Critical Thresholds for Data Sharing

Greg Williams1

1RPS MetOcean,

For over 40 years, RPS have conducted metocean measurement programmes on the North West Shelf of Australia and established a suite of state-of-the-art tools and proven wind, wave, and current model configurations for setting design and operational criteria for offshore facilities, pipelines, ports and coastal infrastructure and Defence applications.

In a domain where measurement techniques, quality assurance and control processes, and numerical weather prediction models are undergoing continuous improvement, a variety of mechanisms for repeatable verification and evaluation against specific aims, applications, and performance metrics are well defined, established, and generally formalised by expert working groups, international agencies, and industries that have been engaged in these activities for decades.

In the past, commercial measurement activities occurred in regions where data were sparse or non-existent, and numerical modelling was used to understand the larger area or long-term environmental extremes. Now, with over 1000 measurement sites, 25000 datasets, and a billion data records supporting the calibration of numerical models, it is possible to design an optimal measurement+modelling network, sufficient to replace a large portion of the existing sites supporting day to day operations – with associated savings in time, convenience, maintenance, cost sharing and collaborative data sharing by participants. A significant additional advantage would be the establishment of consistent practice across all elements of maritime design and operation.

This presentation will demonstrate the foundation and design of an optimal measurement network providing support for offshore oil&gas, ports, coastal, defence, and research activities in the North West Shelf of Australia.

Measuring waves and winds from Autonomous Surface Vehicles (ASV)

Darren Burrowes1

1Bluezone Group, ,

Methods for measuring waves and winds from a Wave Glider Autonomous Surface Vehicle (ASV) are described and evaluated. The wave method utilises the frequency spectra of orbital velocities measured by GPS, and the wind stress method utilises the frequency spectra of turbulent wind fluctuations measured by ultrasonic anemometer. Both methods evaluate contaminations from vehicle motion. The methods were evaluated with 68 days of data over a full range of open ocean conditions, in which wave heights varied from 1 to 8 m and wind speeds varied from 1 to 17 m/s. Reference data were collected using additional sensors onboard the vehicle. For the waves method, several additional datasets are included which use independently moored Datawell waverider buoys as reference data. Bulk wave parameters are determined within 5% error, with biases of less than 5%. Wind stress is determined within 4% error, with 1% bias. Wave directional spectra also compare well, although the Wave Glider results have more spread at low frequencies.

 

New Generation of Wave Forecast Models, Made in Australia

Prof. Alexander Babanin1

1University of Melbourne, , Australia

This major update of the physics of the third generation models took 15 years to complete and the effort is ongoing. New source terms for wind input, whitecapping dissipation, interaction of waves with adverse winds (negative input), swell attenuation, wave-bottom interactions, wave-ice interactions have been developed and implemented in official releases of WAVEWATCH-III and SWAN models. Physics and parameterisations for the new source functions are based on field experiments and observations, which allowed us to reveal features and processes previously unknown and dynamics not accounted for. A new source functions for nonlinear wave-wave interactions, including quasi-resonant (rogue-wave) interactions, and for wave-current interactions are also under way and will be outlined. For extreme Metocean conditions, physics of the wind input and whitecapping dissipation terms exhibit additional behaviours irrelevant or inactive at moderate weather.


Biography:
Alexander V. Babanin is Professor in Ocean Engineering at the University of Melbourne, Australia. Qualifications: BSc (Physics), MSc (Physical Oceanography) (Lomonosov Moscow State University, Russia), PhD (Physical Oceanography) (Marine Hydrophysical Institute, Sebastopol, Russia). Worked as a Research Scientist in the Marine Hydrophysical Institute, as an academic In the University of New South Wales, ADFA, Canberra, The University of Adelaide, South Australia, Swinburne University of Technology, Melbourne. Areas of expertise, research and teaching, are wind-generated waves, maritime and coastal engineering, air-sea interactions, ocean turbulence and ocean dynamics, climate, environmental instrumentation and remote sensing of the ocean. These include extreme Metocean conditions, from tropical cyclones to Arctic and Antarctic environments. 280+ career total publications.

