Modelling the Typhoon Haiyan Storm Surge on Eastern Philippines Using Weather Research and Forecasting-Advamced Circulation

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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−𝑓𝑃 𝑦
𝜌(𝑘2+𝑓2)
(2.1)
𝑣 =
−𝑘𝑃𝑥+𝑓𝑃 𝑦
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
2
𝜌𝑣2
𝐶D 𝐴 (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 = 𝑃𝑜 𝑒
−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 (
𝑀𝑔𝑍 𝑔
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
(𝜏 𝑤 + 𝜏 𝐵) (2.10)
Δ𝜂 =
𝐾Δ𝑥𝜏 𝑤
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:

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𝜕𝑆 𝑥𝑥
𝜕𝑥
= 𝜌𝑔( 𝜂̅ + 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
𝜕𝑆 𝑥𝑥
𝜕𝑥
> 0 and 𝜂̅ decreases, assuming that wave heights increase.
On the other hand, wave set-up refers to the condition where
𝜕𝑆 𝑥𝑥
𝜕𝑥
< 0 and 𝜂̅ 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 𝑥̃
𝜕𝑥
+
∂ 𝐽 𝑦̃
𝜕𝑦
+ ( 𝑈𝐻)
𝜕𝜏0
𝜕𝑥
+ ( 𝑉𝐻)
𝜕𝜏0
𝜕𝑦
= 0 (2.13)

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where
𝐽 𝑥
̃ = −Qx
𝜕𝑈
𝜕𝑥
– 𝑄 𝑦
𝜕𝑈
𝜕𝑦
+ 𝑓𝑄 𝑦 −
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 𝑦
̃ = −Qx
𝜕𝑉
𝜕𝑥
– 𝑄 𝑦
𝜕𝑉
𝜕𝑦
+ 𝑓𝑄 𝑋 −
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
ρ0 𝐻
+
𝑀 𝑥−𝐷 𝑥
𝐻
(2.16)
𝜕𝑉
𝜕𝑡
+ 𝑈
𝜕𝑉
𝜕𝑥
+ 𝑉
𝜕𝑉
𝜕𝑦
− 𝑓𝑉 =
𝑔
𝜕
𝜕𝑦
[ζ +
𝑃𝑠
gρ0
− αη] +
τsy;wind +τsy;waves−τby
ρ0 𝐻
+
𝑀 𝑦−𝐷 𝑦
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
-0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
-0.5
0
0.5
1
1.5
2
2.5
0 1 2 3 4 5 6 7
Elevation(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
Elevation(m)
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
Elevation(m)
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.

Modelling the Typhoon Haiyan Storm Surge on Eastern Philippines Using Weather Research and Forecasting-Advamced Circulation

Modelling the Typhoon Haiyan Storm Surge on Eastern Philippines Using Weather Research and Forecasting-Advamced Circulation

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