Innovative Wave Transformation Algorithm for Improved Short-term Wave Forecasting

Sean Garber1, Matthew Zed2, Jan Flynn2, David Taylor1, Jarrod Dent1

1Baird Australia, Sydney Australia.

2Woodside Energy Limited, Perth Australia.

This paper presents recent developments and validation of a wave transformation algorithm capable of accurately and efficiently computing shelf scale spectral wave propagation of swell conditions. The computational efficiency of the wave transformation approach makes it applicable for both the development of high-resolution long term hindcasts over large spatial scales and use in operational nearshore wave forecasting.

When coupled with real-time measurements of waves, a transformation algorithm is able to achieve improved accuracy and resolution of short-range wave forecasts compared with global and regional numerical wave forecasts.

An implementation of the wave transformation algorithm that derived relationships between wave energy at a nearshore location and spectral wave buoy measurements located over 300km away will be presented.  The implementation includes the following unique features compared to other wave transfer applications:

  • Use of a spectral transfer method based on the propagation of hypothetical narrow-banded spectral distributions, centred on each frequency-direction bin of the spectral space.
  • Consideration of the influence of hydrodynamics, in terms of both water level and tidal currents, on wave energy transformation.
  • The inclusion of frequency dependent propagation time in the transformation algorithm.

With directional wave spectra as input, the algorithm then computes the nearshore directional wave spectra and associated parameters. Comparisons between measured and forecast/hindcast wave conditions derived from spectral transfer and third-generation spectral wave models are presented to demonstrate the value of the transformation algorithm, particularly for short term swell forecasts.

Design Tropical Cyclone Wind and Waves for North Western Australia

Jeff Kepert, Stefan Zieger, Saima Aijaz, Diana Greenslade, Aaron Wassing

Experience has shown that the observational record of tropical cyclones is too short to directly estimate long return-period risk for offshore or coastal areas. Accordingly, such risk is usually estimated through the analysis of synthetic datasets.
A new synthetic Tropical Cyclone dataset representing 100,000 years for the Australian region has been developed. The key parameters are cyclone position, movement, maximum wind speed and radii of maximum winds and gale-force winds. The tracks were trained on Best Track data since 1979, but the intensity and structure parameters on post-2003 Best Track data due to inhomogeneities in the historical record earlier than this.
Gradient-level wind fields were based on the parametric wind profile of Willoughby et al (2006). These winds are adjusted for the effects of friction using the dynamical boundary-layer model of Kepert and Wang (2001). A statistical adjustment of the winds is also available, which was trained on output of the dynamical boundary-layer model.
These winds were merged into environmental fields from the ERA-I reanalysis in order to drive the wave simulations. It was desired to merge storms into synoptic situations that were consistent with the presence and track of a tropical cyclone. Accordingly, analogues to the synthetic storms were identified in the observed dataset, then the dates of the analogue were used to determine the merging fields. The reanalysis fields had the existing vortex removed to facilitate the merging.
Wave simulations were undertaken with WAVEWATCHIII® on a 5 km grid with two dedicated 1 km grids around shallow reef areas. Boundary conditions were derived to align in time with the merged fields.
The wind and wave modelling procedures were validated for about 15 historical Tropical Cyclones that occurred in the north-west Australian region.
The dynamical boundary-layer and wave models are computationally expensive, so were used only for storms that were estimated to have high impact at locations of interest by simpler models.
This presentation will give a broad overview of the methods and results and show some verifications. Challenges will also be discussed.

Using predictive tools to locate containers from the YM Efficiency

Giovanna Lorenzin1

1Australian Maritime Safety Authority, ,

AMSA commonly uses a variety of predictive modelling and mapping tools to support emergency response operations during a maritime event. These are essential to ensure resources are effectively utilised and specifically target areas that could be impacted during the event. Most often, these are used to model oil spill trajectories, drifting vessels, or persons lost at sea. However, it was recently also used to assist in locating missing containers from the container ship YM Efficiency.
On 1 June 2018, the vessel encountered bad weather approximately 30 kilometres southeast of Newcastle, and consequently lost 81 containers overboard, with an additional 30 moved or damaged on board. AMSA undertook coordination of the underwater search for the missing containers, and to address the complex issue, the question it asked was: where are they and where do we start looking?
AMSA used object drift modelling to help identify the approximate trajectories of where the containers would have floated to, if still buoyant. This modelling was then used as the basis with which to determine the search areas for the underwater side-scan and Remote Operated Underwater Vehicle (ROUV) surveys. Over the course of several months, the surveys undertaken at the predicted search areas successfully located and confirmed several containers. The locations of where some were found washed ashore also agreed with the initial predictions, making all these results reinforce the reliability and accuracy of the initial modelling.
This presentation will go through how these predictive tools effectively assisted in guiding the search for the lost containers from the YM Efficiency.

