script on the

Terminal window: mpirun –np 10 ./wrf.exe.

The WRF simulation used a nested domain in order to provide a more accurate

representation of the meteorological parameters in the region of interest. As shown in

the figure below, the parent domain covered the entire Philippine region, while the

nested domain covered the eastern Philippine seaboard. The parent domain had a grid

spacing of 20.5 km by 20.5 km, while the nested domain only covered a third of that

with a grid spacing of 6.83 km by 6.83 km. A resolution of 5 m was used for both

domains. In this study, WRF was run in parallel, with an estimated computational time

of 2 hours, producing two output files containing various meteorological parameters

specified in the online WRF documentation, one for the parent domain and the other

for the nested domain. The simulation was set from 0000 UTC 1 November 2013 until

0000 UTC 16 November 2013, covering the duration and aftermath of the storm. Most

parameters were set to default (such as the WRF Single-Moment 3 scheme, a

simplified scheme for mesoscale purposes incorporating ice and snow processes), with

full details of the parameters selected provided in Appendix E.

41

Figure 4.1 WRF Parent Domain (1) and Nested Domain (2)

Covering the Eastern Philippine Coast

II. DESCRIPTION OF THE DATA

A. METEOROLOGICAL DATA

The data to be used in this study is the National Center for Environmental

Protection (NCEP) Final (FNL) grib1 operational global analysis data having a

resolution of 1.0 by 1.0 degrees for its gribs, obtained every six hours. The FNLs,

which are updated daily, have a delayed release so that more observational data can be

added for a more accurate forecast. They are prepared by the Global Data Assimilation

System (GDAS), which obtains it from sources like the Global Telecommunications

System (GTS). The FNLs are available to the public domain and can be downloaded at

http://rda.ucar.edu/datasets/ds083.2/.

42

B. DOMAINS

The primary region of interest of this study will be the coastal provinces along

Eastern Philippines, specifically the areas where Typhoon Haiyan made landfall. These

provinces include Cebu, Capiz, Negros Occidental, Negros Oriental, Iloilo, Leyte and

Samar. Table 4.1 shows the coordinates of the weather observation stations in the

domain that will be used for the wind speed and water elevation files at selected points

(fort.61 and fort.62). The study will focus on the city of Tacloban, Leyte, one of the

cities severely affected by the surge.

Table 4.1 Meteorological Stations Used for the Fort.61 and Fort.62 Files

Output Weather Observation

Stations Coordinates

Guinan, Eastern Samar 11.0333° N, 125.7247° E

Tacloban, Leyte 11.2500° N, 125.0000° E

Ormoc City, Leyte 11.0500° N, 124.6089° E

Daanbantayan, Cebu 11.2500° N, 124.0000° E

Bantayan Island, Cebu 11.1700° N, 123.7200° E

Concepcion, Iloilo 11.2000° N, 123.1000° E

Busuanga, Palawan 12.1000° N, 120.0836° E

The province of Leyte, an island located south of Manila in the Visayas region,

will serve as the main area of interest in this study. Leyte is a rural province, whose

topographical features are primarily mountains and forests. It has an area of 7,367.6

km2 with geographical coordinates 11.0000° N, 124.8500° E. The geographical

43

coordinates of the domain to be covered in the WRF and ADCIRC simulations are

13.25° N, 7.85° N, 122.00° E, 127.7500° E

The WRF simulation on Leyte covered the meteorological conditions in Leyte

before, during, and after Typhoon Haiyan made landfall at Leyte over a two-week

period from November 1-15, 2014. This will be done in order to produce the output

files; 10 m wind speed at the x and y direction, as well as the surface pressure, which

will be forced on ADCIRC through the fort.22 file (details will be discussed in the

ADCIRC section below). ADCIRC will then be used to computationally determine the

water elevation and wind speed data at selected points in the study’s region of interest.

These wind speed and wave height output data can be modelled using MATLAB

through contour maps and time series graphs, in order to show the extent of the storm

surge in these regions.

III. ADVANCED CIRCULATION MODEL (ADCIRC)

After completing the WRF simulation, the 10-m wind speed and surface

pressure data must be reformatted into an ADCIRC-compatible fort.22 file (this will be

discussed later on in this chapter). The hydrodynamic model can be run in 2D or 3D,

with wave height and velocity being obtained from the solutions of Generalized Wave-

Continuity Equation (GWCE) and 2DDI or 3D momentum equations, respectively.

Boundary conditions include specified flow, elevation, external and internal barrier

overflow, wind stress/speed and atmospheric pressure. Tidal conditions can also be

44

forced into the model to make the simulation more realistic (Luettich & Westerink,

2012). ADCIRC can also simulate wave inundation and recession for coastal regions

through its state-of-the-art wetting and drying algorithm that activates and de-activates

grid elements at particular time intervals. Validation of the wetting and drying

algorithm was performed by Blain et al. (2002), Dietrich et al. (2004) and Bhrakasan

et. al (2014). The required files for the execution of the model are fort.14 and fort.15

(which will be explained further in the sections below)

To assist in the generation of these files, a graphical user interface designed by

the US Navy called Navy Unstructured Mesh Creation and Editing Toolkit

(NUMCAT) will be utilized in this study. NUMCAT consists of MeshGUI, MakeF15

and MakeF22, which can produce fort.14, fort.15 and fort.22 files respectively. This

automates the processes required to create the ADCIRC input files using minimal input

provided by the user. MATLAB, Perl and FORTRAN must be installed in the

computer where the user intends to use NUMCAT, since the software uses MATLAB,

Perl and FORTRAN scripts to create the input files. Details for installation of the

different NUMCAT components can be found in Appendix B (Blain et al., 2008).

45

Figure 4.3 Schematic Flowchart for ADCIRC Input and Output Data

A. ADCIRC GRID AND BOUNDARY INFORMATION FILE (FORT.14)

The fort.14 file is an unstructured finite element mesh comprising the

geographical region of interest of the study. External and internal boundaries

surrounding the domain are identified here, with the external boundaries labelled first

before the internal boundaries. For the purposes of this study, external boundaries refer

to the mainland boundaries (IBTYPE=0), or bodies of water around the domain (such

as lakes and oceans) while internal boundaries refer to the island boundaries

(IBTYPE=1). Both boundaries are characterized by a free tangential slip, with the

primary difference being that island boundaries having a strong normal flow, while the

46

mainland boundary has none. These boundaries are connected to each other by the use

of nodes, in order to grid the domain.

Figure 4.4 Finite Element Mesh of Eastern Philippines

Figure 4.5 Schematic Flowchart of the Input and Output Data in MeshGUI

47

The conditions required for the finite element mesh creation are the domain

bathymetry (elevation/depth at each geographical point of the domain) and the

coastline data (latitude and longitude at each geographical point of the domain). The

bathymetry was obtained from the ETOPO1 1 arc-minute (2 km resolution) global

relief model (Amante, C. & B.W. Eakins, 2009), while the coastline data was obtained

from the Global Self-consistent, Hierarchical, High-resolution Geography Database

(GSHHG) (Wessel, P., & W. H. F. Smith, 1996).

In order to simplify the creation of the finite element mesh, MeshCreate, a

MATLAB-based Graphical User’s Interface designed by Blain et al. (2008) was used.

The GUI requires as input the bathymetry and coastline files describing the

geographical region of interest, and produces as output an ADCIRC-compatible fort.14

file. Step-by-step instructions for obtaining bathymetry and coastline data using these

databases for a particular domain, as well as an installation and operational procedure

for MeshCreate will be given in Appendix B.

