ORCID Profile
0000-0002-0230-9189
Current Organisation
NSW Department of Planning, Industry and Environment
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Atmospheric Sciences | Atmospheric Dynamics | Meteorology | Climate Change Processes | Water Resources Engineering | Surfacewater Hydrology | Physical Geography and Environmental Geoscience |
ANZSRC Socio-Economic Objective (SEO)
Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Atmospheric Processes and Dynamics | Climate Change Models | Natural Hazards in Urban and Industrial Environments | Coastal and Estuarine Water Management | Water Services and Utilities
Related Links
Publications
Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia
Publisher: Springer Science and Business Media LLC
Date: 11-03-2018
A regional climate modelling projection ensemble experiment – NARCliM
Publisher: Copernicus GmbH
Date: 25-09-2013
Abstract: Abstract. Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensembles members that can be simulated such that choices must be made concerning which Global Climate Models (GCMs) to downscale from, and which Regional Climate Models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to s le the uncertainty in both GCMs and RCMs, as well as spanning the range of future climate projections present in the full GCM ensemble. The created ensemble provides a more robust view of future regional climate changes.
Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/563629
Abstract: This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.
Projected change in characteristics of near surface temperature inversions for southeast Australia
Publisher: Springer Science and Business Media LLC
Date: 08-05-2019
Optimally choosing small ensemble members to produce robust climate simulations
Publisher: IOP Publishing
Date: 12-2013
Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia
Publisher: IOP Publishing
Date: 08-2020
Abstract: Probabilistic seasonal rainfall forecasting is of great importance for stakeholders such as farmers and policymakers to assist in developing risk management strategies and to inform decisions. In practice, there are two kinds of commonly used tools, dynamical models and statistical models, to provide probabilistic seasonal rainfall forecasts. Dynamical models are based on physical processes but are usually expensive to operate and implement, and rely overly on initial conditions. Statistical models are easy to implement but are usually based on simple or linear relationships between observed variables. Recently, machine learning techniques have been widely used in climate projection and perform well in reproducing historical climate. For these reasons, we conducted a case study in Australia by developing a machine learning-based probabilistic seasonal rainfall forecasting model using multiple large-scale climate indices from the Pacific, Indian and Southern Oceans. Rainfall probabilities of exceeding the climatological median for upcoming seasons from 2011 to 2018 were successively forecasted using multiple climate indices of precedent six months. The performance of the model was evaluated by comparing it with an officially used forecasting model, the SOI (Southern Oscillation Index) phase model (SP) operated by Queensland government in Australia. Results indicated that the random forest (RF) model outperformed the SP model in terms of both distinct forecasts and forecasting accuracy. The RF model increased the percentages of distinct forecasts to 64.9% for spring, to 71.5% for summer, to 65.8% for autumn, and to 63.9% for winter, 1.4 ∼ 3.2 times of the values from the SP model. Forecasting accuracy was also greatly increased by 28%, 167%, 219%, and 76% for four seasons respectively, compared to the SP model. The proposed rainfall forecasting model is based on readily available data, and we believe it can be easily extended to other regions to provide seasonal rainfall outlooks.
Projected changes in east Australian midlatitude cyclones during the 21st century
Publisher: American Geophysical Union (AGU)
Date: 06-01-2016
DOI: 10.1002/2015GL067267
Evaluation of long-term precipitation and temperature WRF simulations for southeast Australia
Publisher: Inter-Research Science Center
Date: 24-02-2016
DOI: 10.3354/CR01366
Impact of Higher Spatial Resolution on Precipitation Properties Over Australia
Publisher: American Geophysical Union (AGU)
Date: 12-12-2022
DOI: 10.1029/2022GL100717
Abstract: Coarse resolution datasets often poorly capture precipitation properties. It is commonly expected that skill at the local level should increase by refining spatial resolution. Here, we examine the influence of spatial resolution on the accuracy of means and extremes in precipitation by comparing high‐resolution dynamically downscaled data against the driving lower‐resolution reanalysis. We show that the higher‐resolution data are less accurate than lower‐resolution data for both mean and extreme precipitation. The quantitative deterioration (increase in the domain averaged relative error) from coarse to high resolution varies typically between 1%–8% and 5%–30% for mean and extreme precipitation, respectively. We also find that the finer‐scale variability resolved only by the higher‐resolution system and successfully predicted by that system is of negligible magnitude compared to the overall error (less than 1%). We conclude that finer‐scale resolution by itself does not necessarily bring a meaningful improvement in local simulation accuracy.
Biases in Estimating Long‐Term Recurrence Intervals of Extreme Events Due To Regionalized Sampling
Publisher: American Geophysical Union (AGU)
Date: 07-08-2023
DOI: 10.1029/2023GL105286
Abstract: Preparing for environmental risks requires estimating the frequencies of extreme events, often from data records that are too short to confirm them directly. This requires fitting a statistical distribution to the data. To improve precision, investigators often pool data from neighboring sites into single s les, referred to as “superstations,” before fitting. We demonstrate that this technique can introduce unexpected biases in typical situations, using wind and rainfall extremes as case studies. When the combined locations have even small differences in the underlying statistics, the regionalization approach gives a fit that may tend toward the highest levels suggested by any of the in idual sites. This bias may be large or small compared to the s ling error, for realistic record lengths, depending on the distribution of the quantity analyzed. The results of this analysis indicate that previous analyses could potentially have overestimated the likelihood of extreme events arising from natural weather variability.
