A First Generation Dynamical Tropical Cyclone Storm Surge Forecast System Part 1: Hydrodynamic model

Diana Greenslade, Andy Taylor, Justin Freeman, Holly Sims, Eric Schulz, Frank Colberg, Prasanth Divakaran, Mirko Velic and Jeff Kepert

October 2018

Bureau Research Report - 031

A FIRST GENERATION DYNAMICAL TROPICAL CYCLONE STORM SURGE FORECAST SYSTEM PART 1: HYDRODYNAMIC MODEL

A FIRST GENERATION DYNAMICAL TROPICAL CYCLONE STORM SURGE FORECAST SYSTEM PART 1: HYDRODYNAMIC MODEL

A First Generation Dynamical Tropical Cyclone Storm Surge Forecast System Part 1: Hydrodynamic model

Diana Greenslade, Andy Taylor, Justin Freeman, Holly Sims, Eric Schulz, Frank Colberg, Prasanth Divakaran, Mirko Velic and Jeff Kepert

Bureau Research Report No. 031

October 2018

National Library of Australia Cataloguing-in-Publication entry Author: Diana Greenslade, Andy Taylor, Justin Freeman, Holly Sims, Eric Schulz, Frank Colberg, Prasanth Divakaran, Mirko Velic and Jeff Kepert Title: A First Generation Dynamical Tropical Cyclone Storm Surge Forecast System Part 1: Hydrodynamic model ISBN: 978-1-925738-08-7

Series: Bureau Research Report – BRR031

Page i

A FIRST GENERATION DYNAMICAL TROPICAL CYCLONE STORM SURGE FORECAST SYSTEM PART 1: HYDRODYNAMIC MODEL

Enquiries should be addressed to:

Diana Greenslade:

Bureau of Meteorology GPO Box 1289, Melbourne Victoria 3001, Australia [email protected]:

Copyright and Disclaimer

© 2016 Bureau of Meteorology. To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of the Bureau of Meteorology.

The Bureau of Meteorology advise that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law and the Bureau of Meteorology (including each of its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

Page ii

A FIRST GENERATION DYNAMICAL TROPICAL CYCLONE STORM SURGE FORECAST SYSTEM PART 1: HYDRODYNAMIC MODEL

Contents

ABSTRACT ...... 1 1. Introduction ...... 2 2. Other operational storm surge forecast systems ...... 4 3. Surface forcing ...... 6 4. Hydrodynamic modelling...... 9 4.1 Model configuration ...... 10 4.2 Offshore territories ...... 11 4.3 Subsetted domain ...... 13 4.4 Wave set-up ...... 14 5. Verification ...... 15 5.1 Observations ...... 16 5.2 Summary of Performance ...... 17 5.3 Case Study results ...... 20 5.3.1 TC Anthony ...... 20 5.3.2 TC Yasi ...... 22 5.3.3 TC Ita ...... 25 5.3.4 TC Lam ...... 28 5.3.5 TC Marcia ...... 29 5.3.6 TC Olwyn ...... 31 5.3.7 TC Nathan ...... 33 5.4 Comparison with existing systems ...... 34 6. Further Work ...... 38 7. Acknowledgements...... 40 8. References ...... 41

Page iii

A FIRST GENERATION DYNAMICAL TROPICAL CYCLONE STORM SURGE FORECAST SYSTEM PART 1: HYDRODYNAMIC MODEL

List of Figures

Fig. 1 Velocity magnitude and velocity vectors from Best Track data for TC Yasi on 2 February 2011 at 0400 UTC. The calculated velocity field includes storm forward motion, friction and inflow angle correction...... 8 Fig. 2 Domain of the full tropical grid. This is subsetted for each run...... 10 Fig. 3 Spatial extents of all grids tested for Christmas Island, with a snapshot of storm surge height at 3:00 UTC on 22nd March 2014 from TC Gillian overlaid...... 12 Fig. 4 Time series of maximum value of sea level in each grid...... 13 Fig. 5 Best Tracks for the seven events examined...... 16 Fig. 6 Hourly fixes of the Best Track for TC Anthony (light blue crosses) and location of 3 tide gauges (red diamonds) used for verification ...... 20 Fig. 7 Left hand panel shows the location of the Bowen tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Bowen station data (black diamonds) during TC Anthony compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up. 21 Fig. 8 Same as Fig. 7 but for Shute Harbour ...... 21 Fig. 9 Same as Fig. 7 but for Laguna Quays ...... 21 Fig. 10 Hourly fixes of the Best Track for TC Yasi (blue crosses) and location of the 6 tide gauges (red diamonds) used for verification ...... 22 Fig. 11 Left hand panel shows the location of the Cairns tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Cairns station data (black diamonds) during TC Yasi compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up. 23 Fig. 12 Same as Fig. 11 but for Mourilyan ...... 23 Fig. 13 Same as Fig. 11 but for Clump Point ...... 23 Fig. 14 Same as Fig. 11 but for Cardwell ...... 24 Fig. 15 Same as Fig. 11 but for Townsville ...... 24 Fig. 16 Same as Fig. 11 but for Cape Ferguson ...... 24 Fig. 17 Hourly fixes of the Best Track for TC Ita (green crosses) and location of the 6 tide gauges (red diamonds) used for verification...... 25 Fig. 18 Left hand panel shows the location of the Cooktown tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Cooktown station data (black diamonds) during TC Ita compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set- up. 26 Fig. 19 Same as Fig. 18 but for Cairns ...... 26 Fig. 20 Same as Fig. 18 but for Cardwell ...... 26 Fig. 21 Same as Fig. 18 but for Townsville ...... 27 Fig. 22 Same as Fig. 18 but for Cape Ferguson ...... 27

Page iv

A FIRST GENERATION DYNAMICAL TROPICAL CYCLONE STORM SURGE FORECAST SYSTEM PART 1: HYDRODYNAMIC MODEL

Fig. 23 Same as Fig. 18 but for Bowen ...... 27 Fig. 24 Hourly fixes of the Best Track for TC Lam (pink crosses) and location of the 2 tide gauges (red diamonds) used for verification...... 28 Fig. 25 Left hand panel shows the location of the Weipa tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Weipa station data (black diamonds) during TC Lam compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up. 29 Fig. 26 Same as Fig. 25 but for Groote Eylandt ...... 29 Fig. 27 Hourly fixes of the Best Track for TC Marcia (orange crosses) and location of the 2 tide gauges (red diamonds) used for verification...... 30 Fig. 28 Left hand panel shows the location of the Rosslyn Bay tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Rosslyn Bay station data (black diamonds) during TC Marcia compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up...... 31 Fig. 29 Same as Fig. 28 but for Port Alma ...... 31 Fig. 30 Hourly fixes of the Best Track for TC Olwyn (purple crosses) and location of the tide gauge (red diamond) used for verification...... 32 Fig. 31 Left hand panel shows the location of the Point Murat tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Point Murat station data (black diamonds) during TC Olwyn compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up...... 32 Fig. 32 Hourly fixes of the Best Track for TC Nathan (red crosses) and location of the tide gauge used for verification (red diamond)...... 33 Fig. 33 Left hand panel shows the location of the Cooktown tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Cooktown station data (black diamonds) during TC Nathan compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up...... 34

Page v

A FIRST GENERATION DYNAMICAL TROPICAL CYCLONE STORM SURGE FORECAST SYSTEM PART 1: HYDRODYNAMIC MODEL

List of Tables

Table 1 Peak values for each grid...... 13 Table 2 Details of the Tropical Cyclones used for verification. Maximum observed surge refers to sea level after removal of astronomical tides and centering of the residuals (see Section 5.1)...... 15 Table 3 Comparison between observed and modelled peak surge amplitude for the 7 TC events studied here. MAEs less than 0.5m are highlighted in green...... 18 Table 4 Comparison between observed and modelled peak surge timing for the seven TC events studied here...... 19 Table 5 Comparison between ROMS and SEAtide errors in peak surge amplitude. All values except percentages in (m). Green shading indicates the lowest absolute error for each site...... 36 Table 6 Comparison between ROMS and SEAtide errors in peak surge timing...... 37

Page vi

ABSTRACT

A new Tropical Cyclone (TC) storm surge forecast system has been developed. This Tropical Storm Surge system is a step-change from previous parametric storm surge systems used operationally at the Bureau of Meteorology. Parametric TC vortices are derived from the Bureau of Meteorology’s Official Forecast Track and its associated ensemble tracks. These are gridded and used to force the hydrodynamic model, ROMS (Regional Ocean Modelling System). Wave set-up (derived from AUSWAVE-R) and astronomical tides are linearly combined with the ROMS storm surge to provide forecasts of coastal sea level at a spatial resolution of approximately 2.5 km around the tropical Australian coastline. Storm surge hindcasts have been evaluated for seven recent TC events using post- analysed ‘Best Track’ TC parameters through comparison with tide gauge observations of coastal sea level. The mean bias in the maximum surge for the seven test cases is -1 cm, suggesting that there is negligible systematic over- or under-prediction in the system. The mean absolute error of peak surge amplitude is 26 cm. This demonstrates a substantial improvement over existing operational systems.

Page 1

1. INTRODUCTION

Storm surge can be defined as an elevation of water level at the coast resulting from strong winds and reduced atmospheric pressure. Storm surges are very often associated with Tropical Cyclones (TCs) as they come onshore and may also be generated by intense low-pressure systems in non-tropical areas. Storm surge typically includes wave setup – an additional elevation of the water level due to wave- breaking. Storm tide is the combination of storm surge and astronomical tide – if a storm surge arrives at high tide, the impacts can be considerably more damaging than if it arrives at low tide.

The Bureau of Meteorology has recently undertaken a project to enhance its operational storm surge forecasting system. The project consists of three key components: 1) an event-based TC ensemble storm surge system implemented for Queensland, the Northern Territory and Western Australia, 2) a national storm surge system for forecasting anomalous sea levels due to mid-latitude storms and tropical lows (Allen et al., 2018), and 3) operationalisation of an existing aggregate sea level monitoring and alert system at tide gauge locations (Taylor and Brassington, 2017). There are many scientific and technical commonalities to each of the three components of the project.

This report describes only the first component, the event-based TC system. Furthermore, it focusses on the hydrodynamic model aspect of this process with an evaluation of the ensemble forecasts to come in a later report. It should also be noted that the first two of the systems listed above are traditionally referred to as ‘storm surge’ systems despite the fact that they ultimately both include the effects of astronomical tides.