48

Figure 4.6 MeshCreate GUI Startup Window

The finite element mesh of the fort.14 file for eastern Philippines consists of

20769 nodes and 38766 triangular elements. The default semi-circular external

boundary was chosen for the mesh, as shown in Figure 4.4. The contour bathymetry

map for the Eastern Philippine region is shown in Figure 4.7 below.

49

Figure 4.7 Bathymetry Map of Eastern Philippines

B. MODEL PARAMETER AND PERIODIC BOUNDARY CONDITION

FILE (FORT.15)

The parameters required for running ADCIRC in 2D or 3D are specified in the

fort.15 file. (Luettich & Westerink, 2012). The MakeF15 GUI, designed by Blain et al.,

2008, isa Perl-based program, was used to facilitate the creation of fort.15. For this

study, the simulation was cold-started (started from scratch), and made use of the

spherical (longitude/latitude) coordinates. The model time step was set to 2.0 s, with

the predictor algorithm turned on to facilitate a more stable model. The time derivative

and advective terms were both turned off in ADCIRC’s computation of the

Generalized Wave Continuity Equation (GWCE) due to numerical instabilities midway

through the run. The finite amplitude terms were also included in the simulations, with

the bathymetric depth assumed equal to what was written in the fort.14 file. The

ramping option was also turned on and applied for one runtime day.

50

The hyperbolic tangent ramp function was also specified and applied to both

tidal and meteorological forcings. Other relevant parameters include the GWCE

weighting factor, which was set to 0.02, the spatially constant horizontal eddy

viscosity, which was set to 5.0, the minimum bathymetric water depth, which was set

to 1.0 m, and the minimum velocity, which was set to 0..05 m/s. Meteorological

forcing was applied by setting the NWS option to 5. (A more detailed explanation of

this parameter will be provided in the fort.22 section).

A tidal forcing was also used for the simulation, comprising the following tidal

constituents: K1, O1, M2, and P1, which are the dominant tidal constituents for the

Eastern Philippine region. The tides in the eastern coast of the Philippines are of mixed

nature, but are principally diurnal with a form ratio of 2.3, where the form ratio F is

simply the ratio of the amplitudes of the dominant diurnal (K1 and O1) and semi-

diurnal (M2 and S2) components, which is expressed in mathematical form as:

F=(O1+K1)/(M2+S2). (If F>1, then one can say the tide is prominently diurnal, but if

0<F<1, the tides are predominantly semi-diurnal) (Villanoy & Mancebo, 1998). Table

4.2 below lists the amplitudes, nature and description of each of the dominant tidal

constituents in the Eastern Philippine coast, which was also used as the basis of

selecting the tidal constituents for the ADCIRC simulation.

51

Table 4.2 Major Tidal Constituents in the Eastern Philippines

Tidal Constituent Amplitude Source Nature

K1 32.01 Luni-Solar Diurnal Diurnal

O1 26.18 Principal Lunar Diurnal Diurnal

M2 17.63 Principal Lunar Semi-Diurnal

P1 11.66 Principal Solar Diurnal Diurnal

S2 7.62 Principal Solar Semi-Diurnal

Figure 4.8 MakeF15 GUI Window

52

C. SINGLE FILE METEOROLOGICAL FORCING INPUT (FORT.22)

When the meteorological forcing parameter in the fort.15 file is set to the

following nonzero values (NWS=1, 2, -2, 3, 4, -4, 5, -5, 6, 8, 12, 15, 19, 20, 101, 102, -

102, 103, 104, -104, 105, -105, 106), a single file meteorological forcing input is read

in and is used to drive the simulation. There are several formats, with the format to be

used in a particular study depending upon the meteorological data to be used. Some re-

formatting may also be required in order to convert the meteorological data into an

ADCIRC-compatible fort.22 file.

For this study, the NWS option selected was 5, wherein wind velocity in the x

and y-direction (U and V) and atmospheric pressure corresponding to each of the nodes

for all time slices encompassing the entire model duration are read into the fort.22 file.

The first data entry corresponded to the start of the ADCIRC run (November 7, 2013),

with succeeding entries being written at the specified meteorological wind time

interval. This is the time interval (in seconds) between successive wind and pressure

data in the fort.22 file.

D. ADCIRC PREPROCESSING AND EXECUTION

Prior to running ADCIRC, the mesh and control files (fort.14 and fort.15) must

first be divided into smaller subdomains, especially if a multi-core processor is to be

used, The decomposed files are then distributed into each of the specified cores, in

order for ADCIRC to run on each partition located in their particular CPU. This is

done in order to speed up the simulation time, as ADCIRC is computationally

53

intensive. Mesh partitioning is performed by the METIS package located in ADCIRC’s

work folder.

Figure 4.9 ADCPREP Flowchart

54

Figure 4.10 ADCIRC Flowchart

In order to begin preprocessing, go to the folder where the input files are located (cd

work on the Terminal window). Execute the adcprep program by typing ./adcprep on

Terminal. When adcprep is executed, the user will be prompted to enter the number of

cores to be used for the simulation. Mesh partitioning is done by selecting 1. partmesh,

and decomposition of input files into the subdomains defined by partmesh is perfomed

by re-running adcprep and choosing 2. prepall and specifying the necessary input files

55

(fort.14 and fort.15) required by the prompt. Successful preprocessing will be denoted

by the creation of the metis_graph.txt and partmesh.txt files (Dietrich, 2010).

The user can run ADCIRC in either serial or parallel mode by executing

./adcirc or mpirun –np 24 ./padcirc on the Terminal window, respectively. (24 in the

padcirc syntax refers to the number of cores to be used in the parallel run, the user

should modify this based on the number of processors present in the facility. The

advantage of using a parallel run is that the subdomains prepared by the adcprep

program can be distributed to the different cores, with MPI providing computing

synchronization among the subdomains, resulting in a significantly lesser

computational time. For this study, running ADCIRC in parallel mode only took a

computational time of 2 hours and 30 minutes, while a serial run takes at least thrice

that time.

56

CHAPTER 5

RESULTS, DISCUSSION AND ANALYSIS

There are a variety of output files that can be created and can be saved in

different formats, depending on the parameters the user specified in the fort.15 file.

ADCIRC output can be written in ASCII, binary or NetCDF format. ASCII-formatted

files have the advantage of being read by a variety of applications, such as spreadsheets

and text editors, however they tend to be extremely large, especially if the simulation

period is over an extended period and the user desires to have elevation and wind

output written at smaller time steps. If memory is limited, the user can consider using

the binary format, which is more compact than the ASCII files.

For a proper storm surge characterization, four output files are sufficient: the

Elevation Time Series at Specified Elevation Recording Stations (fort.61), Depth-

averaged Velocity Time Series at Specified Velocity Recording Stations (fort.62),

Elevation Time Series at All Nodes in the Model Grid and the Depth-averaged

Velocity Time Series at All Nodes in the Model Grid (fort.64). (Note that these output

files are only created if a 2D run is chosen in the fort.15 file).

A. DESCRIPTION OF THE OUTPUT

ADCIRC allows the water elevation data to be written either at specified

locations in the mesh (fort.61) or over all the nodes present in the domain (fort.63).

Both present their own distinct advantages, as the fort.63 file can be used to show a

contour map of the storm surge over the entire domain, while the fort.61 file are better

57

used for showing elevation time series graphs at the desired points. Furthermore, since

elevation data is only being written for fewer points, the fort.61 files can be requested

at smaller time intervals compared to the fort.63 files while still maintaining a

reasonable file size. Although fort.63 files can also be requested at smaller time

intervals, this will require a significant amount of file space. For practical purposes,

fort.63 files are generally written in half-hour or one-hour intervals, while fort.61 files

can reasonably provide output at up to two-minute intervals (Hill, 2007).

