Forecasting rainfall based on the Southern Oscillation Index phases at longer lead-times in Australia

Cobon, D. H.; Toombs, Nathan

doi:10.1071/rj12105

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…General circulation models, however, do not generally perform well at forecasting rainfall, despite substantial efforts to enhance performance over many years [8][9][10]. Various statistical models continue to be developed for rainfall prediction in Australia, generally using climate indices as inputs [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Improving monthly rainfall forecasts using artificial neural networks and single-month optimisation: a case study of the Brisbane catchment, Queensland, Australia

Abbot¹,

Marohasy²

2015

WIT Transactions on Ecology and the Environment

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Official medium-term rainfall forecasts failed to warn of the impending heavy rainfall in the Brisbane catchment during the summer of 2010-2011 with resulting catastrophic flooding causing loss of life, extensive property damage and disruption of economic activity in southeastern Queensland, Australia. Since the flooding, the Australia Bureau of Meteorology has changed its method of forecasting from an empirical statistical scheme to the use of a general circulation model, the Predictive Ocean and Atmospheric Model for Australia (POAMA). More skilful forecasts, however, can be achieved through the use of an altogether different technique involving artificial neural networks (ANN), a form of machine learning. Building on previous studies comparing the skill of the ANN forecasts for Gatton and Harrisville in the Brisbane catchment with output from POAMA, this study shows how monthly ANN rainfall forecasts can be further significantly improved through a technique referred to as "single-month optimisation". This technique enables the temporal variability in the influence of key climate indices to be better incorporated into the rainfall forecast. In particular, correlation coefficients exceeding 0.85 at a 12 month lead time can be achieved for both Harrisville and Gatton.

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Section: Introductionmentioning

confidence: 99%

Improving monthly rainfall forecasts using artificial neural networks and single-month optimisation: a case study of the Brisbane catchment, Queensland, Australia

Abbot¹,

Marohasy²

2015

WIT Transactions on Ecology and the Environment

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“…It is also apparent that there is no simple method to ascertain what lag period for the particular indices selected should be included to forecast a specific lead time, for any one location [11]. Assessment of the many studies undertaken over recent decades [6][7][8][9][10][11][33][34][35], indicates that the problem of pre-selecting optimal inputs is intrinsically difficult, and that forecast solutions are not amenable to the application of simple formulas incorporating sparse information.…”

Section: Resultsmentioning

confidence: 99%

Forecasting of monthly rainfall in the Murray Darling Basin, Australia: Miles as a case study

Abbot¹,

Marohasy²

2015

WIT Transactions on Ecology and the Environment

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The Murray Darling Basin accounts for nearly 40% of the value of agricultural production in Australia, and 65% of the irrigated land. We use an artificial neural network (ANN), a form of machine learning, to show the potential for more reliable monthly rainfall forecasts with a lead time of 3 months, and the potential skill of the same model for 6, 9, 12 and 18 month lead-times for the township of Miles, in the northern Basin. The skill of these forecasts is contrasted with the skill of the Predictive Ocean Atmosphere Model for Australia (POAMA), a general circulation model used for operational forecasts by the Australian Bureau of Meteorology. Forecasts from the ANN are significantly more skilful for all lead times. The ANN's capacity to integrate information from the climate indices Niño 1.2, 3, 3.4 and 4, the Dipole Mode Index (DMI), and also a composite local maximum and minimum atmospheric temperature series, contributes on average approximately 60% of the skill of the ANN forecast.

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“…Many studies have recognised the link between ENSO and yearto-year rainfall variability in Queensland (e.g. Pittock 1975;McBride and Nicholls 1983;Allan 1985;Risbey et al 2009;Cobon and Toombs 2013). As well as ENSO, there are several major remote climate drivers also affecting Australian rainfall (Risbey et al 2009, p. 3250), such as blocking, Southern Annular Mode (SAM), and the Indian Ocean Dipole (IOD).…”

Section: Relationship Between Multi-year Wet and Dry Periods And Ensomentioning

confidence: 99%

“…9-13 year timescale) should be seen as an additional source of climatic risk in the same way that ENSO has been recognised and considered in grazing management decisions (e.g. McIntosh et al 2005;Marshall 2008;Cobon and Toombs 2013;Partridge 2017).…”

Section: Relationships Of Decadal and Inter-decadal Variability With Wet And Dry Periodsmentioning

confidence: 99%

Queensland’s multi-year Wet and Dry periods: implications for grazing enterprises and pasture resources

et al. 2021

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Year-to-year variability in rainfall has long been recognised as a major issue in managing livestock enterprises across Australia's grazing lands. Extension products documenting rainfall variability have been developed over the last 30 years and have been keenly sought by producers and their advisors. This paper describes multi-year rainfall variability from 1889 to 2020 and provides the basis for classifying the 131 years of rainfall into 18 discrete Wet (7), Average (2) and Dry (9) periods as presented in the 'Queensland's Extended Wet and Dry Periods' poster. The classification was consistent with: analysis of fluctuations and trends in the long-term time series of reported livestock numbers; drought declarations for government assistance; and documented periods of pasture resource degradation and recovery. Rainfall during the nine Wet and Average periods was þ18% above the long-term average annual rainfall (LTAAR), in contrast to the Dry periods with À17% below LTAAR. Wet periods (including Average) were on average 7 years in duration, ranging from 5 to 9 years. Dry periods were on average 8 years in duration and ranged from 5 to 13 years. Detailed analysis of the effects of the El Niño Southern Oscillation (ENSO) phenomenon indicated that: (a) the Wet/Dry periods were dominated by different frequencies and amounts of rainfall in La Niña/El Niño years; (b) rainfall in ENSO neutral years was generally above and below average rainfall for the Wet or Dry periods respectively; (c) the frequency of ENSO year-types was less important than the overall rainfall surplus (or deficit) in La Niña (or El Niño) years within the Wet (or Dry) periods respectively; and (d) the timing of Wet and Dry periods was correlated with indices of quasi-decadal and inter-decadal variability in components (sea surface temperatures and atmospheric pressures) of the global climate system. Climatic risk assessment systems for grazing management at multi-year timescales are yet to be developed.

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Forecasting rainfall based on the Southern Oscillation Index phases at longer lead-times in Australia

Cited by 18 publications

References 29 publications

Improving monthly rainfall forecasts using artificial neural networks and single-month optimisation: a case study of the Brisbane catchment, Queensland, Australia

Improving monthly rainfall forecasts using artificial neural networks and single-month optimisation: a case study of the Brisbane catchment, Queensland, Australia

Forecasting of monthly rainfall in the Murray Darling Basin, Australia: Miles as a case study

Queensland’s multi-year Wet and Dry periods: implications for grazing enterprises and pasture resources

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