Historically, the three relevant Bureau Regional Offices have had their own, different systems for forecasting storm surges due to TCs. Establishing a common, nationally consistent workflow for storm surge forecasts that integrates seamlessly with other systems and procedures is of significant value from an operational perspective. A further rationale for the present development is that the existing systems are all based on parametric and/or scenario-based techniques (e.g. SEAtide, see section 2) which, while fast to run, have some limitations. For example, the parametric technique assumes equilibrium conditions, i.e. that a TC is propagating in a straight line with constant intensity parameters which is not typically the case in reality.

The capability to run a dynamical storm surge model exists, however this has not previously been done on an operational basis for a number of reasons. Firstly, within a TC forecasting environment, the storm surge forecast needs to be produced very rapidly. This was not possible at the time the existing storm surge forecast systems were implemented, but current computational capacity means that this is now possible within operational constraints. Secondly, the Bureau’s Official Forecast Track (OFT) for any

Page 2

individual TC is not directly connected to any one particular NWP model, but is produced from a consensus of a number of different models by forecasters using the TC-Module system (Donaldson and Taylor, 2018). Thus if a storm surge forecast were to be produced using forcing from an individual model such as ACCESS-TC (Davidson et al., 2014; Puri et al., 2013), there would likely be inconsistencies between the storm surge forecast and the OFT. This is highly undesirable when emergency management issues are considered.

A further factor that has previously limited dynamical deterministic storm surge forecasts is that the surge forecast is very sensitive to errors in the TC location, velocity, intensity, size etc. Mean TC track errors for Bureau OFTs during the time period 2010 to 2015 were 97 km for 24 hour forecasts and 156 km for 48 hour forecasts (Australian Bureau of Meteorology, 2016). This suggests that ensemble storm surge forecasting is required in order to take account of the forecast uncertainty. Within TC-Module, there is an application that produces a forecast of wind speed exceedance probabilities from the OFT. The technique is based on the development of an ensemble of TC tracks using the ‘DeMaria’ method (DeMaria et al., 2009) which takes into account historical TC track and intensity errors. This produces an ensemble of TC tracks based on the OFT.

In this project, this TC track ensemble, in addition to the OFT, is used as the basis for forcing an ensemble of storm surge models in order to provide probabilistic storm surge forecasts. There are a number of steps involved in this process. Firstly, it should be noted that the ensemble tracks have been developed to provide wind speed exceedance probabilities, and they are not always physically realistic. Thus, they must be modified to ensure that they are dynamically consistent for forcing a storm surge model. This is documented in Kepert (2014) and is not addressed in this report. For each ensemble member, a series of gridded forcing fields are produced from the TC track. Once the forcing fields have been developed, these are used to force a hydrodynamic model to produce an ensemble of storm surge forecasts, and finally, the resulting surge forecasts are combined with wave set-up and astronomical tides to produce forecasts of storm tide at the coast.

This report focusses on describing the configuration and verification of the hydrodynamic/storm surge model aspect of this process. The forecasts, including ensembles, are not addressed here but will be described and evaluated in a separate document. The report is structured as follows: Section 2 places this work in context by describing some other relevant operational storm surge forecast systems; Section 3 describes the technique of converting the TC vortex to a gridded forcing field; Section 4 describes the model setup and some experiments undertaken with the hydrodynamic model to provide guidance on aspects of model configuration such as spatial resolution and domain; Section 5 presents an evaluation of the system for a number of recent TCs, including a comparison with existing

Page 3

operational systems; and Section 6 presents some indications of further work that could be undertaken to improve the system.

2. OTHER OPERATIONAL STORM SURGE FORECAST SYSTEMS

There are many other countries around the world that are vulnerable to the impacts of TCs and storm surges. Two examples of existing operational systems used to forecast the impacts of storm surge due to TCs are those based at the Japan Meteorological Agency (JMA) and the United States National Weather Service (NWS). These are both based on dynamical storm surge models, and are described below, along with some other relevant developments within Australia.

JMA currently operates an Asian-area storm surge model to provide predictions of surge within the Tokyo Regional Specialised Meteorological Centre (RMSC) area of responsibility (Hasegawa et al., 2017). The hydrodynamic model is based on the vertically integrated shallow water equations and has a spatial resolution of approximately 3.7 km. The domain covers 0°N to 42°N and 95° E to 160°E. The model is forced by surface winds and pressure from the JMA mesoscale atmospheric model (spatial resolution of 0.25° by 0.2°) on an ongoing basis providing 72-hour forecasts every 6 hours. When there is a TC in the region, an additional 5 model runs are undertaken, forced with a simple parametric TC model ‘bogussed’ into the atmospheric forcing. These 5 extra forecasts are selected using cluster analysis on 27 ensemble members of the JMA Typhoon Ensemble Prediction System (Kyouda and Higaki, 2015). Gridded astronomical tides are added to the predicted storm surge to provide forecasts of total sea level. Wave set-up is not currently included.

The U.S. NWS uses the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model (Jelesnianski et al., 1992) for a range of storm surge predictions and hazard assessments (Glahn et al., 2009). For real-time TC storm surge forecasts, it is applied in deterministic mode over 32 domains, or ‘basins’ covering the U.S. East coast and offshore regions. The position of the forecast track determines which basins will be run. SLOSH forcing is provided by parametric TC information from the official forecast (location, radius-to-maximum-winds, pressure gradient). Using ‘best track’ (see section 5) parametric TC information for a set of 13 storms, SLOSH was found to be accurate to within 20% of the peak surge value most of the time, although the errors could be significantly larger (Glahn et al., 2009).

In addition to the deterministic forecast, probabilistic forecasts are derived from the Probabilistic Storm Surge model (P-surge), which is comprised of an ensemble of SLOSH forecasts. The forcing for each ensemble member is created by modifying the official forecast TC position, size and intensity based on past errors, specifically errors in the along-track distance, cross-track distance and intensity (Taylor and

Page 4

Glahn, 2008). Verification of P-surge was undertaken using SLOSH hindcasts, rather than directly using observations, because of the low number of good quality storm surge observations available. There are two main products created from P-surge, both generated on a grid covering the area of interest. The first of these is the probability of storm surge greater than a particular pre-determined value, and the second is the storm surge value that is exceeded by a particular percentage (typically 10%) of the ensemble members.

Within the Bureau of Meteorology’s Tropical Cyclone Warning Centres in Queensland and the Northern Territory, the existing system used to provide operational forecasts of storm surge is the SEAtide system (SEA, 2005, 2016). SEAtide is a parametric modelling system, developed following the approach described in Harper (2001). A number of geographical regions covering the coastline of interest are established and many thousands of potential tropical cyclone scenarios are constructed in order to determine the storm tide response in each region as a function of the incident storm parameters. The storm tide predictions are based on previously developed numerical models of storm tide for specific coastal regions (e.g. SEA, 2002). This information is then summarised into a further numerical parametric model for each region that enables rapid retrieval of response information. For Western Australia (WA), a similar parametric system has been developed based on the GEMS surge model (Hubbert and McInnes, 1999). In order to provide a storm surge forecast, key features of the TC (e.g. location, maximum wind speed, speed of forward movement) 12 hours prior to landfall are used to provide maximum surge values at a specified number of locations along the WA coast. A standard 0.5m is added for wave setup.

There have been a number of research projects within Australia to develop real-time storm surge forecast systems. Under a Queensland state government project, Burston et al. (2013a) proposed a real- time storm surge system for the Queensland coast based on the Bureau of Meteorology ensemble TC tracks. They acknowledged that the raw tracks were not suitable for storm surge forecasting and so a method was developed to reconstitute a set of tracks from the ensemble. Forcing fields were generated using a Holland et al. (2010) parametric TC profile. Hydrodynamic modelling was undertaken using the MIKE21 Flexible Mesh model (Warren and Bach, 1992). Tides were linearly added to storm surge forecasts but wave set-up was not incorporated. An inundation modelling approach was also developed at higher spatial resolution (80-100m) but found to be too computationally expensive to run in real-time (Burston et al., 2013b).

A previous research and development activity at the Bureau of Meteorology (Davidson et al., 2005) involved the development of a storm surge forecast system that was forced by TC-LAPS, the previous operational TC NWP system (Davidson and Weber, 2000). This system used a 2D shallow water model

Page 5

developed by Sanderson (1997) and incorporated a technique to account for possible errors in the TC- LAPS forecast through manual adjustment of the TC parameters.

3. SURFACE FORCING

The accuracy of a storm surge model forecast is heavily reliant on the quality of the atmospheric forcing used. It is well known that standard NWP models are not typically able to capture the intensity of a Tropical Cyclone, so often, specialised TC models are used (e.g. ACCESS-TC). However, as noted in the Introduction, for an operational forecast system, it is not ideal to run a storm surge model under a specific NWP model, so for this project an ensemble of gridded surface wind and pressure fields is derived from a series of synthetic vortex parameters obtained from TC Module. A further step could be to blend the gridded surface fields with a background NWP field to provide a more physically realistic forcing field. This is not done for the present system, but is discussed in Section 6 as a possible future development.

A wide variety of TC vortex profile functions are available. For this project, the modified Rankine vortex (Hughes, 1952) is used, mainly because it is also used in the wind radius prediction component of the De Maria ensemble generation scheme. This choice thus has the virtue of being consistent with the wind radius generation method. In addition, recent work has demonstrated that the modified Rankine vortex is more accurate than alternative profiles such as the Holland vortex for storm surge and wave modelling (e.g. Bastidas et al., 2016). The formulation of the vortex described here incorporates asymmetry in the wind fields induced by the storm forward motion and an inflow angle correction to the gradient wind field.

In the present report, we focus on storm surge hindcasts using 'Best Track' forcing. The 'Best Track' for any TC is a time series of TC parameters, produced by forecasters, or other analysts, after the end of the TC season, and taking into account all available observations. The specific parameters required from the Best Track data to describe the TC at any time are:

1. Longitude of the storm centre,  2. Latitude of the storm centre, 

3. Maximum wind speed, Vm

4. Radius of maximum wind, rm 5. A cyclone size parameter, α (0 < α < 1)

Page 6

The velocity profile of the modified Rankine vortex is calculated through the radial integration of the axisymmetric vorticity following Kepert (2013). Storm asymmetry is introduced by radially modifying the velocity distribution to include the storm forward motion velocity:

푉(푟, 휃) = 푉(푟) + 훿푓푚푉푓푚 sin 휃 (3.1)

where θ is the angle relative to the storm direction, δfm is the fraction of forward motion and is in the range 0.5 to 1 and Vfm is the storm forward motion velocity. Under this formulation, for southern hemisphere storms, Vm is located 90 anticlockwise to the storm direction vector. The location of Vm can be anywhere from 65 to 114 (Harper, 2001). In the subsequent model we locate the storm maximum velocity at 65 anticlockwise from the storm direction and use δfm = 0.5. The storm velocity is calculated using a forward difference of best track data where the distance covered by the storm over the time period between consecutive fixes is determined by the haversine formula. Setting Vfm = 0, the vortex reduces to the axisymmetric structure.