Wind velocity can also be post-processed and visualized in a manner similar to

that of the elevation output. If the user desires to show a time series graph of wind

velocity at specified locations, using the fort.62 file is preferable. On the other hand,

the global velocity output (or velocity output over all the nodes of the finite element

mesh) is best visualized using a contour plot. It must be noted that for this study, a two-

dimensional run option was selected, and therefore, the user has the added option of

presenting the two-dimensional velocity vectors along with the contour map (Hill,

2007).

B. WATER ELEVATION AND WIND VELOCITY OUTPUT FROM WRF

FORCING

The output parameters that will be taken from WRF are the surface winds and

the surface pressure. The surface winds in question are the zonal (U) and meridional

(V) 10-m winds. Specifically, this study will present graphs displaying the maximum

wind velocity and the minimum central pressure, as these are what primarily drive a

storm surge. Once this output is forced on ADCIRC, the significant wave heights and

directions of the surge along the different coastal areas will be presented. The behavior

58

of the waves with or without the interpolation of wave-induced stress will also be

shown. The bulk of the wave analysis will be performed on the domains directly

affected by the storm surge, which will be the smaller domain. Contour animations of

surface pressure and the 10-m U and V winds during the 15-day model run time

encompassing the period before, during and after the Typhoon Haiyan storm surge, as

well as time series graphs for wave height and wind velocity are presented here. The

MATLAB-based NUMCAT post-processing and visualization tools were used to

create the time series graphs of water elevation and wind velocity, while Panoply was

used to create the contour animations of surface pressure and the 10-m U and V winds.

Day 2 in the plots below refer to 1200H November 5 while Day 11 refers to 1200H

November 10.

Figure 5.1 a) Elevation Time Series at Bantayan Island, Cebu using fort.19 Tidal

Forcing and b) WXTide Tidal Chart for Cebu for November 1-7, 2013

-0.010.000.010.020.030.040.050.060.070.080.09

-0.5

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5 6 7

Ele

vati

on

(m

)

November 2013 (Days)

Elevation Time Series WXTide Tidal Forcing at Bantayan

WXTide Tidal Forcing fort.19 Tidal Forcing

59

Figure 5.2 a) Elevation Time Series at Basey, Samar using fort.19 Tidal Forcing and b)

WXTide Tidal Chart for Basey for November 1-7, 2013

Figure 5.3 a) Elevation Time Series at Tacloban, Leyte using fort.19 Tidal

Forcing and b) WXTide Tidal Chart for Cebu for November 1-7, 2013

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 1 2 3 4 5 6 7

Ele

vati

on

(m

)

November 2013 (Days)

Elevation Time Series Using WXTide Tidal Forcing at Basey

fort.19 Tidal Forcing WXTide Measured Tide

-0.2

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7

Ele

vati

on

(m

)

November 2013 (Days)

Elevation Time Series using WXTide Tidal Forcing at

Tacloban

WXTides Tidal Forcing fort.19 Tidal Forcing

60

Figure 5.4: Water Elevation Time Series for Basey, Tacloban, and Bantayan Island

Using 10-m WRF Wind Meteorological Forcing

The model run time using the WRF meteorological forcing spanned a period of

fifteen days from November 1-16, 2013. However, for a better appreciation and

visualization of the storm surge event, a nine day period from 0000 UTC 2 November

(Day 2 on Fig 5.4) 2013 until 0000 UTC 11 November 2013 (Day 11 on Fig 5.4).

Figure 5.4 shows the elevation time series for Tacloban, Basey and Bantayan, which

were superimposed into each other in order to better see the amplitude of the storm

surge at these locations relative to each other. Water elevation graphs using tidal

forcing from the external open ocean boundary (fort.19) are presented in Figures 5.1,

5.2 and 5.3 along with the corresponding observed tidal gauge readings at Tacloban,

Basey and Bantayan.. These three locations were chosen due to the significant storm

surge heights recorded there by subsequent field surveys.

The occurrence of the storm surge for the three locations corresponded to 12-

hour period from Day 6.5 to 7, evidenced by the dip in amplitude in Bantayan Island

on Day 6.5, followed shortly thereafter by Tacloban and Basey, with Basey having a

61

particularly noticeable dip at -2 m. The spike in amplitude signifying the storm surge

occurs at around Day 7 for all three locations, with the storm surge at Bantayan

occurring first, followed shortly thereafter by Tacloban and Basey. The results also

show that Tacloban had the highest computed water spike at 1.3 m, with Basey and

Bantayan Island both topping out at 1 m. A quick recession in water level after the

occurrence of the storm surge was also observed; this is due to the fact that ADCIRC

being a bathtub model, which means it uniformly increases and decreases the water

levels over the region of interest (Allen & Sanchagrin, 2013), in corroboration with

eyewitness reports (BBC, 2013; Takagi et al. 2015).

One method to verify the accuracy of the elevation time series graphs is to

check the tidal waves (as well as the measurement of the high tide and low tide)

graphed for Bantayan, Tacloban and Basey against existing tidal databases. To achieve

this purpose, the WXTides32, a Windows-based tidal database available freely online,

was utilized. It can be observed from the tidal graphs from Figs. 5.1-5.3 are

characterized by semi-diurnal sinusoidal waves, with the tidal peaks at Basey and

Tacloban underpredicted by 0.6 and 0.8 m, respectively, while the tidal peaks were

underpredicted by up to 1.5 m for Bantayan Island. The high tides for all locations are

generally in phase with each other, however the low tides are out of phase with each

other by 180 degrees. A possible explanation for such results are the coarse bathymetry

utilized for the study (2 km spacing), and as such the complete effects of terrain and

topography cannot be captured in the mesh. In the case of Bantayan Island, it may be

possible that the bathymetry in Bantayan includes unnecessary barriers through which

waves bounce off from and thus become reflected, leading to lower water level

62

readings. Nevertheless, the model has reasonably reproduced the diurnal tidal wave

patterns of Tacloban, Basey and Bamtayan Island, as well as the general phase shape

of the three locations.

Figure 5.5: JTWC Typhoon Track of Typhoon Haiyan

Figure 5.6: WRF 10-m U and V Wind Velocity Vector with Magnitude and Direction

for Typhoon Haiyan

63

Figure 5.7: WRF Surface Pressure Contour Map for Typhoon Haiyan

Using a plotting software external to MATLAB called Panoply, contour maps

of the 10-m U and V winds and surface pressure were produced, as well as a vector

map (Figures 5.7 and 5.8, respectively), while the 10-m U and V wind velocity vector

map (for both magnitude and direction), is shown in Figure 5.7. The screenshots from

these plots shown in this document are taken from the 0300 8 November 2013 time

interval, with the typhoon having already made landfall in both Leyte and Samar and

heading in a northwest direction. Unusually low pressure and intense winds were

exhibited by Typhoon Haiyan, with pressure dropping all the way to 890 hPa when the

storm made landfall in Basey and Tacloban (Figure 5.8), with the mountainous region

of Surigao del Norte, while not directly affected by Haiyan at the time of the screen

shot, had a pressure reading of 860 hPa due to its altitude. Wind speeds in excess of 90

64

m/s (324 kph), were also measured by WRF, an overestimation when compared against

the observed speed of 315 kph reported by the Hong Kong Observatory (2013).