The Wave Climate of the Southern Ocean

Qingxiang Liu

Although the Southern Ocean is often viewed as a very remote area, it plays a critical role in global climate. Waves generated in intense Southern Ocean storms propagate across the Indian, Pacific and Atlantic Oceans and define the wave climate for many areas of these oceans. In addition, the wind and wave climate of the Southern Ocean plays an important role in determining the rate of decay of Antarctic glaciers which are an important element in global sea level change. Obviously, for Australia the Southern Ocean play a critical role in many operational activities. Despite this important role, little is known about the wind and wave climate of this vast region. This presentation will bring together a series of unique datasets to provide a comprehensive view of this wind and wave climate. These datasets include: the long duration model reanalysis dataset ERA-I, a 33-year calibrated and validated altimeter dataset and buoy data from four deployments. These buoys have been located at: Macquarie Island (540S), Campbell Island (520S), a site west of South America (550S) and the Southern Ocean Flux Buoy south of Tasmania (460S). Data from these buoy deployments spans a total of approximately 7 years and provide directional spectra in unique long fetch environments. In addition to providing valuable data for model validation, coastal and offshore engineering design and Naval Architecture, these combined datasets provide new insights into air-sea interaction under extremely long fetch conditions. The paper will also use the satellite datasets to investigate changes in wave conditions in recent decades and the role that climate variability plays in such changes. This analysis will examine: long-term trends, annual variability and multi-year oscillations.


Biography:

Qingxiang Liu was born in 1989. He graduated with a degree in marine science from Ocean University of China, Qingdao, China, in 2011, and received the Ph.D. degree in physical oceanography in 2016 with a thesis dedicated to altimeter measurements of ocean surface winds and waves. In 2017, he joined the Ocean Engineering Centre of the University of Melbourne as a research fellow. His main research interests are related to spectral wave modelling, wave-ice interactions, tropical cyclone and remote sensing of ocean waves.

Precise positioning for the maritime sector

Miss Imogen Rea1, Mrs Anna Riddell1

1Geoscience Australia, ,

Geoscience Australia is delivering a national capability that will provide accurate, reliable and instant positioning across Australia and its maritime zones. To ensure that accurate positioning information can be received without the need for mobile phone or internet coverage, we’re delivering an Australian Satellite-Based Augmentation System (SBAS). An SBAS will overcome current gaps in mobile and radio communications and correct positioning signals down to centimetres, greatly benefiting the maritime sector through improved positioning information available anywhere, anytime.

A recent 18 month trial of SBAS signals demonstrated significant benefits of precise positioning across commercial shipping, marine construction, offshore research, cruise, commercial port operations and general harbour management. It’s expected that an operational SBAS will provide greater redundancy to automated and semi-automated port operations, enable real-time hydrographic surveying for dynamic harbour environments and congested areas and replace the costs of subscription services for offshore activities. Demonstrator project participants included Maritime Industry Australia Limited, Acoustic Imaging, Port Authority of NSW and Identic Solutions.


Biography:
Imogen Rea is a satellite-based augmentation system (SBAS) engineer, working within the Positioning Australia program at Geoscience Australia. She previously worked with users assessing the SBAS test-bed performance and developing an economics benefit study. With a bachelor’s in Aerospace Engineering, Imogen is an advocate for women in STEM and is passionate about communicating the terrestrial applications of space in Australia.

NERA’s Oceanography Projects

Mr Timothy Duff1

1NERA, Perth, Australia

National Energy Resources Australia (NERA) is one of six Growth Centres established by the Australian Government under the Industry Growth Centres Initiative. NERA’s role is to grow collaboration and innovation to assist the energy resources sector manage cost structures and productivity, direct research to industry needs, deliver the future work skills required and promote fit-for-purpose regulation.

 

NERA’s work program focuses on applied R&D, collaboration, innovation and regulatory reform with an aim to reduce costs, unlock resources, accelerate growth of the local supply chain and support the uptake of digital and clean technologies. While NERA’s portfolio of forty industry led collaborative projects is diverse, a number of projects have a strong oceanography aspect. These projects include:

–              Exmouth Integrated Artificial Reef: a unique project that will enhance marine habitat and recreational fishing opportunities by creating Australia’s first integrated artificial reef, which is partially made up of re-purposed offshore structures from the oil and gas industry.

–              TASER Living Lab: assessing the effectiveness of innovative coatings, materials and technologies against calcareous deposition and marine organism growth on subsea equipment.