The best track velocity data is modified according to equation (3.1) and we use the median velocity of the best track sector data to determine the wind speed at 64 nm, 48 nm and 34 nm radii. A lower bound

−1 of 0.1 ms for Vm is imposed.

The application of equation (3.1) to the symmetric formulation given by Kepert (2013) results in the the radial velocity no longer flowing inward along isobars. The inflow angle is (Phadke et al., 2003)

푟 10 (1 + ) 푟 ≤ 푟푚 푟푚 푟 훽 = 20 + 25 ( − 1) 푟 ≤ 푟 < 1.2푟 (3.2) 푟 푚 푚 푚 { 25 푟 ≥ 1.2푟푚 and the angle correction is applied by rotating the gradient wind vectors inward by β,

푢푟 = 푢 cos(−훽) − 푣 sin(−훽) (3.3)

푣푟 = 푢 sin(−훽) + 푣 cos(−훽) (3.4)

The conversion of 10 m winds, u10, to surface stress follows Large and Pond (1981) with the drag coefficient capped in accordance with Powell et al (2003),

−1 1.2 푢10 < 10.92 푚푠 3 −1 −1 퐶푑10 = {0.49 + 0.065푢10 10.92 푚푠 ≤ 푢10 < 23.23 푚푠 (3.5) −1 2.635 푢10 ≥ 23.23 푚푠

Page 7

Fig. 1 Velocity magnitude and velocity vectors from Best Track data for TC Yasi on 2 February 2011 at 0400 UTC. The calculated velocity field includes storm forward motion, friction and inflow angle correction.

An example velocity field for TC Yasi using the best track data at 0400 UTC on 2 February 2011 is given in Fig. 1.

Atmospheric pressure is given by

푟 푉2(푠) 푃(푟) = 푃 + ∫ 휌 ( + 푓푉(푠)) 푑푠 (3.6) 푐 0 푠

3 where 푃푐 is the minimum central pressure in hPa, and ρ = 1.15 kg m is density. The central pressure of the storm is calculated using the wind-pressure relationship of Knaff and Zehr (2007) with the latitudinal corrections suggested by Courtney and Knaff (2009).

In its operational configuration, the stress and pressure forcing fields are generated on a 0.5° resolution grid and then interpolated onto the hydrodynamic model grid using a cubic interpolation method.

Page 8

4. HYDRODYNAMIC MODELLING

The Regional Ocean Modelling System (ROMS; Shchepetkin and McWilliams, 2005) was selected as the hydrodynamic modelling component for both the TC event-based storm surge system and the national system. This was based on a comprehensive review of a large number of possible numerical models (Colberg et al., 2013) and a strategy to limit the number of different modelling systems used within the Bureau of Meteorology for coastal ocean modelling applications. ROMS is a hydrostatic, primitive equation model, featuring a nonlinear free-surface.

For the present storm surge implementation, ROMS solves the depth-averaged momentum and continuity equations:

휕푼 흉 −흉 + 푼. ∇푼 + 풇 × 푼 = −푔∇η + 푠 푏 + 풗 (4.1) 휕푡 휌퐻 휕휂 1 ∂푃 + 퐻∇. 퐔 = (4.2) 휕푡 푔휌 ∂푡 where 푼 = (푢̅, 푣̅) is the depth averaged velocity, t is time, 풇 is the Coriolis term, 푔 is the gravitational constant,  is the free surface height, 휌 is density, H is the total depth, 흉푠 and 흉푏 are the surface and bottom stress respectively, 풗 is the viscosity and P is atmospheric pressure. By not including any vertical structure in the model, some modes of variability may not be included or well simulated. However, it is likely that the baroclinic component is very weak (Peng and Li, 2015) and it is reasonable to ignore it, particularly for TC surge. This approach is consistent with the National storm surge system (Allen et al., 2018) and also current international practice, as discussed in Section 2.

At the domain boundaries the normal component of the depth-average velocity is subject to the Flather boundary condition (Flather, 1976) while the sea level  is subject to a Chapman boundary condition with zero external forcing (Chapman, 1985). Other details of the model configuration are as follows. Spatially uniform quadratic bottom drag is used, with a drag coefficient of 1 x 10-3. The bathymetry data used is the Geoscience Australia 9-arc second bathymetry (Whiteway, 2009) merged with the 30- arc second GEBCO_2014 Grid, (version 20150318, http://www.gebco.net) for the area north of 8oS (where the Geoscience Australia data does not exist). The time step is 6 seconds. Wetting/drying is turned on with a critical depth of 0.1m. During a computational step, if the total water depth is less than the critical depth then no flux is allowed out of that cell, however, water can flow into the cell. The land-sea mask was generated using the zero contour level of the interpolated bathymetry and manually post-processed to remove single cell land points, isolated wet cells and single cell channels and bays.

Page 9

4.1 Model configuration

There is a balance to be found between specifying a large domain in order to fully capture the relevant atmospheric forcing, and a small domain in order to run the model as quickly as possible. In addition, there is a balance to be found between specifying fine resolution in order to better resolve the variability of the sea level and describe the coastline more realistically, and low resolution in order to reduce computational cost.

For the full tropical domain, a ‘ribbon’ grid was selected in preference to a rectangular grid because: a) for the domain of interest, i.e. the tropical coastline of Australia, it will have inherently higher resolution near the coast compared to offshore, and b) a ribbon grid is more computationally efficient for ROMS as it minimises the number of land points used over the continent. A domain was established, covering all anticipated latitudes within which a Tropical Cyclone might make landfall (see Fig. 2). Eight grids of different spatial resolutions covering this domain were tested. Note that the characteristics of the ribbon grid means that the spatial resolution across the grid is not uniform. The mean resolutions of the grids tested ranged from 1.26 km (929 x 6273 grid points) to 10 km (117 x 785 grid points).

Fig. 2 Domain of the full tropical grid. This is subsetted for each run.

A number of synthetic storms were modelled for each of the eight grid configurations, and the results were assessed on the basis of: 1) location of maximum sea-level anomaly, 2) root-mean-square (rms) variability of sea-level anomaly in a small region (approximately 50 km by 50 km) surrounding the location of the maximum, and 3) computational time.

The impact of spatial resolution on sea-level variability was found to be relatively small. The approach used was as follows. The average rms value (part (2) above) for each storm was plotted as a function of the inverse of the total grid size. A line of best fit extrapolated to the origin therefore

Page 10

provides an approximate value of the rms for a hypothetical ‘infinite’ resolution grid. With this approach, the coarsest grid examined showed a difference of 5% in rms sea-level variability compared to the hypothetical ‘infinite’ resolution grid, and the finest resolution grid examined showed a difference of 1%. Based on this analysis and requirements from users, the configuration selected was a grid with a mean resolution of 2.5 km. Spatial resolutions of the coastal cells within this grid range from 1.9 km to 4 km. This grid showed a difference in rms sea-level variability of approximately 2% compared to an ‘infinite’ grid.

4.2 Offshore territories

In addition to mainland Australia, there is a requirement to produce storm surge forecasts for a number of offshore territories, namely Cocos Island, Christmas Island, Norfolk Island and Lord Howe Island. Macquarie Island is not included for the tropical system because it is highly unlikely that a TC would impact there.

Lord Howe Island is located within the tropical grid shown in Fig. 2, but Cocos, Christmas, and Norfolk Islands are not, so they require separate grids to be developed. Some previous studies undertaking storm surge modelling for small islands with steep continental slopes have used relatively small grids surrounding the area of interest. For example, McInnes et al. (2014) used a domain size of approximately 500 km x 500 km surrounding the Fiji archipelago to model storm surge impacts there. Conversely, Kennedy et al. (2012) used a domain size approximately 3,000km by 3,000 km surrounding the Hawaiian Islands in their study of storm surge inundation. A relevant point is that the former study did not include wind-wave processes and the latter did. This may explain the difference in domain size, because a larger domain is desirable for modelling wind-wave processes in order to ensure that the fetch is included. For the present study, the wave estimates are obtained from an alternate source (see Section 4.4), so we do not need to ensure that the full wave fetch is covered.

In order to establish the optimal domain size for the Australian offshore territories, a series of 9 grids of different spatial extents (‘Grid 0’ being the smallest grid, and ‘Grid 8’ the largest grid) was developed encompassing Christmas Island. These are shown in Fig. 3. All grids were rectangular, with the same spatial resolution of 2 km. In this experiment, we consider the largest grid (Grid 8) to be the ‘truth’ and assess how the results degrade as the domain is reduced. Sea-level observations are not used here as the intent is to assess the relative difference in sea level.

Page 11

Fig. 3 Spatial extents of all grids tested for Christmas Island, with a snapshot of storm surge height at 3:00 UTC on 22nd March 2014 from TC Gillian overlaid.

The forcing used for this series of experiments was an eight-day time series of hourly surface stress and pressure during TC Gillian, which passed very close to Christmas Island in March 2014 (Bureau of Meteorology, 2014a). Surface forcing fields were derived from the Best Track vortex details for TC Gillian1 merged into ACCESS-R. For each grid, the maximum sea level was found within the region of the smallest grid (i.e. grid 0) and is listed in Table 1. The maximum values for grids 0 to 6 are also plotted as a function of time in Fig. 4. (Grids 7 and 8 are not shown here as they are very similar to grid 6). It can be seen that as the grids become larger, the maximum values converge. Little change can be seen once the grid becomes larger than grid 6, and so grid 6 is used here, which has a spatial extent of approximately 1,000 km by 1,000 km. The domains for Norfolk Island and Cocos Island are also set to this size.

1 The pressure and wind stress fields used in this section were obtained using an early version of the forcing field derivation technique, which was later abandoned. However, since the same forcing was used for all experiments, and the intent is to assess the relative difference in sea levels, it was not necessary to re-compute the forcing fields.

Page 12

Grid Nx by Ny Peak Value (m) % difference from previous peak value Grid 0 201 x 251 0.3022 - Grid 1 255 x 306 0.3189 5.5% Grid 2 309 x 361 0.3310 3.8% Grid 3 364 x 416 0.3419 3.3% Grid 4 418 x 472 0.3539 3.5% Grid 5 473 x 527 0.3627 2.5% Grid 6 527 x 582 0.3700 2.0% Grid 7 582 x 638 0.3765 1.8% Grid 8 693 x 636 0.3778 0.35%

Table 1 Peak values for each grid

Fig. 4 Time series of maximum value of sea level in each grid.