Figure 5.8: Maximum Water Levels at All Locations for WRF U10-V10 Forcing

A contour map for the maximum water levels occurring at all points in the

Eastern Philippine mesh is presented in Figure 5.9, which can paint a picture of where

the storm surge occurred over The maximum water level recorded at the San Pedro

Bay (the body of water nearest Tacloban and Basey) was 0.5 m, and 0.2 m in the

Visayan Sea (the body of water nearest the Bantayan Islands). At other locations where

Haiyan made landfall, 0.4 m near the Concepcion Bay and 0.2 m near Guinan. Due to

the relatively small range of water levels (ranging from 0-0.4 m near the coastline and

0.6 m-1.2 m at the open sea), the smaller differences in maximum water level were

better captured by the visualization.

65

C. COMPARISON WITH JTWC OUTPUT

Figure 5.9: Elevation Time Series Graph at Tacloban, Basey and Bantayan Using

JTWC Forcing

The Joint Weather Typhoon Center (JTWC) Best Track Data for Typhoon

Haiyan was also utilized in this study to serve as basis of comparison for the WRF

water elevation time series and contour maps. Using the JTWC data involves selecting

the NWS=8 option in the fort.15 file. Luettich & Westernink (2004) note that the

JTWC best track data can be copied without modification into the ADCIRC work

folder and be renamed as fort.22 in order to be used as meteorological forcing for

ADCIRC. The JTWC best track data for Typhoon Haiyan started at 0600 UTC

November 2, 2013 (Day 0) until 0600 UTC November 11, 2013, for a total run time of

nine days (Day 9).

The elevation time series graphs for Bantayan Island, Tacloban and Basey were

plotted simultaneously and presented in Figure 5.11. Similar to the WRF plots, there

are also noticeable dips in amplitudes prior to the spike in water levels indicative of the

surge, which occurs at Day 6. An especially drastic drop was observed in Basey,

66

similar to what was measured by the WRF plots. The timing of the storm surge using

the WRF forcing is consistent with the JTWC data, although WRF plotted the

occurrence of the storm surge a little later than the JTWC forcing.

There is a distinct difference observed between the water levels predicted using

the JTWC data and the WRF data, with the JTWC data recording significantly higher

wave heights for all three locations (up to 3.3 m in Tacloban as opposed to only 1.3 m

for WRF data), despite the fact that the same mesh was used for the two. A possible

explanation for this is that the JTWC data, being of a hurricane-track type format, has

incorporated extra features of the storm, such as elevated radial winds and gustiness

which are not completely captured with WRF simulations (Lin et al., 2010). The WRF

forcing may not have also captured the maximum sustained wind at the time of the

surge, leading to the lower water levels. The timing of the WRF-forced data is also not

similar relative to JTWC, which could be explained by the differences in the typhoon

track between WRF and JTWC, as well as the funnel effect which led to increases in

water levels in westward prior to the storm arriving there.

Figure 5.10: Maximum Elevation Contour Map For All Locations Using JTWC

67

Similar to the WRF forcing, a contour map plotting the maximum elevation for all

bodies of water in the mesh is presented in Figure 5.12. Water-level readings of

approximately 4 m were recorded at the San Pedro Bay and Leyte Gulf and 3 m for the

the Visayan Sea and the Concepcion Bay, and 2 m at the Calituban and Dawahon Reef

Other bodies of water in the Eastern Philippine mesh had peak water levels of

approximately 1 m, since the range of water levels was much greater compared to the

WRF forcing, and so the more minute differences in water levels were not captured by

this contour map.

Figure 5.11: a) WRF U10-V10 and b) JTWC Water Elevation Time Series Graphs for

Bacolod, Cadiz and San Carlos

68

Figure 5.12: a) WRF U10-V10 and b) JTWC Elevation Time Series Graphs for Guinan

and Ormoc

Other water elevation time series graphs of other locations where storm surges

induced by Typhoon Haiyan were recorded are presented in Figures 5.13a) and 5.14 b)

for WRF U10-V10 forcing, and in Figure 5.13a) and 5.14b) for JTWC forcing.

Generally, the dip in amplitude leading up to the actual storm surge was captured for

both WRF and JTWC, with the storm surge event occurring sometime between Day

6.5 and Day 7. The tidal phases for both sets of data were also relatively consistent

with each other. Water level readings for the other stations hovered generally between

0.5 m and 1.0 m, which was also consistent with that of the JTWC forcing. However,

relative to on-site field surveys conducted by Takagi et.al (2013), the measured storm

surge in Tacloban was approximately 5.0 m, meaning the WRF and JTWC data

underpredicted the storm surge by 3.5 m and 1.5 m respectively. This may be due to

the coarse bathymetry used in the study, which may have not accounted for completely

the effects of the actual water depth of the San Pedro Bay surrounding Tacloban City.

69

D. ELEVATION TIME SERIES USING U1-V1 WRF FORCING

Figure 5.13 a) WRF U1-V1 and b) JTWC Elevation Time Series for Tacloban, Basey

and Bantayan Island

Figure 5.14 a) WRF U1-V1 and b) JTWC Elevation Time Series for Bacolod, Cadiz

and San Carlos

70

Figure 5.15: a) WRF U1-V1 and b) JTWC Elevation Time Series at Guinan and Ormoc

Figures 5.13a), 5.14a) and 5.15 a) show the elevation time series using ground-

level (U1-V1) winds for the same eight locations as with the U10-V10 forcing. The

water increases observed between Days 7 and 8 show miminal storm surge at

Tacloban, Basey and Bantayan Island, in the order of approximately 0.2-0.5 m, as

opposed to the 10-m U and V elevation graphs where the occurrence of the storm surge

is more distinct. This may be explained by the fact that since they are ground-level

winds, they insufficiently capture the effects of hurricane winds, and as such cannot

capture the more drastic increases in water levels. However, they adequately capture

the tidal patterns and phases present in Eastern Philippines, and as such may be more

suited to such purposes, since astronomical tides are independent of elevated winds.

E. U20-V20 ELEVATION TIME SERIES USING WRF FORCING

71

Figure 5.16: a) WRF U20-V20 and b) JTWC Elevation Time Series at Tacloban, Basey

and Bantayan Island

Figure 5.17: a) WRF U20-V20 and b) JTWC Elevation Time Series at Bacolod, Cadiz

and San Carlos

72

Figure 5.18: a) WRF U20-V20 and b) JTWC Elevation Time Series for Guinan and

Ormoc

Figures 5.16a), 5.17a) and 5.18a) show the elevation time series using 20th

-level

U and V winds at 900 m (ARW User’s Guide, 2014). The timing of the amplitude

increase is predicted incorrectly relative to the WRF 10-m U and V wind forcing, with

the simulations recording the amplitude uptick a day in advance. This type of result is

consistent with a previous study conducted by Rhodes & Lindquist (2011) on the 900-

m U and V winds. A possible explanation may be that the rotation of the wind at

higher altitudes causing water level spikes to occur earlier relative to observed data,

although Rhodes & Lindquist noted that using 900-m wind data produces generally

inconclusive results.