Great Barrier Reef shipping – Gaining insight from ocean modelling, observations and cloud-based data analytics

Dr Daniel Machado1, Dr Soma Maroju2, Dr Igor Prislin3, Dr Ian Teakle4

1BMT, Melbourne, Australia, 2BMT, Houston, USA, 3BMT, Escondido, USA, 4BMT, Brisbane, Australia

The Great Barrier Reef (GBR) is Australia’s most iconic Marine Park, the world’s most extensive coral reef ecosystem, of outstanding biodiversity, conservation and scientific value, recognised by UNESCO World Heritage listing. The GBR underpins economic activities, largely around tourism, with estimated annual contribution of more than $6billion to the Australian economy and supporting over 60,000 jobs. Inland, export resource industries (coal, gas and minerals) have developed substantially and are predicted to continue doing so, with increasing port facilities and shipping demands. Threats to the GBR comprise global and regional issues like climate change and coastal development, port expansion with associated dredging of shipping lanes and berth pockets for more and larger vessels. Individual vessels calling port in the GBR are predicted to enlarge from 4,000 in 2012 to 10,000 by 2032. Traffic intensifies not only from commodity exports but also cruise-ships, recreational boats, yachts and navy vessels; in hand with traffic go underlaying risks for shipping incidents. Operational oceanography has a key role for managing these risks by informing incident prevention, response preparedness and delivery. To support this, we developed a modelling framework for three-dimensional hydrodynamic and particle tracking modelling in the GBR, using the flexible mesh TUFLOWFV model with meteorological and global ocean circulation model forcing, including baroclinic and ocean turbulence coupling. We have integrated this modelling framework and publicly available in-situ metocean observation data-streams into the cloud-based analytic platform BMT Deep, providing an interactive marine data portal for analysis, from which operational insight can be gained.

Bio to come

Operational products supporting the future management of the Great Barrier Reef

Dr Richard Brinkman1, Dr Claire Spillman2, Dr Roger Beeden3, Dr Jessica Benthuysen1, Mr Craig Steinberg1, Dr Neal Cantin1, Dr Grant Smith2, Dr William Skirving4

1Australian Institute Of Marine Science, , , 2Bureau of Meteorology, , , 3Great Barrier Reef Marine Park Authority, , , 4NOAA, ,

Mass coral bleaching occurred on the Great Barrier Reef (GBR) in 2016 and 2017 as part of a

continuous global bleaching event that started in late 2014 and impacted coral reefs worldwide. Mass bleaching occurs during extended periods of elevated ocean temperature. It has the potential to result in significant and widespread loss of coral, and compromise the ecological, cultural, social and economic benefits and services provided by healthy coral reefs. The length and severity of the 2016-2017 event resulted in significant coral mortality over a large proportion of the GBR.


Monitoring of sea surface temperature (SST) via remote sensing has historically provided a synoptic view of anomalous ocean heating and underpinned operational products for monitoring and predicting bleaching. Advances in the resolution and accuracy of dynamical seasonal forecasts from coupled oceanographic and atmospheric models, increased in situ observations and the application of highly resolved regional & local ocean models are improving our ability to forecast anomalous environmental conditions and move beyond surface-focused products to integrate vertically resolved subsurface observations and model outputs.

Reef managers have a range of management responses that can be implemented at different space and time scales to help manage local impacts and improve resilience to global pressures. The selection and application of tactical and strategic response actions requires access to validated long-term, seasonal and short-term forecasts of environmental condition. This presentation will outline the current state and future direction of operational products to predict coral bleaching, and support management of the GBR and reefs worldwide.