Australian snowpack in the NARCliM ensemble: evaluation, bias correction and future projections
Publisher: Springer Science and Business Media LLC
Date: 19-10-2017
Extreme rainfall, rainfall erosivity, and hillslope erosion in Australian Alpine region and their future changes
Publisher: Wiley
Date: 02-09-2020
DOI: 10.1002/JOC.6266
The role of topography on projected rainfall change in mid-latitude mountain regions
Publisher: Springer Science and Business Media LLC
Date: 22-03-2019
Introducing NARCliM1.5: Evaluation and projection of climate extremes for southeast Australia
Publisher: Elsevier BV
Date: 12-2022
Evaluating rainfall patterns using physics scheme ensembles from a regional atmospheric model
Publisher: Springer Science and Business Media LLC
Date: 23-04-2013
Modelling and evaluating the impacts of climate change on three major crops in south-eastern Australia using regional climate model simulations
Publisher: Springer Science and Business Media LLC
Date: 27-03-2019
Impact of identification method on the inferred characteristics and variability of Australian east coast lows
Publisher: American Meteorological Society
Date: 27-02-2015
Abstract: The Australian east coast low (ECL) is both a major cause of damaging severe weather and an important contributor to rainfall and dam inflow along the east coast, and is of interest to a wide range of groups including catchment managers and emergency services. For this reason, several studies in recent years have developed and interrogated databases of east coast lows using a variety of automated cyclone detection methods and identification criteria. This paper retunes each method so that all yield a similar event frequency within the ECL region, to enable a detailed intercomparison of the similarities, differences, and relative advantages of each method. All methods are shown to have substantial skill at identifying ECL events leading to major impacts or explosive development, but the choice of method significantly affects both the seasonal and interannual variation of detected ECL numbers. This must be taken into consideration in studies on trends or variability in ECLs, with a subcategorization of ECL events by synoptic situation of key importance.
Biases in estimating long-term recurrence intervals of extreme events due to regionalised sampling
Publisher: Authorea, Inc.
Date: 09-03-2023
DOI: 10.22541/ESSOAR.167839988.80634934/V1
Abstract: Preparing for environmental risks requires estimating the frequencies of extreme events, often from data records that are too short to confirm them directly. This requires fitting a statistical distribution to the data. To improve precision, investigators often pool data from neighboring sites into single s les, referred to as “superstations,” before fitting. We demonstrate that this technique can introduce unexpected biases in typical situations, using wind and rainfall extremes as case studies. When the combined locations have even small differences in the underlying statistics, the regionalization approach gives a fit that may tend toward the highest levels suggested by any of the in idual sites. This bias may be large or small compared to the s ling error, for realistic record lengths, depending on the distribution of the quantity analysed. The results of this analysis indicate that previous analyses could potentially have overestimated the likelihood of extreme events arising from natural weather variability.
Extreme precipitation in WRF during the Newcastle East Coast Low of 2007
Publisher: Springer Science and Business Media LLC
Date: 28-07-2016
Evaluating the performance of a WRF physics ensemble over South-East Australia
Publisher: Springer Science and Business Media LLC
Date: 19-11-2011
The implication of spatial interpolated climate data on biophysical modelling in agricultural systems
Publisher: Wiley
Date: 17-12-2020
DOI: 10.1002/JOC.6371
Trends and low frequency variability of East Coast Lows in the twentieth century
Publisher: CSIRO Publishing
Date: 31-12-2018
DOI: 10.22499/3.6801.001
Using large-scale climate drivers to forecast meteorological drought condition in growing season across the Australian wheatbelt
Publisher: Elsevier BV
Date: 07-2020
Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia
Publisher: Wiley
Date: 27-07-2018
DOI: 10.1002/JOC.5705
Outgoing longwave radiation and cloud radiative forcing of the Tibetan Plateau
Publisher: American Geophysical Union (AGU)
Date: 06-2000
DOI: 10.1029/2000JD900201
Using large-scale diagnostic quantities to investigate change in East Coast Lows
Publisher: Springer Science and Business Media LLC
Date: 23-01-2015
Projected changes in vertical temperature profiles for Australasia
Publisher: Springer Science and Business Media LLC
Date: 30-07-2020
Propagation of climate model biases to biophysical modelling can complicate assessments of climate change impact in agricultural systems
Publisher: Wiley
Date: 29-08-2019
DOI: 10.1002/JOC.5820
Related Organisations
NSW Department Of Planning, Industry And Environment
Location: Australia
Related Funding Activities
Linkage Projects - Grant ID: LP200100138
Start Date: 09-2021
End Date: 08-2024
Amount: $362,500.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP120200777
Start Date: 2013
End Date: 12-2015
Amount: $240,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP120200494
Start Date: 05-2013
End Date: 04-2017
Amount: $300,000.00
Funder: Australian Research Council
View Funded Activity