73

F. VALIDATION WITH PUBLISHED FIELD DATA

Province Location Storm

tide

(m)

Date and time of

peak height

Eastern Samar Matarinao Bay 5.276 11-08-2013 09:50,

Biliran Poro Island, Biliran Str 4.675 11-08-2013 12:10,

Leyte Tacloban, San Juanico Str 4.498 11-08-2013 11:00,

Quezon Port Pusgo 4.422 11-09-2013 02:20,

Eastern Samar Andis Island, Port Borongan 4.341 11-08-2013 09:30,

Quezon Santa Cruz Harbor 4.172 11-09-2013 02:20,

Palawan Port Barton 3.912 11-09-2013 02:00,

Iloilo Banate 3.895 11-09-2013 02:10,

Leyte Palompon 3.89 11-08-2013 12:40,

Leyte Ormoc 3.761 11-08-2013 13:20,

Northern Samar Helm Harbor, Gamay Bay 3.704 11-08-2013 09:10,

Cebu Tuburan 3.238 11-08-2013 12:20,

Negros Occidental Himugaan River Entr 3.05 11-08-2013 14:00,

Negros Occidental Cadiz 2.989 11-08-2013 03:10,

Eastern Samar Guiuan 1.901 11-08-2013 08:40,

Manila Manila, Philippines 1.352 11-10-2013 02:10,

Table 5.1: DOST-Project NOAH Predicted Storm Tide Height at Eastern Philippines

for Typhoon Haiyan

In order to check the accuracy of the predicted water elevation heights using the

WRF 10-m U wind data, the results must be validated against existing published field

data as well as similar numerical simulation studies on Typhoon Haiyan. Table 5.1

above presents the DOST-Project NOAH storm tide (the combined astronomical tide

and storm surge water elevation) height for different locations in Eastern Philippines

during the Typhoon Haiyan event. The highest recorded storm tide was at Matarinao

Bay, Eastern Samar (5.3 m), followed by Poro Island, Biliran (4.7 m) and Tacloban,

74

Leyte (4.5 m). Further numerical simulation studies of the Typhoon Haiyan storm

surge at Tacloban City of 3.5 m using SWAN and WaveWatch (Lee & Yamashita,

2013), 4 m using SWAN+ADCIRC (Kim et. al, 2013), 4-4.5 m using the Takagi storm

surge model (Takagi et.al, 2013) and 4.5 m using the JMA model (Lagmay et.al, 2015,

Briones, 2014). Considering that the water elevation predicted using the 10-m U and V

meteorological forcing at Tacloban was only 1.5 m, the storm surge was

underpredicted by 2.0-3.0 m relative to the other Typhoon Haiyan numerical

simulations, and by 4.5 m relative to the subsequent field surveys conducted by Takagi

et. al (2013) in Tacloban, as their team measured a 5.5-6.0 m storm surge,

corroborating eyewitness reports.

There are several reasons which may explain why the storm surge in Typhoon

Haiyan was underestimated by the WRF simulations. First, the maximum wind speed

(200 kph) and central pressure (950 hPa) captured by WRF for Typhoon Haiyan was

significantly lower than that of the observed data measurements of 315 kph and 895

hPa respectively (JTWC, 2013; JMA, 2013; Lagmay et. al, 2015, Takagi et.al, 2013).

There were also variations in the storm track, which may have contributed to the

incorrect timing of the storm surge. Lastly, the coarse bathymetry used for the Eastern

Philippine mesh may not have sufficiently captured the wide and gradual sloping of the

sea floor of the Leyte Gulf, leading to lower water elevation readings.

75

CHAPTER 6

SUMMARY OF RESULTS, CONCLUSIONS AND RECOMMENDATIONS

This study has characterized the primary components of the Typhoon Haiyan

storm surge, water elevation and wind velocity, and presented their corresponding time

series graphs and contour maps, respectively. Two sources of meteorological forcing

data were also used, the Weather Research and Forecasting 10-m winds and surface

pressure, and the JTWC Best Track Data. Simulations carried out using both types of

forcing sufficiently captured the diurnal nature of tides and their respective tidal phases

in the Eastern Philippines.

For the three primary locations selected for this study, a maximum water level

of 1.0 m, 1.5 m and 1.2 m was recorded for Basey, Tacloban and Bantayan Island,

respectively, during the storm surge event using the WRF meteorological forcing, as

opposed to 2.0 m in Basey, 3.5 m in Tacloban and 2.5 m in Bantayan Island using the

JTWC forcing. Possible reasons why the WRF data underpredicted the storm surge

(relative to the JTWC data and the other Typhoon Haiyan numerical simulations) is the

underestimation of the maximum sustained winds and central pressure by the WRF

simulations. In the case of Bnatayan Island, the water levels measured by the model,

using both WRF and JTWC forcing were significantly lower compared to those

recorded by tidal gauges. This may be caused by wave reflection from natural barriers

present in the mesh, which hinder the recording of more realistic water level readings

by the model. For Tacloban and Bantayan Island, the tidal peaks were reasonably

76

predicted, with an underprediction only in the range of 0.2-0.4 m, respectively.

The reported peak water elevation height in Tacloban for the JTWC forcing is

reasonably similar to the other Typhoon Haiyan numerical simulations, who reported a

storm surge values ranging 3.5-4.5 m (using a variety of storm surge models). which is

2.5 m and 0.5 m higher than the computed elevation using WRF and JTWC

meteorological forcing, respectively. However, compared to field surveys collected by

Takagi et.al (2013) in Tacloban, the storm surge was greatly underestimated, since

their surveys measured a 5 m water level, and even a 7 m water level in some areas.

The researcher recommends that wave forcing be added in the future, in order

to more accurately simulate the storm surge, as well as take into account coastal

inundation due to wave flooding at the shore. A spin-up simulation using

meteorological forcing data spanning a 2 to 3 week period before the start of the

hurricane data will help the tides reach dynamic equilibrium and thus can provide more

accurate and realistic elevation predictions. Since the current study only had a 5-day

run time with a 2-day ramping period, a longer ramping period and run time can be

utilized to see whether this affects the accuracy of the results. An extensive tidal

harmonic analysis can also be performed for Eastern Philippines in order to further

investigate and characterize the tidal forces in this region. Finally, further studies can

also be conducted in testing the sensitivity of changing ADCIRC boundary condition

parameters, such as advective terms, the finite amplitude terms, ramping options, etc.

and determine their corresponding effects on the accuracy of the ADCIRC elevation

output.

77

APPENDICES

APPENDIX A: ADCIRC INSTALLATION GUIDE

Now that the mpi compiler for GNU has been installed, ADCIRC can now be

installed. As with the mpi installation, a step-by-step procedure of the command-line

scripts to be executed is outlined below; for detailed instructions, please refer to the

ADCIRCDEvGuide pdf found in the ADCIRC work directory.

1. Go to the ADCIRC work directory:

cd /home/earth/adcirc/work

2. Run config.guess manually to see what it guesses about the platform architecture:

./config.guess

This should yield the following path: x86_64-unknown-linux-gnu.

3. Check the availability of the compilers to be used. Since the gfortran compiler is to

be used, type:

which gfortran

This should point to the following path: /usr/bin/gfortran

4. If a parallel run in ADCIRC is desired, type:

which gcc

This should point to the following path: /usr/bin/gcc, and which mpif90 should

point to the following path: /home/earth/mpich2-install/bin/mpif90. If not, repeat step 6

of the MPICH installation where we set the bin in the startup script, Ubuntu does not

remember this once the Terminal window is closed so this may have to be repeated

every time one wishes to run ADCIRC.