Bio to come

Using seasonal forecasting to manage impacts of extreme ocean temperatures on marine industries

Dr Claire Spillman1, Dr Grant Smith1, Dr Alistair Hobday2, Mr Jason Hartog2, Dr Catherine de Burgh-Day1, Ms Paige Eveson2

1Bureau of Meteorology, Melbourne, Australia, 2CSIRO Oceans and Atmosphere, Hobart, Australia

Anomalously warm ocean temperatures have implications for many marine systems and industries, including mass coral bleaching and mortality, reduced aquaculture yields and altered wild fish migration patterns. Seasonal forecasts from dynamical ocean-atmosphere models of marine heatwaves and their drivers can be very useful tools for managers and business owners, allowing for proactive management responses. The Australian Bureau of Meteorology’s seasonal forecast model ACCESS-S1 currently produces operational real-time global forecasts of sea surface temperatures, with tailored outlooks produced for coral reef, aquaculture and wild fisheries management in Australian and New Zealand waters. Thermal stress forecast products have been developed, incorporating both the magnitude and duration of heat stress events, with widespread management applications. Advance warning of marine heat events can enable managers and industries to plan ahead and effectively manage resources to reduce impacts of such events. Additionally, ACCESS-S1 seasonal forecasts have also been used to inform planning of monitoring programs and event-responsive instrument deployments such as the IMOS glider program. Seasonal forecasts are a valuable tool to improve both the understanding and the management of these events, as well as the complex interactions that lead to them, particularly in a changing climate.

Dr Claire Spillman holds a PhD in Environmental Engineering and joint BEng/BSc degrees in Environmental Engineering (Hons) and Chemistry from the University of Western Australia. Her postgraduate work investigated impacts of estuarine circulation and oceanic inputs on aquaculture production using high resolution hydrodynamic-ecological modelling.
Dr Spillman is a senior research scientist at the Bureau of Meteorology, Australia. Her current research is primarily focused on dynamical seasonal forecasting in marine applications, particularly for coral reef and fisheries management. Applications include predictions for Great Barrier Reef coral bleaching risk, commercial fisheries and aquaculture on multiweek to seasonal timescales.

The development of the New Zealand Ocean Operational Forecast System

Dr Joao Marcos Azevedo Correia de Souza1

1, Raglan, New Zealand

New Zealand’s maritime domain is one of the largest on the planet. The seafood sector alone brings $4.18B to NZ annually. MetService is the responsible institution for providing a reliable forecast system of the ocean estate to respond to such demand. To accomplish this, a complex system including different ocean models and information endpoint delivery mechanisms was developed. The system is designed for fast operationalization of state-of-the-art techniques and portability between different platforms. A mix of “Regional Ocean Modeling System” (ROMS) and “Semi-implicit Cross-scale Hydroscience Integrated System Model” (SCHISM) domains are used to evaluate and predict ocean circulation and state properties, while “Wave Watch III” (WW3) and “Simulating Waves Nearshore” (SWAN) are used for simulating surface gravity waves. An architecture based on docker images and controlled by an “in house” built python based scheduler ensures a stable and robust system. Following international best practices, new developments to include wave-circulation coupling and data assimilation are under-way. These are undertaken in the framework of publicly funded research projects. A general description of the operational system with its unique architecture is presented. The development of new features is discussed, with a special focus on the assimilation of ocean observations into the national circulation model. The steps taken in the design and implementation of this national operational model are discussed. This effort is part of the Moana Project – a national research project that includes the participation of the main oceanographic institutes in New Zealand.

With more than 15 years of experience, my expertise is in interdisciplinary ocean processes and data assimilative hydrodynamic simulations. I was the principal investigator on several research projects, including the development of an ocean reanalysis using the ROMS model with 4-dimensional variational data assimilation to investigate predictability of ocean forecast systems, analysis of deep circulation in the Gulf of Mexico using a combination of observations and model results, and range of nearshore circulation studies. Currently, I am the science team leader for the MetOcean research and development team and responsible for the development of the data assimlation system.