4.3 Subsetted domain

In order to reduce computational resources in its operational configuration, the full domain shown in Fig. 2 is not used for an individual TC. Instead, a subset of the full grid is defined as a function of the

Page 13

forecast track (or tracks, in the case of an ensemble.) The geospatial extent of the subsetted grid is determined from the minimum and maximum longitude values of all ensemble tracks. An additional padding (currently set at 1,000 km) is applied to the spatial extent of the grid to ensure that the model domain extent is sufficient to resolve the oceanic dynamics. This means that as the OFT and ensemble tracks change for different base-times, i.e. between forecasts, the subsetted domain may be different.

4.4 Wave set-up

Wave setup is an additional elevation of water level at the coast due to wave breaking. This process transfers momentum from the wave field to the depth integrated water column in the surf zone. This is distinct from wave overtopping, or wave run-up, which relate to the impact of individual waves on shoreline structures or beaches. Wave setup can be a significant contribution to the total coastal sea level during Tropical Cyclones, particularly in regions with steep continental slopes (e.g. Kennedy et al., 2012), such as much of the east coast of Australia.

It is possible to directly calculate the wave set-up from a spectral wave model such as SWAN or potentially WAVEWATCH3 (it is not currently incorporated in WW3 so would require additional effort) using radiation stress theory. This is not possible at present due to the computational cost. In addition, the spatial resolution of national or regional scale wave models does not typically allow estimates of wave spectra close enough to the coastline. Therefore, wave set-up is typically parametrised based on estimates of the wave field some distance offshore.

For the initial implementation of the Tropical Storm Surge System the wave setup is calculated from deterministic AUSWAVE-R operational forecasts (Durrant and Greenslade, 2011; BNOC, 2016). This is the approach developed by CSIRO for their contribution to the storm surge project (O’Grady et al. 2015). The advantage of this approach is that it is based on an existing Bureau operational system and the computational cost is minimal. However there are some disadvantages to this approach. These are identified and briefly discussed in Section 6 and further details may be found in Greenslade (2016).

The parametrisation of wave set-up ( o ) was derived from multiple simulations around the Australian coast made with the SWAN nearshore wave model and is calculated as follows:

1 −  1  H  4 7  s  (4.3) o = 0.31(0.8)H s 0.32S     Lo  where Hs is Significant Wave Height (from AUSWAVE-R), Lo is peak wavelength (from AUSWAVE- R) and S is the bathymetric slope. Bathymetric slopes were determined around the Australian coastline from the ROMS grid interpolated bathymetry dataset. Operationally, a coastal grid points file is created,

Page 14

which consists of all the valid model grid points closest to the coast, with single grid point islands removed where necessary. Total sea level is provided at these coastal points, consisting of the ROMS surge, wave setup as described above, and tidal predictions interpolated from a pre-computed gridded tidal prediction file.

5. VERIFICATION

Seven recent TCs are used as test cases for evaluating the system. These are listed in Table 2. These particular TCs were chosen predominantly due to the availability of a set of sea-level observations from tide gauges. Five of the TCs made landfall in the Queensland region, one in the Northern Territory and one in Western Australia.

Tropical Maximum Location of maximum Amplitude of maximum Dates Cyclone Category observed surge observed surge (m) QLD TC Jan 2011 2 Bowen 0.95 Anthony QLD Jan/Feb TC Yasi 5 Cardwell 5.14 2011 QLD TC Ita Apr 2014 5 Cooktown 1.09

NT TC Lam Feb 2015 4 Groote-Eylandt 0.99

QLD TC Feb 2015 5 Port Alma 1.86 Marcia WA TC Mar 2015 3 Point Murat 1.07 Olwyn QLD TC Mar 2015 2 Cooktown 0.75 Nathan

Table 2 Details of the Tropical Cyclones used for verification. Maximum observed surge refers to sea level after removal of astronomical tides and centering of the residuals (see Section 5.1).

The locations of the Best Tracks for each of these TCs are shown in Fig. 5. These post-analysed Best Tracks provide a basis for the best possible forcing that can be produced using the method described in Section 3, and in principle, should provide the best possible characterisation of the resulting storm surge. For these hindcast case studies, the parameters for determining the wave set-up (see Equation 4.3) are extracted from AUSWAVE-R historical forecasts taking an analysis field and the subsequent hourly forecasts until the next analysis is available. This results in an hourly sequence of wave fields consisting of analyses at 6-hourly intervals (or 12 hours depending on the date), with 5 hours (or 11 hours) of short-range forecasts between each analysis.

Page 15

Fig. 5 Best Tracks for the seven events examined.

5.1 Observations

The storm surge hindcasts are evaluated against tide gauge observations. Tide gauges report relative sea level at a sparse set of point locations; typically in sheltered harbours on structures such as jetties. These locations are not ideal for sampling the extremes of storm surges, but currently provide the most fit-for-purpose objective data set available. The measured coastal sea-level signal contains variability attributable to phenomena across a very wide range of time and space scales, including astronomical tides, storm surges, tsunamis, infra-gravity waves, seiching and seasonal variability.

All available tide gauge observations were obtained during each event and a series of pre-processing steps was applied to each sea-level time series to facilitate direct comparison to the model. Temporal samples were homogenised to regular 10-minute intervals and predicted harmonic tides were subtracted. The resulting residuals were centred around zero, i.e. the bias (calculated over a time period of approximately 20 days surrounding the peak surge) was removed from the observations. These pre- processing steps focus attention on the dynamics of the storm surge and intentionally disregard the other aspects of what will ultimately constitute a skilful forecast guidance. For this study, the resulting tide gauge time series were inspected to select those that were near the TC landfall location and showed a discernible surge (a residual of 0.5m or greater). This provided a total of 21 time series of observed storm surge over the seven TCs examined.

Page 16

5.2 Summary of Performance

A summary of the storm surge verification results for all of the case studies is shown in Table 3 and Table 4. These present the amplitude and timing of the peak observed surge at each tide gauge, along with the results from the Best Track simulations. Mean values (of maximum sea level) shown in the tables are calculated as an average over all tide gauges. The components of the sea level (surge and wave setup) are also provided separately. The question of whether tide gauges observe wave set-up is debatable. In general, wave set-up is a phenomenon occurring on beaches or exposed coastlines where waves are breaking, whereas tide gauges are typically located within sheltered harbours. However, the tide gauges used for this study are mostly quite exposed to the open ocean. Consequently, the tide gauge observations used here are treated as representative of total surge, i.e. including a wave set-up component.

Overall, the mean bias in the maximum surge amplitude is -1 cm. This is negligible and suggests that there is no systematic over- or under-prediction in the system. The mean absolute error of peak surge is 26 cm. This is a moderate error (approximately 30%) when compared with the mean observed surge of 1.25 m. However, it is a significant improvement over existing systems (see section 5.4). For 18 of the 21 sites (86%), the error is less than 0.5m, or 10% of the observed peak (whichever is larger). This is well within the Bureau of Meteorology's target for operational acceptance (at least 80% of sites with errors less than 0.5m or 10%).

Peak timings are presented in Table 4. These need to be interpreted with caution as there is one event (TC Lam) which occurred on a relatively long time scale. In addition, some of the sites had multiple peaks of similar amplitude which can result in large errors in timing even if the amplitude errors are very small. This is discussed in the next section. The mean bias in the timing is -30 minutes (negative means that model is early) and the average absolute value of the error in the timing of the peak is 90 minutes. However, when TC Lam is removed from the average, the absolute difference reduces to 64 minutes, with the mean bias remaining approximately the same (-29 minutes). This early bias may be partially explained by the fact that the model grid points are always slightly offshore compared to the tide gauge locations, so the storm surge will arrive at the location of the model grid point before arriving at the tide gauge.

Note that the observed times were interpolated to 10-minute intervals at 5, 15, 25… minutes past the hour, while the modelled times are output at 10-minute intervals at 0, 10, 20… minutes past the hour, so the modelled and observed times are not exactly coincident. This 5-minute offset is not considered a major issue here, but it should be borne in mind that the minimum achievable difference is 5 minutes.

Page 17

Observed Modelled Surge Setup Diff |Diff| Diff TC Tide gauge location maximum maximum (m) (m) (m) (m) (%) (m) (m) Shute Harbour 0.50 0.84 0.76 0.09 0.34 0.34 67.2 Anthony Bowen 0.95 0.77 0.68 0.09 -0.18 0.18 18.9 Laguna Quays 0.87 1.61 1.53 0.08 0.74 0.74 85.4 Cape Ferguson 1.87 2.00 1.48 0.52 0.13 0.13 7.2 Townsville 2.25 2.04 1.86 0.18 -0.21 0.21 9.2 Cairns 0.96 0.81 0.57 0.24 -0.15 0.15 15.7 Yasi Clump Point 2.74 2.55 2.05 0.49 -0.19 0.19 6.8 Cardwell 5.14 4.60 4.36 0.24 -0.54 0.54 10.4 Mourilyan 1.10 1.29 0.52 0.77 0.19 0.19 17.2 Cape Ferguson 0.62 0.57 0.34 0.23 -0.05 0.05 8.6 Cairns 0.57 0.50 0.36 0.14 -0.07 0.07 11.9 Townsville 0.51 0.42 0.33 0.09 -0.09 0.09 18.0 Ita Cooktown 1.09 1.56 1.30 0.27 0.47 0.47 42.7 Cardwell 0.52 0.61 0.52 0.09 0.09 0.09 17.8 Bowen 0.56 0.34 0.23 0.10 -0.22 0.22 39.6 Groote Eylandt 0.99 0.48 0.46 0.02 -0.51 0.51 51.3 Lam Weipa 0.76 0.30 0.27 0.02 -0.46 0.46 60.5 Rosslyn Bay 0.52 0.98 0.93 0.05 0.46 0.46 89.4 Marcia Port Alma 1.86 1.69 1.66 0.03 -0.17 0.17 9.1 Olwyn Point Murat 1.07 0.98 0.66 0.32 -0.09 0.09 8.1 Nathan Cooktown 0.75 0.95 0.71 0.24 0.20 0.20 26.1

Mean 1.25 1.23 1.03 0.20 -0.01 0.26 29.6

Table 3 Comparison between observed and modelled peak surge amplitude for the 7 TC events studied here. MAEs less than 0.5m are highlighted in green.