5. Since GNU compiler flags are to be used, set compiler=gnu on the make command

line in order to build ADCIRC by typing:

make adcirc compiler=gnu

6. If this is successful (indicated by the creation of an odir3 folder and an adcirc file

with a purple logo in the work directory), run the following for the parallel

ADCIRC compiler:

make padcirc compiler=gnu

78

7. If this is successful (indicated by the creation of an odir4 folder and a padcirc file

with a purple logo in the work directory), it is time to build adcprep, a preprocessor

for the ADCIRC input files, i.e. fort.14 and fort.15. (Full details on adcprep can be

found in the ADCIRC online documentation). Type:

make adcprep compiler=gnu

A successful compilation will be indicated by the creation of an odir4 folder and an

adcprep file with a purple logo in the work directory)

8. Make all the remaining executables by typing:

make metis

A successful compilation will be indicated by the creation of an odir_metis folder and

an padcirc file with a purple logo in the work directory

make adcpost

A successful compilation will be indicated by the creation of an odir13 folder and an

adcpost file with a purple logo in the work directory (Dietrich, 2010).

79

APPENDIX B: INSTALLING, RUNNING AND TROUBLESHOOTING

NUMCAT

The NUMCAT pre-processor software is free of charge and can be downloaded

upon request from the NRL persons of interest through e-mail: Mr. Robert Linzell

(linzell@nrlssc.navy.mil) and Dr. Cheryl Ann Blain (blain@nrlssc.navy.mil). Mr.

Linzell also handles software errors and help questions on NUMCAT, while Dr. Blain

handles project-level decisions. In this section, a step-by-step procedure on the

installation of the two NUMCAT GUIs (MeshGUI and MakeF15), since MakeF22

currently does not support the NWS=5 option and WRF format. For more details,

please refer to the MeshGUI and MakeF15 user manual PDFs found in the top-level

Documents Directory of the NUMCAT software).

I. MESHGUI

The MeshGUI software must be installed in the same platform where

MATLAB is installed. For this study, the platform used was Windows 7, while the

version of MATLAB used was MATLABR2011a. There must also be an existing

bathymetry and boundary (coastline) information file in the current working directory

where MATLAB is to be run. This current working directory should be separate from

the NUMCAT Matlab folder and the MATLAB bin folder. The user is advised to

create a new personal folder inside the NUMCAT directory (e.g. MyMatlab) where all

the input files (such as the bathymetry, coastline, fort.14 and fort.15 files and so on) to

be set in MATLAB as the current working directory. To set the MyMatlab as the

current working directory, issue this command-line script at the MATLAB Command

Window:

MLStartDir ("C:/<username>/NUMCAT1.0.7/MyMatlab")

where <username> is the path to the MyMatlab directory in your computer. Always

remember to change to this directory after the MATLAB startup, as failure to do so

will result in the input files not being read (details are in the Troubleshooting section).

Prior to running MeshGUI, the Matlab directory and its corresponding

subdirectories must be added to the MATLAB paths. This is done by issuing the

newpath command-line script on the Command Window, and the syntax for doing this

80

for a Windows platform is:

path(path,'C:/<username>/NUMCAT1.0.7/Matlab')

where <username> is the path to the Matlab directory in your computer. (Note that the

version of NUMCAT may be different from the author’s, which is 1.0.7, so the user

must make sure that his/her version is added to the newpath. The <username> line does

not need to be typed manually, the simplest way is to open the Matlab directory and

copy the file destination from the address bar. Type the newpath syntax into the

MATLAB Command Window [path(path,’ ’)] and paste the file destination between

the single quotation marks. (This also works for the MLSStartup script. For non-

Windows users and more details on path configuration, please refer to the MATLAB

Help Browser or the MATLAB online documentation on this website:

http://www.mathworks.com/help/matlab/ref/path.html).

A sample startup script is also included with the NUMCAT software package,

placed in the top-level /Matlab directory. Copy this startup script into the MyMatlab

directory (the personal folder where all input files are to be placed, and the MATLAB

current working directory). The user should then open the startup script in MATLAB

and use the MATLAB text editor specify the paths to the Matlab and Matlab Extension

Toolboxes by cutting and pasting the appropriate file destinations into the

corresponding lines in the startup.m program (these lines contain empty quotation

marks and are described by their corresponding comments).

II. MakeF15

Since the MakeF15 GUI is a Perl-based script, Perl must also be installed in the

same platform where MakeF15 is to be executed. For this study, a 32-bit Windows

platform was used, and the corresponding Perl version 5.20.1.1 was downloaded from

www.strawberryperl.com. (64-bit Windows platform users are advised to obtain a

corresponding Perl version from www.activestate.org). The user should also ensure

that the third-party Perl modules Perl/TK and Date::Manip are also installed along with

MakeF15 (they can be obtained from www.cpan.org if they are not included in the Perl

download). An important prerequisite of the MakeF15 GUI is an existing ADCIRC

Boundary and Grid Information File (fort.14), this should be placed in the current

81

working directory where MakeF15 is located (otherwise, an error message will appear

upon startup of the GUI prompting the user to input the fort.14 file).

Prior to the execution of the GUI, the environmental variables to the tidal

database and executable program must be set. On the Windows Command Prompt, the

following commands should be issued (for the shell script)

set TIDE_PROG1=<username>\NUMCAT-

1.0.7\ADCIRC_Utilities\Makef15\fortran\tide_fac.exe

set TIDE_PROG2=<username>\NUMCAT1.0.7\ADCIRC_Utilities\Boundary_Process

ing\Forcings\Tidal\Constituents\Grenoble_FES2004\extract_gren_2004.exe

where username is the path to the user’s specific platform configuration. (Note that

omission of this step will result in an error message saying that tde_fac.exe cannot be

found when one wants to have the tidal parameters generated. Also, the setting of the

environment paths may have to be repeated every time the platform is started up, as the

Command Prompt does not remember the path configurations). Once this is complete,

issue the following command-line script from Command Prompt in order to start the

GUI:

makefort15gui.pl

Select the desired settings for the boundary conditions (please refer to the

MakeF15 PDF and the online ADCIRC fort.15 documentation for a detailed

description of these parameters. Additionally, the parameters inside the GUI have been

hyperlinked to Help pages, and can be accessed by clicking on the desired parameter

name). After complete input to the fort.15 file has been provided, the new fort.15 file

will be generated by saving in the File→Save As menu.

82

APPENDIX C: OBTAINING BATHYMETRY AND COASTLINE DATA

Prior to the creation of the fort.14 file through the MeshCreate software, the

user should make sure that the bathymetry and coastline data corresponding to the

desired region must be located in the MATLAB current working directory (or the input

file directory, which for this study is MyMatlab). This study made use of the NOAA

ETOPO1 1-arc minute Global Relief Model for the bathymetry data, while the

coastline data was obtained from Global Self-consistent, Hierarchical, High-resolution

Geography Database This section describes how to obtain the raw bathymetry and

coastline data, as well as how to reformat the coastline data into a MeshGUI-

compatible format using the supplementary MATLAB program makecoast.

I. OBTAINING BATHYMETRY DATA

1. Access the ETOPO1 bathymetry database by typing

http://maps.ngdc.noaa.gov/viewers/wcs-client/ on the Web browser.

2. Specify an area of interest by inputting the coordinates of the desired region,

making sure to record these for future reference.

3. Select XYZ as the output format. Click the Download button to save the

bathymetry data to the computer.

4. Copy-paste the xyz file into the current working directory.

Figure C.1 NOAA ETOPO1 Bathymetry Grid Extractor Window

II. OBTAINING COASTLINE DATA

83

The GSHHG global shoreline database can be downloaded from the NOAA

http://www.ngdc.noaa.gov/mgg/shorelines/data/gshhg/latest/. Windows platform users

can also download the GEOphysical DAta System - Next Generation (GEODAS-NG)

software in order to facilitate the extraction of the desired region for a particular study

from the GSHHG database. Unzip the GSHHG folder and place it in the GEODAS-NG

directory. A step-by-step procedure for the coastline extraction is given below:

1. Start Coastline Extractor. Click the Start button, then go to Programs -> GEODAS-

NG Smart Start -> Coastline Extractor.

