Factors affecting ENSO predictability in an empirical model of tropical air-sea interactions

Harun Rashid1

1CSIRO, Melbourne, VIC


El Niño‒Southern Oscillation (ENSO) is the dominant mode of tropical interannual climate variability, with large influence on global weather and climate. Here we construct an empirical dynamical model of tropical Pacific air-sea interactions to investigate various factors affecting ENSO prediction skill. A hierarchy of models with increasing complexity have been constructed using data for 1958-1990 and retrospective forecasts are made for 1991- 2017 for each of the models. The model with the best ENSO prediction skill is then chosen as a reference model. The reference model’s predictability limit, defined here as the forecast lead month of 0.5 anomaly correlation (AC), is around 11 months. After establishing the suitability of this model by comparing its simulated ENSO properties with the observed, we use it to determine the relative importance of several factors affecting the model’s ENSO prediction skill. In particular, we examine the extent to which ENSO prediction skill is affected by the main atmosphere-ocean interaction processes―thermocline and zonal wind feedbacks and zonal wind forcing―on ENSO predictability. We find that all these processes significantly affect ENSO predictability and extend the predictability limit by up to five months, with the largest effect coming from the thermocline feedback. The other processes with progressively smaller effects are the total zonal wind forcing, zonal wind feedback and external zonal wind forcing. This result suggests that the dynamical seasonal prediction models must have a good representation of the major ENSO processes in order to have good ENSO prediction skills.


Dr Harun Rashid is a senior research scientist in CSIRO Climate Science Centre of the Oceans and Atmosphere BU. His expertise is in understanding and predicting climate variability using dynamical and empirical models of the earth’s climate system. His current interest is modelling El Niño‒Southern Oscillation (ENSO) using Coupled Global Climate Model (CGCM) and Empirical Dynamical Models (EDMs).

An semi-supervised framework for the classification and collation of complex radio galaxies using rotationally invariant self-organizing maps

Tim Galvin1

1CSIRO, Perth, WA, Australia


The Australian Square Kilometer Array Pathfinder (ASKAP) is a next generation radio telescope located in Western Australia. Once fully commissioned one of its key science projects, the Evolutionary Map of the Universe (EMU), is expect to detect upwards of 70 million radio objects over five years. This absurd data volume requires new approaches to classify complex objects to extract the maximum amount of scientific knowledge. We investigate how rotationally invariant self-organizing maps (SOM) can be used as a tool to identify the predominate morphological shapes of sources as well as a tool to transfer knowledge from labelled data-sets to unlabelled subjects. Further, by exploiting the transform information learnt by the SOM, we construct an approach that is able to identify the individual components of complex sources across multiple wavelengths without requiring large training sets with known labels. As this approach requires only source positions and no labels for training, it is ideal for EMU and similar deep, all sky radio surveys from the next generation of radio telescopes.


PostDoc working at CSIRO Astronomy and Space Science researching applications of machine learning methods for the classification of radio objects.

It’s all connected! Visualisation, tools and systems for sustainable aquaculture.

Mr Daniel Wild1, Andy Steven, John Andrewartha, Patricio Bernal, Francisco Bravo, Rodrigo Bustamante, Scott Condie, Mark Crane, Jeffrey Dambacher, Sven Dowideit, Elizabeth Fulton, Rebecca Gorton, Mike Herzfeld,  Jonathan Hodge, Eriko Hoshino,  Erin Kenna, Nugzar Margvelashvili, Nick Moody, Diego Ocampo, Roland Pitcher, Shane Richards, Farhan Rizwi, Santosh Aryal, Jenny Skerratt,  Amara Steven, Linda Thomas, Sharon Tickell, Ingrid van Putten, Paula Vaquero,  Karen Wild-Allen

1CSIRO, Brisbane, Australia


To sustainably manage one of the largest aquaculture industries on the planet, Chile’s industry regulators need to be capable of timely, evidence-based decision making.

The SIMA Austral (Integrated System for Aquaculture Management) project seeks to transform and enhance Chile’s national aquaculture sector through the implementation of an integrated environmental and sanitary management system, supporting both long-term strategic planning, and rapid, at-a-glance situational awareness.

In this talk, we will give an overview of the SIMA Austral platform with some visual examples of the day-to-day operational problems it addresses, and look at the technology stack used in our solution – including tools like GitLab, Docker Swarm, KeyCloak, Traefik, Elasticsearch, Prometheus, Grafana, Web Processing Services and more.


Dan works in the Coastal Informatics team within CSIRO as a software developer, typically building temporo-spatial analysis tools leveraging in-situ observation and near real-time numerical model data.

As a data-driven design and visualisation specialist – Dan uses technology to maximise scientific impact by making complex information more accessible to decision makers and the general public.