Page 18

Observed peak Modelled peak Difference |Difference| TC Tide gauge location time time (minutes) (minutes) Shute Harbour Jan 30 9:55 Jan 30 11:10 75 75 Anthony Bowen Jan 30 11:45 Jan30 12:10 25 25 Laguna Quays Jan 30 9:55 Jan 30 10:40 45 45 Cape Ferguson Feb 2 15:05 Feb 2 15:50 45 45 Townsville Feb 2 16:45 Feb 2 16:20 -25 25 Cairns Feb 2 17:15 Feb 2 17:00 -15 15 Yasi Clump Point Feb 2 14:45 Feb 2 14:10 -35 35 Cardwell Feb 2 15:15 Feb 2 15:10 -5 5 Mourilyan Feb 2 16:45 Feb 2 13:40 -185 185 Cape Ferguson April 12 22:55 April 12 23:20 25 25 Cairns April 12 5:15 April 12 6:00 45 45 Townsville April 12 21:25 April 12 22:20 55 55 Ita Cooktown April 11 15:25 April 12 13:00 -145 145 Cardwell April 12 14:05 April 12 9:50 -255 255 Bowen April 13 4:15 April 13 4:10 -5 5 Groote Eylandt Feb 19 1:35 Feb 18 19:20 -375 375 Lam Weipa Feb 18 23:15 Feb 19 4:20 305 305 Rosslyn Bay Feb 20 1:45 Feb 19 23:50 -115 115 Marcia Port Alma Feb 20 5:25 Feb 20 4:30 -55 55 Olwyn Point Murat March 12 17:15 March 12 17:30 15 15 Nathan Cooktown March 19 17:25 March 19 16:40 -45 45 Mean -30 90

Table 4 Comparison between observed and modelled peak surge timing for the seven TC events studied here.

Page 19

5.3 Case Study results

In this section, the comparisons between modelled and observed surge for each TC event are presented and examined in detail. In the time series plots, the vertical axes are kept constant for all locations for each TC (but vary from TC to TC), in order to be able to compare the relative signal between locations. Furthermore, the temporal range of the figures (the horizontal axis) is kept at 2.5 days for all TCs (except TC Ita and TC Lam which were long-lived events) for consistency.

5.3.1 TC Anthony

Tropical Cyclone Anthony (Auden, 2011) was initially identified as a tropical low in the northwest Coral Sea on the 22nd January 2011 (see Fig. 5). The low moved away from the Queensland coast and formed into a TC on the 23rd of January. This system oscillated between a low intensity TC and a tropical low over the next few days as it circulated in the Coral Sea. As a tropical low, it began to propagate towards the central Queensland coast on January 29th. It re-intensified into a marginal Category 2 system before making landfall near Bowen just before 10pm on January 30th.

Fig. 6 Hourly fixes of the Best Track for TC Anthony (light blue crosses) and location of 3 tide gauges (red diamonds) used for verification

Observations of the storm surge from TC Anthony were available from 3 tide gauges. The locations of these gauges are shown are in Fig. 6. Figure 7 to Figure 9 show comparisons of the observed and modelled sea level. In each figure, the left hand panel shows the location of the tide gauge in relation to the output coastal grid points, and the right hand panel shows the time series of modelled versus observed surge.

Page 20

Fig. 7 Left hand panel shows the location of the Bowen tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Bowen station data (black diamonds) during TC Anthony compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up.

Fig. 8 Same as Fig. 7 but for Shute Harbour

Fig. 9 Same as Fig 7 but for Laguna Quays

It can be seen that the ROMS simulation performs reasonably well at Bowen, with some slight underestimation (18cm) of the peak, but overestimates the peak of the surge at Shute Harbour and Laguna Quays.

Page 21

5.3.2 TC Yasi

Severe TC Yasi developed as a tropical low northwest of Fiji on 29th January 2011 (Australian Bureau of Meteorology, 2011) and started propagating towards the west (see Fig. 5). The system rapidly intensified to a cyclone category and was named Yasi on the 30th January. Yasi maintained its westward propagation and further intensified to a Category 3 in the afternoon of the 31st January and then Category 4 in the evening of the 1st February. During this time, Yasi started to accelerate towards the tropical Queensland coast. Yasi was upgraded to a marginal Category 5 system early on 2nd February and maintained this intensity as it made landfall early on Thursday 3rd February. Yasi is one of the most powerful cyclones to have affected Queensland on record. A disaster situation was declared on 2 February for a number of coastal areas from Cairns to Mackay along the coast, and to Mount Isa in the west. High erosion damage to beaches was reported and there was also damage to local infrastructure in the Mission Beach to Cardwell area (Queensland Department of Environmental and Resource Management, 2011).

Fig. 10 Hourly fixes of the Best Track for TC Yasi (blue crosses) and location of the 6 tide gauges (red diamonds) used for verification

Comparisons of the modelled surge and wave setup with tide gauge observations are shown in Fig. 11 to Fig. 16. It is worth noting that at Mourilyan the wave set-up reaches 0.77 m, which is the highest wave set-up value seen for any of these case studies and somewhat of an outlier. This is likely due to the value of the coastal slope at this location. It can be seen that the total surge (ROMS + wave setup) matches the observed surge very well for this location, and indeed for most locations for this TC. The

Page 22

total surge at Cardwell is of interest as it is the highest observed surge considered in these case studies. The ROMS system underestimates this surge by approximately 10%. A large portion of this is likely to be due to the wave setup – the value of wave setup seen at the peak here is 24cm, which is likely an underestimate as other studies have estimated the wave setup to be closer to 40cm (e.g. Hetzel et al., submitted).

Fig. 11 Left hand panel shows the location of the Cairns tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Cairns station data (black diamonds) during TC Yasi compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up.

Fig. 12 Same as Fig. 11 but for Mourilyan

Fig. 13 Same as Fig. 11 but for Clump Point

Page 23

Fig. 14 Same as Fig. 11 but for Cardwell

Fig. 15 Same Fig. 11 but for Townsville

Fig. 16 Same as Fig. 11 but for Cape Ferguson

Close inspection of the observed time series at Mourilyan shows that the surge consists of two separate peaks, which are very similar in amplitude but occur about 3 hours apart. The peak of the surge from the ROMS system matches these peaks well in amplitude but occurs closest to the first peak, which is marginally lower than the second observed peak, and which is where the timing of the peak is defined. This is reflected in the verification statistics as noted in section 5.2. Similar features can be seen at Townsville and Cape Ferguson.

Page 24

5.3.3 TC Ita

TC Ita began life as a tropical low southwest of the Solomon Islands and was classified as a Category 1 cyclone on the afternoon of April 5th 2014 (Australian Bureau of Meteorology, 2014b). The cyclone moved slowly westwards, and then remained stationary for two days while continuing to intensify, reaching Category 3 on April 8th. It then recommenced its westward motion, until the afternoon of April 10 when it intensified extremely rapidly, reaching Category 5 within 6 hours. At the same time it turned southwest towards the Queensland coast. TC Ita weakened somewhat in the hours leading up to landfall and was rated as a Category 4 at landfall on the evening of Friday the 11th.

Upon landfall, TC Ita continued to track southward. It weakened reasonably quickly and passed west of Cooktown as a Category 2 cyclone. It spent two days propagating southwards over land, but near the coast, weakening to Category 1. TC Ita eventually moved off the Queensland coast on the night of April 13th, transitioning into an extra tropical low and accelerating away from the coast.

Fig. 17 Hourly fixes of the Best Track for TC Ita (green crosses) and location of the 6 tide gauges (red diamonds) used for verification.

Observations of the storm surge from TC Ita were available from 6 tide gauges (see Fig. 17). The following figures show the location of each tide gauge in relation to the model grid, and the time series of modelled versus observed sea level at each gauge.

Page 25

Fig. 18 Left hand panel shows the location of the Cooktown tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Cooktown station data (black diamonds) during TC Ita compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up.

Fig. 19 Same as Fig. 18 but for Cairns

Fig. 20 Same Fig. 18 but for Cardwell

Page 26

Fig. 21 Same as Fig. 18 but for Townsville

Fig. 22 Same as Fig. 18 but for Cape Ferguson

Fig. 23 Same as Fig. 18 but for Bowen

Maximum observed surge for this event was around 1 m at Cooktown. The ROMS model overestimated the surge by over 40 cm there and was somewhat early, but performed better at the other locations. It can be seen that the surge was relatively small elsewhere since the TC at that stage was propagating southwards over land.

Page 27

5.3.4 TC Lam

Severe TC Lam was the first severe cyclone to cross the Northern Territory coast for nearly a decade (Australian Bureau of Meteorology, 2015a). On Sunday 15 February 2015, a tropical low over the northwest Coral Sea crossed Cape York Peninsula and entered the Gulf of Carpentaria. It developed quickly during the next two days as it moved slowly towards the west. The system was named TC Lam early on Tuesday 17 February, whilst located over the northern Gulf of Carpentaria. Lam strengthened into a Category 2 TC that same day and continued to intensify and move slowly westward. Lam was upgraded to Category 3 on Wednesday 18 February, shortly before it passed directly over the Cape Wessel Islands (between 136⁰E and 137⁰E longitude). Due to the slow movement of the cyclone, gale- force winds were experienced at Cape Wessel for a period of approximately 30 hours. Severe TC Lam took a turn towards the southwest at around midnight on Wednesday 18 February. It then tracked parallel to the west coast of the Wessel Islands throughout Thursday 19 February, while intensifying further. Severe TC Lam reached Category 4 intensity that evening and crossed the mainland coast early on Friday 20 February.

Fig. 24 Hourly fixes of the Best Track for TC Lam (pink crosses) and location of the 2 tide gauges (red diamonds) used for verification.

Observations of the storm surge from TC Lam were available from 2 tide gauges. Figure 25 to Figure 26 show the location of each tide gauge in relation to the model grid, and the time series of modelled versus observed sea level at each gauge. Modelled time series are limited in this case as the Best Track vortices were not available beyond 0Z on February 20th, when TC Lam made landfall

Page 28

Fig. 25 Left hand panel shows the location of the Weipa tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Weipa station data (black diamonds) during TC Lam compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up.

Fig. 26 Same as Fig. 25 but for Groote Eylandt

As noted in the description of the event, TC Lam was a broad and slow moving event, and the surge observations reflect this, with peak sea levels of up to 1 m occurring over a period of several days. Note that these locations are quite distant from the TC track and the peak surges occur around 19th February, which is when the TC was propagating parallel to the Wessel Islands. The variability in sea level seen here is due to surge that has propagated away from the regions where it was generated. Despite that fact that these time series do not represent storm surge at landfall, the model/obs comparisons are included here, predominantly because there are so few observations available of surge greater than 0.5m, that any observation at all can provide useful information.

