A5090235209- Atmospheric and Environmental Gas Dynamics
- Atmospheric Ozone and Climate
- Hydrocarbon exploration and reservoir analysis
- Reservoir Engineering and Simulation Methods
- Climate variability and models
- Statistical Methods and Bayesian Inference
- Bayesian Methods and Mixture Models
- Geochemistry and Geologic Mapping
- Atmospheric chemistry and aerosols
- Statistical Methods and Inference
- Soil Geostatistics and Mapping
- Carbon Dioxide Capture Technologies
- Oceanographic and Atmospheric Processes
- Remote Sensing and LiDAR Applications
- Teaching and Learning Programming
- Environmental Impact and Sustainability
- Advanced MRI Techniques and Applications
- Geological Modeling and Analysis
- Complexity and Algorithms in Graphs
- Advanced Clustering Algorithms Research
- Spectroscopy and Laser Applications
- Optimization and Search Problems
- Methane Hydrates and Related Phenomena
- Meteorological Phenomena and Simulations
- Complex Network Analysis Techniques
University of Wollongong
2019-2025
The University of Western Australia
2025
The University of Sydney
2019
The University of Melbourne
2006-2007
Software (Spain)
2007
Abstract. Accurate accounting of emissions and removals CO2 is critical for the planning verification emission reduction targets in support Paris Agreement. Here, we present a pilot dataset country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) stock changes aimed at informing countries' budgets. These estimates are based on “top-down” NCE outputs from v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble inverse...
Abstract The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction dioxide (XCO 2 ) retrievals may fill some this gap. Here, we estimate using atmospheric inversions the Orbiting Carbon Observatory (OCO‐2) XCO within different platforms, including Global Assimilation System (GCAS) v2, Copernicus Atmosphere Monitoring Service, OCO‐2 Model Inter‐comparison Project (MIP). We...
For offshore structures like wind turbines, subsea infrastructure, pipelines, and cables, it is crucial to quantify the properties of seabed sediments at a proposed site. However, data collection costly, so analysis must be made from measurements that are spatially sparse. Adding this challenge, structure exhibits both nonstationarity anisotropy. To address these issues, we propose GeoWarp, hierarchical spatial statistical modeling framework for inferring 3-D geotechnical sediments. GeoWarp...
For offshore structures like wind turbines, subsea infrastructure, pipelines, and cables, it is crucial to quantify the properties of seabed sediments at a proposed site. However, data collection costly, so analysis must be made from measurements that are spatially sparse. Adding this challenge, structure exhibits both nonstationarity anisotropy. To address these issues, we propose GeoWarp, hierarchical spatial statistical modeling framework for inferring 3-D geotechnical sediments. GeoWarp...
Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully hierarchical statistical framework flux inversion trace gases from flask, in situ, and remotely sensed data. extends the conventional synthesis through consideration correlated error term, capacity online bias correction, provision uncertainty quantification on all unknowns that appear model. We show, an observing system simulation experiment (OSSE), these extensions are crucial when data indeed...
Abstract. Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out first global hierarchical Bayesian inversion to solve for nitrous emissions, which includes prior emissions with truncated Gaussian distributions model errors, in order examine drivers of surface growth rate. We show that both climatic variability are key variations rate between 2011 2020. derive...
Abstract Tropical lands play an important role in the global carbon cycle yet their contribution remains uncertain owing to sparse observations. Satellite observations of atmospheric dioxide (CO 2 ) have greatly increased spatial coverage over tropical regions, providing potential for improved estimates terrestrial fluxes. Despite this advancement, spread among satellite‐based and in‐situ CO flux inversions northern Africa (NTA), spanning 0–24°N, large. Satellite‐based annual source 0.8–1.45...
Consensus clustering has emerged as one of the principal problems in data mining community. In recent years theoretical computer science community generated a number approximation algorithms for consensus and similar problems. These run polynomial time, with performance guaranteed to be at most certain factor worse than optimal. We investigate feasibility algorithms, an attempt link data-mining research. On realistic sets, quadratic running times are impractical. Unfortunately these even...