–              Cluster Program: Subsea Innovation Cluster Australia (SICA) and the Ocean Energy Cluster aim to accelerate growth and expand capabilities through collaboration.

–              National Decommissioning Research Initiative: aims to improve the knowledge base underpinning decisions relating to the impact of infrastructure on the marine environment.

 

This presentation will provide a summary of these projects, including the key deliverables, progress to date and how the information will be shared with the wider industry.


Biography:
As Project Manager at NERA, Tim’s role is to ensure the effective delivery of programs and designated projects. This includes managing key stakeholder relationships, promoting project outcomes and identifying opportunities for collaboration in the energy resources sector. Tim supports NERA’s role to create connections to innovate and transform the energy resources sector. Tim holds a Bachelor of Engineering (Mining Environmental Engineering) and a Master of Business Administration.

OceanCurrent: New opportunities in satellite SST

Dr Madeleine Cahill1, Dr Edward King1, Mr Roger Scott1

1CSIRO, Hobart, Australia

Himawari-8 was launched by JAXA (Japan Aerospace Exploration Agency) in 2015 and the Bureau of Meteorology has been producing the 10min full disk SST since mid-2017. With 10min sampling Himawari-8 greatly improves the chances of getting cloud-free images throughout the day but the 10min data also presents a data management issue. OceanCurrent provides images of 4hr composite SST based on the 2km Himawari-8 SST and all other AVHRR and VIIRS SST. Four-hour composites provide a high-enough time resolution SST record that allows small-scale oceanographic features to be discovered and followed, without overwhelming data volume. By including the higher resolution VIIRS and AVHRR SST we provide improved near-coastal SST. In particular, the VIIRS sensor offers the prospect of sub-1km spatial resolution which is an area of great potential and further research activity.


Biography:
Bio to come

Evaluation of a machine learning framework to forecast storm surge

Mr Daryl Metters1

1Queensland Department Of Environment And Science, Brisbane, Australia

Machine learning is being used to achieve solutions to issues in many areas of science. Forecasting storm surge is an area of interest that has traditionally relied on parametric and numerical modelling methodologies to achieve any degree of forecasting precision. Machine learning in comparison offers computationally inexpensive means of elucidating solutions to many issues. In this study machine learning is investigated as an alternative to modelling of storm surge using numerical modelling methods only. Two methodologies were implemented using inputs of: the non-tidal residual; wind speed and direction and; atmospheric pressure. A 24-hour forecast of sea level was achieved via initially (1) forecasting wind speed and direction and atmospheric pressure using two time series machine-learning models and (2) using BoM numerically modelled forecasts of wind speed and direction and atmospheric pressure. Six machine-learning models were then used to forecast the non-tidal residual using standard learning and testing machine-learning methods extended with the two weather forecast datasets. The 24 hour forecast of sea level and storm surge was then compared to the actual sea level and storm surge for each of the two methodologies and six machine learning models. Model performance was evaluated with correlations between actual and forecast sea level and storm surge levels. Good results were achieved with the numerical model forecast inputs giving a close fit to the sea level and a good correlation with actual storm surge. The time series generated inputs failed to achieve a significant correlation. The six machine learning models varied in their performance.


Biography:
Daryl Metters is a Senior Scientist in the Coastal Impacts Unit within the Queensland Government Department of Environment and Science. Daryl has a BSc (Hons) in Marine Science from Flinders University. Daryl has worked in the National Tidal Centre, Australian Bureau of Meteorology as a Tidal analyst, and as Manager Spatial Information (Tidal services) in Maritime Safety Queensland. His work includes monitoring sea levels in Australia and the South Pacific region, data management and operational aspects of tide gauge and wave monitoring networks in Queensland.