5.3.5 TC Marcia

The tropical low that eventually became severe TC Marcia was first identified in the Coral Sea on Sunday, February 15th 2015 (Australian Bureau of Meteorology, 2015b). Marcia was tracked over the next few days as it drifted eastward with little change in intensity. During the afternoon of Wednesday February 18th 2015, the low pressure system reached TC intensity and was named Marcia, before

Page 29

beginning to move towards the southwest. Tropical cyclone Marcia continued to intensify during February 18th and reached Category 2 intensity by that evening. It continued on a south-westerly track on the 19th and rapidly intensified, increasing by two categories to a Category 4 severe TC in approximately 12 hours. Late on February 19th, Marcia made a sharp turn towards the south and intensified even further, and was estimated to have reached Category 5 intensity at 4am on Friday 20th February.

Severe TC Marcia crossed the coast at Shoalwater Bay as a Category 5 system in the morning of February 20th. It was a relatively compact system compared to other severe TCs such as severe TC Yasi and weakened quickly as it moved over land during the day.

Fig. 27 Hourly fixes of the Best Track for TC Marcia (orange crosses) and location of the 2 tide gauges (red diamonds) used for verification.

Observations of the storm surge from TC Marcia were available from 2 tide gauges. Figure 28 to Figure 29 show the location of each tide gauge in relation to the model grid, and the time series of modelled versus observed sea level at each gauge.

Page 30

Fig. 28 Left hand panel shows the location of the Rosslyn Bay tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Rosslyn Bay station data (black diamonds) during TC Marcia compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up.

Fig. 29 Same as Fig. 28 but for Port Alma

The observed storm surge was relatively small at Rosslyn Bay in this case, reaching just over 0.5m and barely discernible in the observations. At Port Alma, there a clear surge signal in the observations and this is well captured by the model. However, it is worth noting that the tide gauge is located within the estuary some distance away from the coastline – the closest model grid point in this case is just over 5 km away from the tide gauge. This may explain the fact that the modelled surge arrives almost an hour earlier than in the observations.

5.3.6 TC Olwyn

TC Olwyn began as a tropical low in an active monsoon trough approximately 900 kilometres north of Exmouth on 8 March, 2015. It initially moved slowly towards the east, then towards the south on 10 March. It maintained a southerly track while slowly strengthening and was named TC Olwyn on 11th March, when it was 500 kilometres north of Karratha (Australian Bureau of Meteorology, 2015c). Olwyn intensified while moving towards the south-southwest and reached Category 2 in the evening of 11 March. The system accelerated as it approached Exmouth and strengthened to Category 3 intensity

Page 31

on 12th March when it was located about 155 kilometres north northeast of Exmouth. On 13th March, Olwyn tracked in a southerly direction down the Gascoyne coast. Olwyn then weakened to Category 2 intensity during the evening of 13 March as it passed to the east of Denham, crossing the coast shortly afterwards.

Fig. 30 Hourly fixes of the Best Track for TC Olwyn (purple crosses) and location of the tide gauge (red diamond) used for verification.

Observations of the storm surge from TC Olwyn were available from only 1 tide gauge. Fig. 31 shows the location of this tide gauge in relation to the model's coastal grid-points, and the time series of modelled versus observed sea level.

Fig. 31 Left hand panel shows the location of the Point Murat tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Point Murat station data (black diamonds) during TC Olwyn compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up.

Page 32

In this case, the modelled surge replicates the observed surge very well, for both the amplitude and the timing.

5.3.7 TC Nathan

The final case study considered here is TC Nathan. The tropical low that would become TC Nathan was first identified and tracked on the morning of Monday 9 March, 2015 in the northern Coral Sea (Australian Bureau of Meteorology, 2015d). During the next 36 hours the low drifted towards the west- southwest while slowly intensifying, and was named as Category 1 cyclone Nathan on the evening of Tuesday 10 March. The cyclone continued to move west-southwest towards Cape York Peninsula while developing further, reaching Category 2 after another 12 hours on the morning of Wednesday 11 March. Following this, Nathan remained stationary off the Cape York Peninsula coast for roughly two days at Category 2 strength.

Nathan was then steered to the east away from the coast for the next two days and became slow moving then drifted very slowly south for two more days, all this time fluctuating between Category 1 and Category 2 in intensity. Finally Nathan was again steered westwards towards the Cape York Peninsula coast and intensified, reaching Category 4 strength in the last hours before it made landfall at about 4am on Friday 20 March north of Cooktown.

Fig. 32 Hourly fixes of the Best Track for TC Nathan (red crosses) and location of the tide gauge used for verification (red diamond).

Page 33

Only one tide gauge (Cooktown) is considered here as the tide gauges within the Gulf of Carpentaria did not show any discernible surge.

Fig. 33 Left hand panel shows the location of the Cooktown tide gauge (green pin) in relation to the local coastline and the model's coastal grid points (white circles). The closest grid point is indicated by the red circle. Right hand panel shows de-tided Cooktown station data (black diamonds) during TC Nathan compared with the model hindcast at the closest grid point. Blue line is surge only, green line is wave set-up and red line is surge + wave set-up.

The total storm surge is somewhat overestimated here, with modelled surge of 95 cm compared to observed surge of 75 cm. The actual ROMS surge component matches the observed surge very well – it is the wave setup component that contributes to the overestimation. This is also the case for the Cooktown during TC Ita – the surge matches much better if the wave setup component is not included. This suggests that perhaps Cooktown is a tide gauge for which wave setup is not observed.

5.4 Comparison with existing systems

Within the Bureau of Meteorology’s Tropical Cyclone Warning Centres in Queensland, the Northern Territory, and WA, parametric systems are used to provide operational forecasts of storm surge. These have been briefly described in Section 2.In this section, the storm surge hindcasts at tide gauge locations from these systems are compared, where possible, with those described in the previous section from the ROMS system. The operational systems are provided with the same Best Track forcing information as the ROMS system. Only the 5 QLD events using SEAtide are included here. SEAtide forecasts are not currently available at tide gauge locations for TC Lam due to the configuration of SEAtide in the Northern Territory, and the WA system provides a 'worst case' forecast, which is not comparable with the deterministic forecasts assessed here. There are a total of 18 valid observations of surge for which hindcasts are available from both the ROMS system and SEAtide.

A summary of the errors in peak surge amplitude for each system is presented in Table 5. For these 18 observed surges, the Mean Absolute Error (MAE) for ROMS (25 cm) is half that of SEAtide (51 cm). The bias for ROMS (4 cm) is also significantly smaller than that of SEAtide (-41 cm). It should be noted

Page 34

that SEAtide values refer to surge only as wave set-up is not provided at tide gauge locations. If it is assumed that wave set-up is around 20 cm (see Table 3), and this is added to the SEAtide estimates, this would result in a somewhat smaller negative bias (approximately -20 cm) for SEAtide and a smaller MAE (39 cm). The lowest absolute error for each site is shaded in green and it can be seen that ROMS has the lowest MAE for 13 out of 18 sites (72%).

It should also be noted that there are limitations to the underlying technique used for SEAtide which means that it is not really able to provide a surge forecast for some locations when the TC track is complex. This is a limitation of any parametric system. SEAtide assumes that once the TC has made landfall, it continues its track without changing direction or intensity. This means that it is not able to model a complex track such as that of TC Ita, which propagated southwards along the coast after making landfall. This can be seen in the very low amplitudes of the SEAtide surge for TC Yasi at Cairns and Cape Ferguson and most locations for TC Ita. If the cases for which the amplitude of the surge is 0.0 m or 0.1 m are removed, then the performance of SEAtide improves somewhat. The bias reduced to - 32cm (-12cm if wave setup is added) and the MAE reduces to 47 cm (39 cm if wave setup is added).

A summary of the errors in peak surge timing is shown in Table 6 for the 5 Queensland TCs. The statistics here for ROMS (bias = -32 minutes and MAE = 67 minutes) are similar to those overall as presented in section 5.2, except that the absolute value of the difference is considerably smaller, since TC Lam is not included here. SEAtide can be seen to perform very poorly in terms of timing. This is predominantly due to the inclusion of locations for which SEAtide is not designed to predict surge, as discussed above. If the cases for which the amplitude of the surge is 0.0 m or 0.1 m are removed as before, the performance of SEAtide is more comparable to ROMS (mean bias of 26 minutes and absolute difference of 84 minutes).

In summary, these results demonstrate that not only is the dynamic ROMS system able to provide forecasts of surge for a larger set of locations than existing systems, but in addition, the accuracy of the ROMS system is a significant improvement over the existing parametric systems, given the same Best Track forcing.

Page 35

ROMS SEATIDE

Best Obs max Best track Abs Percent Abs Percent Difference track Difference (centred) max (Difference) difference (Difference) difference max Shute Harbour 0.50 0.84 0.34 0.34 68 0.4 -0.10 0.10 20 TC Bowen 0.95 0.77 -0.18 0.18 19 0.7 -0.25 0.25 26 Anthony Laguna Quays 0.87 1.61 0.74 0.74 85 0.6 -0.27 0.27 31 Cardwell 5.14 4.6 -0.54 0.54 11 5.3 0.16 0.16 3 Clump Point 2.74 2.55 -0.19 0.19 7 2.4 -0.34 0.34 12 Townsville 2.25 2.04 -0.21 0.21 9 1.0 -1.25 1.25 56 TC Yasi Cape Ferguson 1.87 2 0.13 0.13 7 0.5 -1.37 1.37 73 Mourilyan 1.10 1.29 0.19 0.19 17 0.4 -0.70 0.70 64 Cairns 0.96 0.81 -0.15 0.15 16 0.0 -0.96 0.96 100 Cape Ferguson 0.62 0.57 -0.05 0.05 9 0.0 -0.62 0.62 100 Cairns 0.57 0.5 -0.07 0.07 12 0.1 -0.47 0.47 82 Townsville 0.51 0.42 -0.09 0.09 18 0.0 -0.51 0.51 100 TC Ita Cooktown 1.09 1.56 0.47 0.47 43 0.9 -0.19 0.19 18 Cardwell 0.52 0.61 0.09 0.09 18 0.1 -0.42 0.42 81 Bowen 0.56 0.34 -0.22 0.22 40 0.0 -0.56 0.56 100 TC Roslyn Bay 0.52 0.98 0.46 0.46 88 0.8 0.28 0.28 54 Marcia Port Alma 1.86 1.69 -0.17 0.17 9 1.6 -0.26 0.26 14 TC Cooktown 0.75 0.95 0.20 0.20 27 1.2 0.45 0.45 60 Nathan Average 1.30 1.34 0.04 0.25 27.86 0.89 -0.41 0.51 55.22

Table 5 Comparison between ROMS and SEAtide errors in peak surge amplitude. All values except percentages in (m). Green shading indicates the lowest absolute error for each site.