Tropical lands play an important role in the global carbon cycle yet their contribution remains uncertain owing to sparse observations. Satellite observations of atmospheric dioxide (CO) have greatly increased spatial coverage over tropical regions, providing potential for improved estimates terrestrial fluxes. Despite this advancement, spread among satellite-based and in-situ CO flux inversions northern Africa (NTA), spanning 0-24◦N, large. Satellite-based annual source 0.8-1.45 PgC yr...
Abstract Maritime engineering relies on model forecasts for many different processes, including meteorological and oceanographic forcings, structural responses, energy demands. Understanding the performance evaluation of such forecasting models is crucial in instilling reliability maritime operations. Evaluation metrics that assess point accuracy forecast (such as root-mean-squared error) are commonplace, but with increased uptake probabilistic methods may not consider full distribution. The...
Daily precipitation has an enormous impact on human activity, and the study of how it varies over time space, what global indicators influence it, is paramount importance to Australian agriculture. We analyze 294 million daily rainfall measurements since 1876, spanning 17,606 sites across continental Australia. The data are not only large but also complex, topic would benefit from a common publicly available statistical framework. propose Bayesian hierarchical mixture model that accommodates...
We present the AdaptSPEC-X method for joint analysis of a panel possibly nonstationary time series. The approach is Bayesian and uses covariate-dependent infinite mixture model to incorporate multiple series, with components parameterized by time-varying mean log spectrum. are based on AdaptSPEC, nonparametric which adaptively divides series into an unknown number segments estimates local spectra smoothing splines. extends AdaptSPEC in three ways. First, through mixture, it applies linked...
Abstract. Accurate accounting of emissions and removals CO2 is critical for the planning verification emission reduction targets in support Paris Agreement. Here, we present a pilot dataset country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) stock changes aimed at informing countries’ budgets. These estimates are based on "top-down" NCE outputs from v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble inverse...
The natural cycles of the surface-to-atmosphere fluxes carbon dioxide (CO2) and other important greenhouse gases are changing in response to human influences. These changes need be quantified understand climate change its impacts, but this is difficult do because occur over large spatial temporal scales cannot directly observed. Flux inversion a technique that estimates spatiotemporal distribution gas' using observations mole fraction chemical transport model. To infer trends identify phase...
Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully hierarchical statistical framework flux inversion trace gases from flask, in situ, and remotely sensed data. extends the conventional Bayesian-synthesis through consideration correlated error term, capacity online bias correction, provision uncertainty quantification on all unknowns that appear model. We show, an observing system simulation experiment (OSSE), these extensions are crucial when...
A Model Intercomparison Project (MIP) consists of teams who each estimate the same underlying quantity (e.g., temperature projections to year 2070), and spread estimates indicates their uncertainty. It recognizes that a community scientists will not agree completely but there is value in looking for consensus information range disagreement. simple average teams' outputs gives estimate, it does recognize some are more variable than others. Statistical analysis variance (ANOVA) models offer...
Motivated first-year undergraduate students should be exposed to some of the processes research and latest results. This brings them into university culture quickly encourages feel part development computer science discipline.To this end, in a second-semester subject were presented with programming project which goal was implement several approximation algorithms for an active problem. In addition, they asked complete four related mathematical puzzles. The lecturer author student show how...
WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully hierarchical statistical framework flux inversion trace gases from flask, in situ, and remotely sensed data. extends the conventional Bayesian-synthesis through consideration correlated error term, capacity online bias correction, provision uncertainty quantification on all unknowns that appear model. We show, an observing system simulation experiment (OSSE), these extensions are crucial when data indeed...
Abstract Our environment is undergoing rapid change as greenhouse gases warm the planet. Noel Cressie, Andrew Zammit-Mangion, Josh Jacobson, and Michael Bertolacci use WOMBAT, a Bayesian hierarchical statistical framework, to infer spatio-temporal distribution of CO2 surface fluxes