Quantum Computing. Available Platforms and Software for Investigating Quantum Algorithms.

Dr Fanel Donea1, William Mead1, Thomas Leatham1

1Csiro Advanced Scientific Computing, Clayton, Australia


The poster presents results from investigating a selection of currently available platforms for running quantum computing algorithms, including direct access to publicly available quantum computers, third party quantum emulators running via the web, and quantum emulators installed on local computers. It also presents work done on building software for a quantum computer emulator. The poster includes work done within the framework of two vacation scholarships offered by CSIRO in 2018-2019.


To be confirmed

Classifying and predicting the electron affinity of diamond nanoparticles using machine learning

Dr Chris Feigl1, Dr Benjamin Motevalli1, Dr Baichun Sun1, Dr Amanda Parker1, Dr Amanda Barnard1

1CSIRO, Docklands, Australia


Using a combination of electronic structure simulations and machine learning we have shown that the characteristic negative electron affinity (NEA) of hydrogenated diamond nanoparticles exhibits a class-dependent structure/property relationship. Using a random forest classifier we find that the NEA will either be consistent with bulk diamond surfaces, or much higher than the bulk diamond value; and using class-specific random forrest regressors with extra trees we find that these classes are and either size-dependent or anisotropy-dependent, respectively. This suggests that the purification or screening of nanodiamond samples to remove strained, heterogeneous or anisotropic particles may be undertaken based on the negative electron affinity.


Dr Chris Feigl is a Research Scientist working within the Materials and Molecular Modelling team of Data61, CSIRO.  He completed his PhD in Theoretical Condensed Matter Physics from RMIT University in 2012, after which he went into executive management for education and training and humanitarian aid organisations in the middle-east region.  Since returning to Australia, Chris’s research has re-focused on applying machine learning methods to the prediction and characterisation of nanomaterial properties.

End User HPC Systems Overview

Dr Ahmed Shamsul Arefin1

1Csiro, Canberra, Australia


The CSIRO’s HPC cluster systems are composed of 500+ compute nodes with various strengths, values and features. Some of the nodes are made up with the world’s fastest GPUs, while some with TB+ of DRAM and so on. However,  the whole family of Linux computing facility is managed by a single SLES software image, rolled out and setup according to its target node profiles. A commercial HPC management tool called Bright Cluster Manager (BCM) is effectively utilized to tackle the HPC administration and monitoring workloads.  In this work, we briefly report the basic framework of management of the CSIRO’s HPC systems and introduce with an upcoming end user monitoring feature called User Portal.


Dr Ahmed Arefin is a Computation Scientist working within the HPC Systems Team, Scientific Computing Platforms, CSIRO. He completed his PhD in Computer Science (Data-Parallel Computing & GPUs) from the University of Newcastle, Australia and worked as a Postdoctoral Researcher (Parallel Data Mining) at the Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine (CIBM), The University of Newcastle, Australia. His research interest focuses on the application of HPC in data mining, graphs and visualization.

Conquering Uncertainty: A Vision for HPC to Maximise the Value of Combat Simulation

Mr Denis Shine1

1Defence Science And Technology Group, Edinburgh, Australia


Land Capability Analysis (LCA) in the Defence Science and Technology Group of the Australian Department of Defence uses closed-loop combat simulation extensively to support Army modernisation decision making. These simulations will assess many options over hundreds of replications, requiring distributed computing systems capable of executing them in parallel. Simulation of combat is naturally an area of great uncertainty. Availability of empirical data is sparse. Entities (soldiers and military platforms) require behaviours and tactics, where there is often no “right” answer, merely a number of “good” answers. Finally combat, both real and simulated, is highly uncertain, with results often impacted heavily by rare but important events.

Presently, LCA maintains a small computing cluster to execute these simulations, but in the near future DST Group is acquiring a High Performance Computing (HPC) capability which will greatly enhance the available computing power. Leveraging HPC represents an opportunity to address many of these problems, both through evolutionary approaches such as exploring more options and conducting wider sensitivity studies, but also by applying more revolutionary techniques, such as “smart” meta-models that explore vast, highly dimensional data spaces, to algorithms which evolve soldier behaviour based on simulated results.

This presentation will articulate a vision for how LCA can use a combination of modern computing infrastructure and modern analytical techniques to address these challenges and ensure it can continue to use simulation as a keystone analytical capability.


Mr Denis Shine is a researcher working for the Defence Science and Technology Group. He specialises in the application of Land Combat Simulation to support Army decision making.