Page 36

ROMS SEATIDE

Modelled Tide gauge Observed Observed Modelled Modelled Difference Difference Modelled Difference |Difference| TC peak location peak day peak time peak day peak time (minutes) (minutes) peak day (minutes) (minutes) time Shute Jan-30 9:55 Jan-30 11:10 75 75 Harbour Jan-30 11:09 74 74 Anthony Bowen Jan-30 11:45 Jan-30 12:10 25 25 Jan-30 13:09 84 84 Laguna Jan-30 9:55 Jan-30 10:40 45 45 Quays Jan-30 11:29 94 94 Cape Feb-02 15:05 Feb-02 15:50 45 45 Ferguson Feb-02 15:01 -4 4 Townsville Feb-02 16:45 Feb-02 16:20 -25 25 Feb-02 15:31 -74 74 Cairns Feb-02 17:15 Feb-02 17:00 -15 15 Feb-02 21:51 216 216 Yasi Clump Feb-02 14:45 Feb-02 14:10 -35 35 Point Feb-02 13:01 -104 104 Cardwell Feb-02 15:15 Feb-02 15:10 -5 5 Feb-02 16:11 56 56 Mourilyan Feb-02 16:45 Feb-02 13:40 -185 185 Feb-02 21:01 256 256 Cape Apr-12 22:55 Apr-12 23:20 25 25 Ferguson Apr-10 12:00 -3535 3535 Cairns Apr-12 5:15 Apr-12 6:00 45 45 Apr-11 23:59 316 316 Ita Townsville Apr-12 21:25 Apr-12 22:20 55 55 Apr-10 12:00 -3445 3445 Cooktown Apr-11 15:25 Apr-12 13:00 -145 145 Apr-11 13:49 -96 96 Cardwell Apr-12 14:05 Apr-12 9:50 -255 255 Apr-11 23:59 -846 846 Bowen Apr-13 4:15 Apr-13 4:10 -5 5 Apr-10 12:00 -3855 3855 Rosslyn Feb-20 1:45 Feb-19 23:50 -115 115 Marcia Bay Feb-20 0:39 -66 66 Port Alma Feb-20 5:25 Feb-20 4:30 -55 55 Feb-20 5:19 -6 6 Nathan Cooktown Mar-19 17:25 Mar-19 16:40 -45 45 Mar-19 18:59 94 94 Mean -32 67 -602 735

Table 6 Comparison between ROMS and SEAtide errors in peak surge timing.

Page 37

6. FURTHER WORK

This report has documented the initial setup and configuration of the Tropical Storm Surge system. Its performance in terms of hindcast accuracy has been shown to be a significant improvement over the SEAtide storm surge system. This report has focussed on verifying the hydrodynamic model (ROMS) forced by parametrised TC vortices derived from Best Track information. The operational system consists of a set of ensemble forecasts forced by a set of ensemble TC tracks derived using the ‘DeMaria’ method (DeMaria et al., 2009). In order to fully verify the forecast system, there is a need to consider the probabilistic information obtained from the ensemble storm surge forecast system. This will be the subject of a further report. Similarly, although significant for the full forecasting system, the harmonic tidal component of sea level and spatial reference levels have not been addressed in this document.

Verifications have been undertaken for seven TC test cases occurring in recent years, but this included only one extreme event, where observed surge was greater than 2 m (TC Yasi). For this case the surge was slightly underestimated (10%). This suggests that in order to provide confidence in forecasts of storm surge using this system, further verifications for extreme events are needed.

Verification would be greatly facilitated through establishing an improved database of tide gauge observations and metadata. The Bureau operates only a small subset of the existing tide gauges and thus treatment of ‘3rd party’ instruments is important. Associating observed sea levels with official tide predictions and site metadata is also required. Such homogenous data handling has been lacking to date and continues to present challenges. Improving the Bureau's sea level verification data foundations would offer immediate benefits not only to TC case studies, but to a range of coastal and ocean forecasting systems.

Representation of the bottom momentum loss is a modelling detail worth further attention. Spatially variable bottom friction parametrisation is likely to be more realistic in regions such as the Great Barrier Reef, where individual reefs are not resolved by the grid. Some initial investigation into this aspect was undertaken for the Queensland domain (see section 4.1) and could be leveraged for further work.

Alternative parametrisations of the wind stress could also be investigated. In this first implementation, a capped version of the Large and Pond (1981) formula is used, as described in section 3. Air-sea interaction and momentum fluxes within extreme environments such as TCs is an active area of research and there are numerous alternative methods for deriving wind stress that could be evaluated.

Page 38

Initially this project explored options to merge the TC vortex with a background NWP field (i.e. ACCESS-R) to provide a more realistic of set of forcing fields for the ROMS model. This approach was ultimately abandoned due to challenges in developing techniques to remove existing vortices for the real-time application. However, the lack of a background NWP field may become an issue in an ensemble system, particularly for cases where the tracks diverge substantially in space as the TC makes landfall. The distribution of sea level at any particular forecast location is made up of sea level from each of the ensemble tracks. For any tracks that are distant from the forecast location, the surface forcing fields will be 'null' at that location, and the probability distribution of sea-levels there will not be an accurate reflection of the true probability distribution. A further benefit of merging the vortex with a background NWP field is that it will be more consistent with the National Storm Surge System and will be a step towards an integrated coastal sea level forecast system.

A key limitation of the existing system is the way in which wave set-up is calculated. For the initial operational implementation, wave setup is based on offshore estimates of Significant Wave Height and Peak Period obtained from deterministic AUSWAVE-R forecasts and calculated using a parameterisation developed by CSIRO (O’Grady et al., 2015), which also depends on bathymetric slope. There are two aspects by which this could be improved. Firstly, and specifically related to the ensemble system, by developing offshore wave fields that are related to each ensemble member, rather than using the same wave field (i.e. AUSWAVE-R forced by ACCESS-R) for all ensemble members, and secondly though improving the algorithm used for the calculation of wave setup from the offshore wave information. See Greenslade (2016) for further discussion on these options.

Allen et al (2018), amongst other authors, have shown that non-linear interactions between tide and surge can, on occasion, have a significant impact on coastal sea level. These interactions are neglected in the present system, by linearly superimposing the astronomical tide and surge components. This issue should be explored in future, although it is expected that there will be an increase in the computational resources required, which will need to be balanced against any gain in skill.

The definition of the spatial grid has a significant impact on computational cost, which is a major constraint for operational systems, particular those underpinning warning services. A more generalised spatial solution (e.g. a relocatable domain, unstructured grid etc.) could reduce the computational cost, and/or provide coverage of further locations and extension of verification opportunities.

There is considerable scope for improved systems integration. This report has not addressed issues related to solutions-architecture, however, future development should be well informed of ongoing changes in the operational environment, including developments in related hydrodynamic systems, such as OceanMAPS, AUSWAVE or tropical NWP prediction systems.

Page 39

In the longer term, inundation forecasts may be possible. The existing storm surge system produces forecasts of coastal sea level, with the implicit assumption that a higher sea level means a more severe impact. Emergency managers are familiar with these types of forecasts and have existing procedures to include this information in their decision-making processes. However, a potential enhancement to the forecast system could be through the development of inundation forecasts. This would provide much more detailed information on the expected impacts for any particular TC event and could refine the areas where evacuation is (or isn’t) required. Coastal inundation has already been addressed within the Bureau of Meteorology to a limited extent for the Joint Australian Tsunami Warning Centre (Allen and Greenslade, 2016). One issue to address is whether coastal inundation should be considered via an holistic approach, encompassing inundation from all potential sources, or if each existing forecast system should be further developed individually, using techniques that may be specific to that application.

A related issue is the interaction with hydrological phenomena such as river flow. There are two-way interactions that would need to be taken account of, with coastal sea level from the surge system providing boundary conditions for river levels, and conversely, a variable river flow could potentially have an impact on the coastal sea level, particularly in estuaries. A further related issue is the effect of heavy rainfall, which has been shown to have an impact on coastal sea level in TC conditions (e.g. Wong and Toumi, 2016). Initial efforts to explore these aspects have been undertaken by De Kleermaeker et al (2018) along with an investigation into the possibility of impact forecasting.

The Bureau of Meteorology is currently putting a considerable amount of effort into the development of coastal modelling. In addition to the Tropical Storm Surge system described in this report, the National Storm Surge system has been implemented operationally (Allen et al., 2018) and a baroclinic high resolution coastal modelling system, also based on ROMS, is being developed for the Great Barrier Reef region under the eReefs project. The tsunami model that underpins warnings from the Joint Australian Tsunami Warning Centre is another relevant modelling system. While most of these systems are in their infancy, and each system is intended for a different purpose, there is potential in the longer term to merge them into a single coastal dynamics forecast system which could service a number of different purposes.

7. ACKNOWLEDGEMENTS

• Andrew Donaldson, Jason Brownlee and Stephen Taylor are thanked for their assistance with TC Module and providing the Best Track data.

Page 40

• Tony Wedd (QLDRO) is thanked for providing the comparison data from SEAtide used in Section 5.4. Brad Santos, Andrew Burton (WARO) and Ian Shepherd (NTRO) are also thanked for useful discussions relating to existing forecast systems. • Stewart Allen is acknowledged for creating the first version of the ribbon grid. • Mikhail Entel, Stewart Allen and Tony Hirst are thanked for constructive reviews of early drafts of the manuscript.

8. REFERENCES

Allen, S.C.R. and Greenslade, D.J.M. 2016: A Pilot Tsunami Inundation Forecast System for Australia, Pure and Applied Geophysics, 173, 3955–3971. Allen, S., D. Greenslade, F. Colberg, J. Freeman, E Schulz, 2018: A first-generation national storm surge forecast system, Bureau of Meteorology Research Report No. 28., Bur. Met. Australia. Auden, T. 2011: Tropical Cyclone Anthony, Australian Bureau of Meteorology's Brisbane Tropical Cyclone Warning Centre, accessed 16th January 2017, Australian Bureau of Meteorology, 2016. TC Verification web page (internal only), accessed 1st July 2016. Australian Bureau of Meteorology. 2011: Severe Tropical Cyclone Yasi, accessed 16th January 2017, Australian Bureau of Meteorology. 2014a: Severe Tropical Cyclone Gillian, accessed 17th January 2017, < http://www.bom.gov.au/announcements/sevwx/wa/watc20140306.shtml> Australian Bureau of Meteorology. 2014b: Severe Tropical Cyclone Ita, accessed 16th January 2017, Australian Bureau of Meteorology. 2015a: Severe Tropical Cyclone Lam post-event report, accessed 17th January 2017, Australian Bureau of Meteorology. 2015b: Severe Tropical Cyclone Marcia, accessed 16th January 2017, < http://www.bom.gov.au/cyclone/history/marcia.shtml> Australian Bureau of Meteorology. 2015c: Severe Tropical Cyclone Olwyn, accessed 17th January 2017, < http://www.bom.gov.au/announcements/sevwx/wa/watc20150308.shtml> Australian Bureau of Meteorology. 2015d: Severe Tropical Cyclone Nathan, accessed 17th January 2017, < http://www.bom.gov.au/announcements/sevwx/qld/qldtc20150310.shtml> Bastidas, L. A., Knighton, J., and Kline, S. W.: Parameter sensitivity and uncertainty analysis for a storm surge and wave model, Nat. Hazards Earth Syst. Sci., 16, 2195-2210, https://doi.org/10.5194/nhess-16-2195-2016, 2016.