Using ocean models for safety and mission success in an operational environment: a Navy perspective

Ms Joanne Haynes1

1Royal Australian Navy, Canberra, Australia

Understanding and exploiting the environment above, at, and below the ocean surface is key to ensuring the best possible employment of Defence capability in order to protect Australia’s national interests. In the Navy context, ocean models play a key role in ensuring the safety of our assets and personnel at sea, as well as equipping the war-fighter with sufficient information to gain tactical advantage in an operationally uncertain environment. This brief will focus on the role of operational oceanography in the provision of forecasting products to the Under Sea Warfare (USW) community in order to ensure safety at sea and achieve mission success. The ocean forecasting tools and models in use by the Navy have been developed as part of the Bluelink Ocean Forecasting Project (a RAN/BoM/CSIRO collaboration) and include: the global scale Ocean Forecasting Australia Model (OFAM), and the regional scale Relocatable Ocean Atmosphere Model (ROAM) and Australian Defence Environmental Prediction Tool (ADEPT). Although they are unlikely to be operating in a contested space, marine industries face a number of similar operational challenges to the Navy due to uncertainties in the ocean environment. Opportunities may exist for better collaboration between marine industries, Defence and the other Bluelink partners. These could include the collection and sharing of ocean observations for model verification and re-analysis purposes, and the identification and analysis of oceanic events which are often difficult to forecast (for example, internal waves) in order to ensure the ongoing usefulness and reliability of operational ocean models well into the future.

Commander Jo Haynes joined the Royal Australian Navy in 1998 and is a Maritime Geospatial Officer – Meteorology and Oceanography (METOC) specialist. She holds a number of tertiary qualifications including a Master of Science in Physical Oceanography and a Master of Military and Defence Studies. Commander Haynes has completed a variety of sea and shore postings and has a breadth of experience in roles within Navy and the Joint space, including operational deployments to the Persian Gulf, Afghanistan and Antarctica. Commander Haynes assumed her current position as the SO1 METOC Undersea Warfare at the Australian Geospatial-Intelligence Organisation in January 2019

Integrating physical and statistical models: a Bayesian approach to predictive uncertainty quantification of solitons

Dr Edward Cripps1, Mr Andrew  Manderson1, Dr  Matt Rayson5, Professor Mark Girolami2,4, Dr John Paul Gosling3, Professor Melinda Hodkiewicz6, Prof. Greg Ivey5, Dr Nicole Jones5

1Department of Mathematics and Statistics, University Of Western Australia, Perth, Australia, 2Alan Turing Institute, The British Library, London, UK, 3School of Mathematics and Statistics, University of Leeds, Leeds, UK, 4Department of Mathematics, Imperial College London, , UK, 5Oceans Graduate School, University of Western Australia, Perth, Australia, 6Faculty of Engineering and Mathematical Sciences, University of Western Australia, Perth, Australia

In shelf seas of 50 to 500 m depth solitons (non-linear internal waves), generated by tidal forcing over topography, are the main driver of extreme currents, induce some of the largest stresses on offshore infrastructure, drive sediment resuspension, and influence dynamic positioning systems during operations. This work demonstrates how recent advances in Bayesian statistical methods and computing integrates with physical models to predict solitons, provide industrial tools for decision making under uncertainty and refine our scientific understanding of associated ocean dynamic processes.  Using data collected on the North West Shelf, we estimate a Bayesian hierarchical model of density stratification and initial amplitude inputs and propagate the results through the Kortweg-de Vries (KdV) equation soliton forecast model.  Posterior distributions summarise the predictive uncertainty of maximum soliton amplitudes, density stratification characteristics, isopycnal heights and various ocean dynamic processes.  Code is implemented in the probabilistic programming language Stan to estimate the Bayesian model and prototype frontend software has been developed.    The work is multidisciplinary and is a collaboration between The Industrial Transformation Hub for Offshore Floating Facilities, University of Western Australia, and The Programme for Data Centric Engineering, Alan Turing Institute-Lloyd’s Register Foundation, London.


Edward Cripps is a researcher at the Department of Mathematics and Statistics, University of Western Australia, in Bayesian statistical methods, with a particular interest in spatio-temporal oceanic and atmospheric applications.   He is Chief Investigator for the Data Analytics programme of the ARC Industrial Transformation Research Hub for Offshore Floating Facilities and the ARC Industrial Transformational Training Centre for Transforming Maintenance through Data Science.

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