The feasibility of tidal energy within Australia’s future energy mix

A/Prof. Irene Penesis1, Dr Mark Hemer2, Dr Remo Cossu3, Dr Jenny Hayward4, Dr Jean-Roch Nader1, Dr Uwe Rosebrock2, Dr Alistair Grinham3, Dr Saad Sayeef4, Dr Peter Osman4, Dr Philip Marsh1, Dr Mike Herzfeld2, Dr David Griffin2, Ms Camille Couzi1

1Australian Maritime College, University Of Tasmania, Launceston, Australia, 2CSIRO Oceans and Atmosphere, Hobart , Australia, 3School of Civil Engineering, The University of Queensland, Brisbane, Australai, 4CSIRO Energy, Newcastle, Australia

For decades, tidal energy has been identified as a potential resource to meet Australia’s future low-emission energy needs. The Australian Tidal Energy (AUSTEn) project (http://www.austen.org.au), co-funded by the Australian Renewable Energy National Agency (ARENA), and led by the Australian Maritime College (University of Tasmania), in partnership with CSIRO and University of Queensland, with strong industry support (SIMEC-Atlantis Energy, MAKO Tidal Turbines Ltd) seeks to determine the technical and economic feasibility of tidal energy in Australia, based on the best understanding of resource achievable. The project also benefits from collaboration with international researchers from Acadia University, Canada, and Bangor University, UK, both of whom are at the forefront of international developments in tidal energy, and who support the project to gain international exposure.

The project consists of three interlinked components to support the emerging tidal energy sector. Component 1 will deliver a National Australian high-resolution tidal resource assessment; Component 2 comprises focused case studies at two promising locations for energy extraction – Banks Strait, eastern Bass Strait, and Clarence Strait, Northern Territory; and Component 3 will deliver a technological and economic feasibility assessment for tidal energy integration into Australia’s electricity infrastructure.

The outcomes of this project will provide considerable benefit to the emerging tidal energy industry, the strategic-level decision makers of the Australian energy sector, and the management of Australian marine resources by helping them to understand the resource, risks and opportunities available.


Biography:
Associate Professor Irene Penesis has developed key industry, research and government partnerships in the wave and tidal energy sector and runs one of the largest marine renewable energy research groups in Australia at the Australian Maritime College, University of Tasmania. Irene has been the driving force behind two major initiatives; Australian Renewable Energy Agency (ARENA) co-funded project, ‘Tidal Energy in Australia – Assessing Resource and Feasibility to Australia’s Future Energy Mix’ which started in 2017, and the Blue Economy Cooperative Research Centre (CRC) recently funded in April 2019. Irene is the Research Director of the Blue Economy CRC.

Using machine learning to improve operational wave forecasts

Dr Jeff Hansen1, Dr Chen Wu1, Prof Phil Watson1, Dr Diana Greenslade2

1University Of Western Australia, Crawley, Australia, 2Bureau of Meteorology, Melbourne, Australia

Operational wave forecasts rely on spectral wave models that due to their numerical implementation (i.e. phase-averaged) and resolution, either parametrize or do not fully resolve key physical processes that impact wave generation, propagation, and dispersion. These factors, coupled with potential errors in atmospheric forcing, can sometimes result in incorrect forecasts for wave conditions and/or the timing of their onset. Many offshore industries depend on accurate wave forecasting, and unexpected conditions may incur cost (due to halting an underway operation or a missed opportunity to complete an operation) or add safety concerns. In this presentation we outline results from an initial study to test the use of machine learning to adjust Bureau of Meteorology AUSWAVE-R wave forecasts. Two years of archived wave forecasts, each extending 72 hours, were extracted at the location of three WA Department of Transport directional wave buoys. Eighty percent of the observed and forecast wave conditions were used as a training data set for a Recurrent Neural Network algorithm which was then used to adjust the remaining 20% (randomly selected and independent from training data). This initial test resulted in the root mean square error of the forecasts being reduced by one-third for significant wave height and by nearly one-half for peak wave period and direction across all sites. Currently the technique is also being applied to the spectral data from the buoys and forecasts. These initial results indicate that machine learning can be an effective mean of improving existing operational wave forecasts with negligible additional computation.


Biography:
Dr Hansen Senior Lecturer in Oceanography at the University of Western Australia. He completed his PhD in 2011 at the University of California Santa Cruz and completed a postdoctoral appointment at Woods Hole Oceanographic Institution before joining the University of Western Australia in 2013. His research interests include ocean waves, nearshore and coastal processes, marine renewables, and marine remote sensing. He currently co-leads the Oceanography and Coastal Processes Research Theme for the Wave Energy Research Centre and is a Chief Investigator on an ARC Linkage project to establish the framework for a coastal erosion and inundation early warning system.

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