Page 41

Bureau National Operations Centre. 2016: Operational Upgrade to the AUSWAVE Regional Wave Model (AUSWAVE-R), BNOC Operations Bulletin No. 108, accessed 19th January 2017, Burston, J.M., Symonds, A. M. and Tomlinson, R. 2013a: Real-time storm tide forecasting for emergency management in Queensland, Australia. In Coasts and Ports 2013: Proceedings of the 21st Australasian Coastal and Ocean Engineering Conference and the 14th Australasian Port and Harbour Conference. Barton, A.C.T., Engineers Australia, 163-168. Burston, J.M, Nose, T. and Tomlinson, R. 2013b: Real-time numerical simulation of storm surge inundation using high-performance computing for disaster management, Queensland 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013. Chapman, D. C. 1985: Numerical treatment of cross-shelf open boundaries in a barotropic coastal ocean model, J. Phys. Oceanogr., 15, 1060—1075 Colberg, F., McInnes, K. and Chattopathy, M. 2013: Storm surge modelling review. CSIRO internal Report, 34 pp [available from the authors] Courtney, J. and J. Knaff, 2009, Adapting the Knaff and Zehr wind-pressure relationship for operational use in Tropical Cyclone Warning Centres, Aust. Met. Oc. Journal, 58, pp 167-179 Davidson, N.E., Xiao, Y., Ma, Y., Weber, H.C., Sun, X., Rikus, L. J., Kepert, J.D., Steinle, P., Dietachmayer, G.S., Lok, C.C.F., Fraser, J.R., Fernon, J. and Shaik, H. 2014: ACCESS-TC: Vortex Specification, 4DVAR Initialization, Verification, and Structure Diagnostics, Mon. Wea. Rev., 142, 1265–1289. Davidson, N.E., Entel, M., Hanstrum, B., Smith, N. and Warren, G. 2005: Enhancements to the Bureau of Meteorology’s ocean surge forecasting system using operational TC LAPS, BMRC Research Report No. 114, Bur. Met. Australia. Davidson, N. E., and H. C. Weber, 2000: The BMRC high-resolution tropical cyclone prediction system: TC-LAPS. Mon. Wea. Rev., 128, 1245–1265, doi:https://doi.org/10.1175/1520- 0493(2000)128<1245:TBHRTC>2.0.CO;2. De Kleermaeker, S., Taylor, A., Greenslade, D., Ormondt, M., and J. Perkins, (in prep). Proof of Concept for real-time inundation and flood impact modelling, NSW Coastal Conference, Bega, NSW, November 2018. DeMaria, M., Knaff, J.A., Knabb, R., Lauer, C., Sampson, C.R. and DeMaria, R.T. 2009: A New Method for Estimating Tropical Cyclone Wind Speed Probabilities, Weather and Forecasting, 24, 1573-1591. Donaldson, A. and Taylor, S. 2018. Tropical Cyclone Module User Guide v16.2, Bureau of Meteorology internal document [available from the authors] Durrant, T.H., Greenslade, D.J.M., 2011. Evaluation and Implementation of AUSWAVE, CAWCR Technical Report No. 41, Bur. of Meteorology, Melbourne, Australia.

Page 42

Flather, R.A., 1976, A tidal model of the northwest European continental shelf, Memoires de la Societe Royale de Sciences de Liege, 6. Glahn, B., Taylor, A., Kurkowski, N. and Shaffer, W.A. 2009: The role of the SLOSH model in National Weather Service storm surge forecasting, Natl. Weather Dig., 33, 4–14. Greenslade, D. J. M. 2016: Wave setup for the tropical storm surge system: Options paper, Unpublished report, 5 pp. (available from the author). Harper B.A. (ed.) 2001: Queensland climate change and community vulnerability to tropical cyclones - ocean hazards assessment - stage 1, Report prep by Systems Engineering Australia Pty Ltd in association with James Cook University Marine Modelling Unit, Queensland Government, March, 375pp, accessed January 17th 2017, Hasegawa, H., N. Kohno, M. Higaki, and M. Itoh, 2017: Upgrade of JMA’s Storm Surge Prediction for the WMO Storm Surge Watch Scheme (SSWS). Technical review, RSMC Tokyo-Typhoon Center, 19, 1-9. Hetzel, Y., Janekovic, I., Pattiaratchi, C.B. and Haigh, I.D. The role of wave setup on extreme water levels around Australia, Submitted to Progress in Oceanography. Holland, G.J., Belanger, J.I. and Fritz, A. 2010: A revised model for radial profiles of hurricane winds, Mon. Wea. Rev. 138, 4393 – 4401. Hubbert, G.D. and McInnes, K.L. 1999. A storm surge inundation model for coastal planning and impact studies. J. Coastal Research, 15, 168-85. Hughes, L.A. 1952: On the low-level structure of tropical storms, J. Meteorology, 9, 422-428. Jelesnianski, C. P., Chen, J. and Shaffer, W. A. 1992: SLOSH: Sea, Lake, and Overland Surges from Hurricanes. NOAA Tech. Rep. NWS 48, 65pp. Kennedy, A.B., Westerink, J.J., Smith, J.M., Hope, M.E., Hartman, M., Taflanidis, A.A., Tanaka, S., Westerink, H., Fai, K., Smith, T., Hamanna, M., Minamidee, M., Otae, A. and Dawson. C. 2012: Tropical cyclone inundation potential on the Hawaiian Islands of Oahu and Kauai, Ocean Modelling, 52/53, 54–68. Kepert, J. D. 2013: How does the boundary layer contribute to eyewall replacement cycles in axisymmetric tropical cyclones? J. Atmos. Sci., 70, 2808–2830, doi:10.1175/JAS-D-13-046.1. Kepert. J. D. 2014: An Improved Method for Generating Ensembles of Tropical Cyclone Tracks, AMS 31st Conference on Hurricanes and Tropical Meteorology, 17 – 22 April 2014, San Juan, Puerto Rico. Knaff, J.A. and R.M. Zehr. 2007. Re-examination of the tropical cyclone wind-pressure relationships, Wea. Forecasting, 22, pp 71-88. Kyouda, M. and M. Higaki, 2015: Upgrade of JMA's Typhoon Ensembel Prediction System. Available online at: http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/techrev/text17-1.pdf

Page 43

Large, W. G. and Pond, S. 1981: Open ocean momentum flux measurements in moderate to strong winds, J. Phys. Oceanogr., 11, 324–326. McInnes, K., Walsh, K., Hoeke, R.K., O’Grady, J.G. and Colberg F. 2014: Quantifying storm tide risk in Fiji due to climate variability and change, Glob Planet Change, 116, 115–129. O’Grady J.G., McInnes, K.L. and Hoeke, R.K. 2015: Real-time estimation of wave setup, CSIRO Internal Report. 32pp Peng, S and Y, Li., 2015, A parabolic model of drag coefficient for storm surge simulation in the South China Sea, Scientific Reports volume 5, Article number: 15496 Phadke, A., Martino, C.D., Cheung, K.F., and S.H. Houston, 2003, Modeling of tropical cyclone winds and waves for emergency management, Oc. Eng., 30, 553–578 Powell, M. D., Vickery, P. J. and Reinhold, T. A. 2003: Reduced drag coefficients for high wind speeds in tropical cyclones, Nature, 422, 279–283. Puri, K., Dietachmayer, G., Steinle, P., Dix, M., Rikus, L., Logan, L., Naughton, M., Tingwell, C., Xiao, Y., Barras, V., Bermous, I., Bowen, R., Deschamps, L., Franklin, C., Fraser, J., Glowacki, T., Harris, B., Lee, J., Le, T., Roff, G., Sulaiman, A., Sims, H., Sun, X., Sun, Z., Zhu, H., Chattopadhyay, M., and Engel, C. 2013: Operational implementation of the ACCESS Numerical Weather Prediction system, Australian Meteorological and Oceanographic Journal, 63, 265-284 Queensland Department of Environmental and Resource Management. 2011: Tropical Cyclone Yasi 2011, accessed 16th January 2017, < https://publications.qld.gov.au/dataset/19c20822-f29e-494c- 880a-113ccd13a04b/resource/3bf0ac2c-565a-4400-8d5b-6a4c15236c82/download/tc-yasi.pdf> Sanderson, B. 1997: A barotropic ocean model for calculating storm surges, BMRC Research Report No. 62, 32 pp. SEA. 2002: Parametric tropical cyclone wave model for Hervey Bay and South East Queensland. Queensland Climate Change and Community Vulnerability to Tropical Cyclones: Ocean Hazards Assessment - Stage 2. Prep. by Systems Engineering Australia Pty Ltd for JCU MMU and the Bureau of Meteorology. [available from the authors] SEA. 2005: Darwin TCWC Northern Region Storm Tide Prediction System - System Development Technical Report. Prep by Systems Engineering Australia Pty Ltd for the Bureau of Meteorology, Darwin. SEA Report J0308-PR001C. [available from the authors] SEA. 2016: SEAtide V3.2 User Guide (Qld-Gulf), SEA Report No. J1302-PR002E. [available from the authors] Shchepetkin, A. F. and McWilliams, J.C. 2005: The regional oceanic modeling system (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model, Ocean Modelling., 9, 347–404. Taylor, A.; Brassington, 2017, G.B. Sea Level Forecasts Aggregated from Established Operational Systems. J. Mar. Sci. Eng., 5, 33.

Page 44

Taylor, A.A., and Glahn, B. 2008: Probabilistic guidance for hurricane storm surge, 19th Conference on Probability and Statistics in the Atmospheric Sciences, 21-24 January 2008, New Orleans, Louisiana, USA, American Meteorological Society. Warren, I. R. and Bach, H. K. 1992: Mike21: a modelling system for estuaries, coastal waters and seas, Environmental Software, 7, 229-240 Whiteway, T. 2009: Australian Bathymetry and Topography Grid, June 2009. Scale 1:5000000. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/53D99B6581B9A Wong, B. and Toumi, R. 2016: Effect of extreme ocean precipitation on sea surface elevation and storm surges, Q. J. R. Meteorol. Soc., 142, 2541-2550.

Page 45