Identifying interesting observations in large astronomical data using a coarse-grained complexity measure

Mr Gary Segal1,3, Dr David Parkinson1,2, Professor Ray Norris3,4

1University Of Queensland, ST LUCIA, Australia,

2Korea Astronomy and Space Science Institute, Daejeon , Korea,

3CSIRO Astronomy and Space Science, Epping, Australia,

4Western Sydney University, Penrith South, Australia


The volume of data that will be produced by the next generation of astrophysical instruments represents a significant opportunity for making unplanned and unexpected discoveries. Conversely, finding unexpected objects or phenomena within such large volumes of data presents a challenge that may best be solved using computational and statistical approaches. We present the application of a coarse-grained complexity measure for identifying interesting observations in large datasets. This measure, which has been termed apparent complexity, has been shown to model human intuition and perceptions of complexity. Unlike supervised learning approaches it does not learn the specific features associated with known interesting observations positioning the approach as an ideal candidate for identifying the unexpected. The approach is computationally efficient and fast making it an ideal candidate for processing very large datasets. We show using data from the Australia Telescope Large Area Survey (ATLAS) that the approach can be used to distinguish between images of galaxies which have been classified as having simple and complex morphologies.


Gary Segal is a PhD student at the University of Queensland and an Australia Telescope National Facility graduate student conducting research at the intersection of statistics, computer science and astronomy. Gary also has a professional background as a quantitative analyst. He finds particular delight in connecting deep theory with practical applications.

CSIRO Galaxy Bioinformatics Portal: A user friendly GUI for bioinformatics on HPC infrasructure

Mr Joel Ludbey1

1CSIRO, Camberwell, Australia


The talk will have two basic themes. One being focused around the features and usability of the Galaxy portal and how it can benefit bioinformaticians. The second being focused around the specific deployment and configuration we have at CSIRO and how it exposes the HPC infrastructure without compromising the benefits of a workflow tool such as Galaxy.

While HPC is particularly suited to bioinformatics it can be troublesome to get over the hurdle that is the CLI particularly if you do not have a technical background. The galaxy portal aims to remove this hurdle and hide away the HPC components behind a simple point-and-click interface that facilitates the creation of reproducible and portable workflows.

Galaxy usability and benefits:

– What is galaxy?

– Why would I want to use galaxy?

– Benefits and features of galaxy?

– Galaxy and CSIRO HPC

– Simple demonstration of a basic workflow using local and remote jobs

Galaxy deployment and configuration

– How is galaxy configured in CSIRO

– How does the HPC infrastructure fit into all this

– Automated deployment using ansible

– Why did we do things this way, could we do better


Joel Ludbey has been operating as a part of the IM&T SC group since 2008, starting as a distributed systems administrator for CSIRO under the Australian Research Collaboration Service (ARCS) joint venture which was responsible for providing eResearch services to the Australian research community. While he has held several system administration focused roles within IM&T SC he is currently operating as the Scientific Workflow Specialist for the IM&T SC User Services group and is the primary technical support and architect of the CSIRO Galaxy portal . A role that aims to aid researchers in the creation, implementation and optimisation of scientific applications and workflows utilising CSIRO’s computational facilities and external partner resources.

Fast and stable multivariate kernel density estimation by fast sum updating

Dr Nicolas Langrené1, Mr Xavier Warin2

1CSIRO, Docklands, Australia,

2EDF, Paris, France


Kernel density estimation and kernel regression are powerful but computationally expensive techniques: a direct evaluation of kernel density estimates at M evaluation points given N input sample points requires a quadratic O(M.N) operations, which is prohibitive for large scale problems. For this reason, approximate methods such as binning with Fast Fourier Transform or the Fast Gauss Transform have been proposed to speed up kernel density estimation. Among these fast methods, the Fast Sum Updating approach is an attractive alternative, as it is an exact method and its speed is independent from the input sample and the bandwidth. Unfortunately, this method, based on data sorting, has for the most part been limited to the univariate case. In this talk, we revisit the fast sum updating approach and extend it in several ways. Our main contribution is to extend it to the general multivariate case for general input data and rectilinear evaluation grid. Other contributions include its extension to a wider class of kernels, including the triangular, cosine and Silverman kernels, and its combination with a fast approximate k-nearest-neighbours bandwidth for multivariate datasets. Our multivariate regression and density estimation tests confirm the speed, accuracy and stability of the method. We hope this work will renew interest for the fast sum updating approach and help solve large scale practical density estimation and regression problems.


Nicolas Langrené is a Research Scientist at Data61, CSIRO. His main research interest is in mathematical and numerical methods, focusing on applied probability and stochastic optimization, with applications in computational finance, computational statistics and energy markets. He completed his MEng in Applied Mathematics and Computer Science at Grenoble INP – ENSIMAG and the University of Grenoble Alpes in 2009, and completed his Msc in Statistics, Probability and Mathematical Finance and his PhD in Stochastic Control at the University of Paris Diderot, Sorbonne Paris Cité in 2010 and 2014 respectively. Before joining CSIRO, he also worked with the investment bank Natixis and the power company EDF.



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