2. Plot coastlines for your area. Choose which coastline dataset you wish to use in the

application from the Coastlines+ top level menu. This includes the GEODAS

Coastlines (not updated), GSHHS Shorelines (including WDBII political bounds

and rivers), or any ESRI shape file formatted in Polyline style. Use the View menu

or Zoom Button to zoom to the general area for which you are interested. For

GEODAS Coastlines and GSHHS Shorelines you can choose resolutions and

shoreline types you wish to display. (For a more accurate representation of the

desired coastline, it is recommended that the full-resolution database be used.).

3. Select area for saving. The area for saving coastline data is the current plot. You

can use your mouse or a dialog to choose this area.

4. Save your Coastline as File. First you will use the Choose Coastline File Format

Dialog to determine the output format (including ESRI Shape File) for the saved

file. Then use the File... Save As Dialog to determine file name and location. It you

don't put an extension on your file name; the standard extension for that format will

be appended to your file name. Make sure to save the file using the MATLAB

ASCII Coastline (.mat) format for compatibility with the makecoast.

84

Figure C.2 GEODAS Coastline Extractor Window

After the coastline extraction has been completed, the file should be converted into a

MeshGUI-compatible format as follows:

1. Start MATLAB and type makecoast on the Command Window.

2. Read the downloaded segmented shoreline data file by typing

c = load(‘mayport.dat’)

where ‘mayport.dat’ is the file name of the user’s coastline file.

3. Execute the makecoast utility:

makecoast(c,0.000001)

NOTE: the second argument, “0.000001”, is a tolerance used to determine the

minimum distance between connected points. A very small value for the

tolerance is recommended, especially with higher resolution coastline data.

4. Follow the dialogue in the command window or at the top of each screen:

a. Check for duplicate segments? Choose (1)

b. Time to delete unnecessary points from the coastline. Left click to start

zooming into the region of interest. Once zoomed in, right click to get to

selection mode. Use the middle mouse button to “rubber band” around points to

85

be removed. Left click to add the selected points to the list to be deleted. Repeat

to remove additional points. Right click to end point removal.

c. Click Done in main menu

d. Interactively select land segments that can be joined. Use the middle mouse

button to “rubber band” around any land segments that can be joined, then right

click to finish selection, and then middle click to join the segments. Right click

to go to the next step, then right click again to finish this step.

e. Interactively select segments that can be marked as islands. Examine for

correctness: islands are shown in red, and mainland segments are plotted in

blue. In particular, look for blue “islands” that are actually mainland segments;

these must be selected to be marked as islands. Left click to start zooming into

the region of interest. Once zoomed in, right click to get to selection mode. Use

the middle mouse button to select segments or to “rubber band” around

segments to be marked. Right click to exit this mode, then click Done in the

menu.

f. Add boundary segment? Choose (3) NOTE: The fewer line segments the better.

Ideally, you should end up with one or two segments depending upon the

number of open water boundaries.

g. Number of boundary to be added, select (1)

h. Add control point, select (1) circular arc.

i. Enter number of points for current segment, use default 20 Note: Define open

boundary segments. When using the circular arc, always select points in a

counterclockwise order. You can only pick 3.control points, first pick a start

point, when click for the second point, extend point away from the coastline to

make a wide, nice arc, and the third point is the ending point, click right mouse

button to end.

j. Delete islands outside external boundary? Choose (1)

k. Keep all the islands? Choose (1)

l. Save the new boundary file as mayport. A .bdy extension will be appended to

the end of the filename.

86

Figure C.3 Makecoast Window

87

APPENDIX D: REFORMATTTING WRF METEOROLOGICAL DATA INTO

AN ADCIRC-COMPATIBLE FORT.22 FILE

In order to reformat the WRF output, the NWS option in the fort.15 file must be

set to 5, with the file having for 4 columns, namely Mesh Node ID, U=10 (10-m wind

at x-direction), V=10 (10-m wind at y-direction) and SFC (surface pressure). One

could match the meteorological data at the various latitude/longitude values to their

corresponding mesh node number using the following MATLAB code from

NUMCAT:

nodes = [1:size(fem.x)]'; % List of node numbers

fid = fopen('mynodes.xyz', 'w');

fprintf(fid, '%d %f %f\n', [nodes, fem.x, fem.y]');

fclose(fid);

This will create an ASCII text file named mynodes.xyz in the current working

directory, with the node number, longitude, and latitude values, respectively (R.

Linzell, personal communication, 2015)

Here is a sample of how the output would appear:

1 120.201243 14.870840

2 120.199557 14.870152

3 120.199018 14.869868

4 120.215547 14.876970

5 120.213759 14.876539

88

APPENDIX E: NAMELIST.INPUT FILE

&time_control

run_days = 15,

run_hours = 0,

run_minutes = 0,

run_seconds = 0,

start_year = 2013, 2013,

start_month = 11, 11,

start_day = 01, 01,

start_hour = 00, 00,

start_minute = 00, 00,

start_second = 00, 00,

end_year = 2013, 2013,

end_month = 11, 11,

end_day = 16, 16,

end_hour = 00, 00,

end_minute = 00, 00,

end_second = 00, 00,

interval_seconds = 21600,

input_from_file = .true., .true.,

history_interval = 180, 180,

frames_per_outfile = 1000, 1000,

restart = .false.,

restart_interval = 5000,

io_form_history = 2,

io_form_restart = 2,

io_form_input = 2,

io_form_boundary = 2,

debug_level = 0,

/

&domains

time_step = 120,

time_step_fract_num = 0,

time_step_fract_den = 1,

max_dom = 2,

e_we = 100, 88,

e_sn = 100, 85,

89

e_vert = 30, 30,

p_top_requested = 5000,

num_metgrid_levels = 27,

num_metgrid_soil_levels = 4,

dx = 20500, 6833.333,

dy = 20500, 6833.333,

grid_id = 1, 2,

parent_id = 1, 1,

i_parent_start = 1, 36,

j_parent_start = 1, 35,

parent_grid_ratio = 1, 3,

parent_time_step_ratio = 1, 3,

feedback = 1,

smooth_option = 0,

/

&physics

mp_physics = 3, 3,

ra_lw_physics = 1, 1,

ra_sw_physics = 1, 1,

radt = 30, 30,

sf_sfclay_physics = 1, 1,

sf_surface_physics = 2, 2,

bl_pbl_physics = 1, 1,

bldt = 0, 0,

cu_physics = 1, 1,

cudt = 5, 5,

isfflx = 1,

ifsnow = 0,

icloud = 1,

surface_input_source = 1,

num_soil_layers = 4,

sf_urban_physics = 0, 0,

maxiens = 1,

maxens = 3,

maxens2 = 3,

maxens3 = 16,

ensdim = 144,

/

90

&fdda

/

&dynamics

w_damping = 0,

diff_opt = 1,

km_opt = 4,

diff_6th_opt = 0, 0,

diff_6th_factor = 0.12, 0.12,

base_temp = 290.,

damp_opt = 0,

zdamp = 5000., 5000.,

dampcoef = 0.2, 0.2,

khdif = 0, 0,

kvdif = 0, 0,

non_hydrostatic = .true., .true.,

moist_adv_opt = 1, 1,

scalar_adv_opt = 1, 1,

/

&bdy_control

spec_bdy_width = 5,

spec_zone = 1,

relax_zone = 4,

specified = .true., .false.,

nested = .false., .true.,

/

&grib2

/

&namelist_quilt

nio_tasks_per_group = 0,

nio_groups = 1,

91

APPENDIX F: ADCIRC MODEL PARAMETER AND BOUNDARY

CONDITION (FORT.15) FILE

Typhoon Haiyan ! RUNDES - 32 CHARACTER ALPHANUMERIC RUN

DESCRIPTION

ADCIRCV50 ! RUNID - 24 CHARACTER ALPANUMERIC RUN

IDENTIFICATION

1 ! NFOVER - NONFATAL ERROR OVERRIDE OPTION

0 ! NABOUT -ABBREVIATED OUTPUT OPTION PARAMETER

1 ! NSCREEN - UNIT 6 OPTION PARAMETER

0 ! IHOT - HOT START PARAMETER

2 ! ICS - COORDINATE SYSTEM SELECTION PARAMETER

0 ! IM - MODEL SELECTION PARAMETER

1 ! NOLIBF - BOTTOM FRICTION TERM SELECTION PARAMETER

2 ! NOLIFA - FINITE AMPLITUDE TERM SELECTION PARAMETER

0 ! NOLICA - SPATIAL DERIVATIVE CONVECTIVE SELECTION PARAMETER

0 ! NOLICAT - TIME DERIVATIVE CONVECTIVE TERM SELECTION

PARAMETER

0 ! NWP - VARIABLE BOTTOM FRICTION AND LATERAL VISCOSITY

OPTION PARAMETER

1 ! NCOR - VARIABLE CORIOLIS IN SPACE OPTION PARAMETER

1 ! NTIP - TIDAL POTENTIAL OPTION PARAMETER

5 ! NWS - WIND STRESS AND BAROMETRIC PRESSURE OPTION

PARAMETER

1 ! NRAMP - RAMP FUNCTION OPTION

9.81 ! G - ACCELERATION DUE TO GRAVITY - DETERMINES UNITS

0.02 ! TAU0 - WEIGHTING FACTOR IN GWCE

-2.00 ! DTDP - TIME STEP (IN SECONDS)

0.00 ! STATIM - STARTING TIME (IN DAYS)

2.00 ! REFTIM - REFERENCE TIME (IN DAYS)

92

3600.0 ! WTIMINC

15.00 ! RNDAY - TOTAL LENGTH OF SIMULATION (IN DAYS)

2.00 ! DRAMP - DURATION OF RAMP FUNCTION (IN DAYS)

0.35 0.30 0.35 ! A00 TIME WEIGHTING FACTORS FOR THE GWCE EQUATION

1.00 5 5 0.05 ! H0

124.311130036993 10.4949201923743 ! SLAM0,SFEA0 - CENTER OF CPP

PROJECTION (NOT USED IF ICS=1, NTIP=0, NCOR=0)

0.0030 ! CF - 2DDI bottom friction coefficien

50.0 ! ESLM - LATERAL EDDY VISCOSITY COEFFICIENT; IGNORED IF NWP

=1

0.0 ! CORI - CORIOLIS PARAMETER - IGNORED IF NCOR = 1

4 ! NTIF

K1 ! ALPHANUMERIC DESCRIPTION OF TIDAL POTENTIAL FORCING DATA

FOLLOWING

.141565 0.7292116061E-04 0.693 0.9119 323.3985 ! TPK, AMIGT, ETRF, FFT,

FACET

O1 ! ALPHANUMERIC DESCRIPTION OF TIDAL POTENTIAL FORCING DATA

FOLLOWING

.100514 0.6759774260E-04 0.693 0.8563 280.7621

P1 ! ALPHANUMERIC DESCRIPTION OF TIDAL POTENTIAL FORCING DATA

FOLLOWING

.046843 0.7252294745E-04 0.693 1.0000 42.6520

M2 ! ALPHANUMERIC DESCRIPTION OF TIDAL POTENTIAL FORCING

DATA FOLLOWING

.242334 0.1405189105E-03 0.693 1.0302 247.3652

4 ! NBFR

K1 ! ALPHANUMERIC DESCRIPTION OF TIDAL POTENTIAL FORCING DATA

FOLLOWING

0.7292116061E-04 0.9119 323.3985 ! AMIG, FF, FACE

93

O1 ! ALPHANUMERIC DESCRIPTION OF TIDAL POTENTIAL FORCING DATA

FOLLOWING

0.6759774260E-04 0.8563 280.7621

P1 ! ALPHANUMERIC DESCRIPTION OF TIDAL POTENTIAL FORCING DATA

FOLLOWING

0.7252294745E-04 1.0000 42.6520

M2 ! ALPHANUMERIC DESCRIPTION OF TIDAL POTENTIAL FORCING

DATA FOLLOWING

0.1405189105E-03 1.0302 247.3652

K1 ! ALPHA NUMERIC DESCRIPTION OF ELEVATION BOUNDARY FORCING

0.18788250 79.74288940 ! EMO, EFA

110.0 ! ANGINN : INNER ANGLE THRESHOLD

-1 0.00 15.00 120 ! NOUTE,TOUTSE,TOUTFE,NSPOOLE: ELEV STATION

OUTPUT INFO (UNIT 61)

8 ! NSTAE : TOTAL NUMBER OF ELEVATION RECORDING STATIONS

12.00 120.12

10.15 124.43

11.14 125.02

10.19 123.59

11.35 122.45

11.47 124.53

10.42 122.34

10.24 122.56

-1 0.00 15.00 120 ! NOUTV,TOUTSV,TOUTFV,NSPOOLV:VEL STATION

OUTPUT INFO (UNIT 62)

8 ! NSTAV : TOTAL NUMBER OF VELOCITY RECORDING STATIONS

12.00 120.12

10.15 124.43

11.14 125.02

10.19 123.59

94

11.35 122.45

11.47 124.53

10.42 122.34

10.24 122.56

0 0.00 0.00 0 ! NOUTM,TOUTSM,TOUTFM,NSPOOLM:METEO STATION

OUTPUT INFO (UNITS 71/72)

0 ! NSTAM : TOTAL NUMBER OF METEOROLOGICAL RECORDING

STATIONS

-1 0.00 15.00 3600 ! NOUTGE,TOUTSGE,TOUTFGE,NSPOOLGE : GLOBAL

ELEVATION OUTPUT INFO (UNIT 63)

-1 0.00 15.00 3600 ! NOUTGV,TOUTSGV,TOUTFGV,NSPOOLGV : GLOBAL

VELOCITY OUTPUT INFO (UNIT 64)

0 0.00 0.00 0 ! NOUTGW,TOUTSGW,TOUTFGW,NSPOOLGW : GLOBAL WIND

OUTPUT INFO

0 ! NFREQ - NUMBER OF CONSTITUENTS TO BE INCLUDED IN HARMONIC

ANALYSIS

0.00 0.00 0 0.00 ! THAS ,THAF,NHAINC,FMV - HARMONIC ANALYSIS

PARAMETERS

0 0 0 0 ! NHASE - CONTROL HARMONIC ANALYSIS AND OUTPUT TO UNITS

51,52,53,54

0 0 ! NHSTAR ,NHSINC - HOT START FILE GENERATION PARAMETERS

1 0 1.0e-09 50 ! ITITER, ISLDIA, CONVCR, ITMAX & ILUMP

95

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