High-quality spatial climate data-sets for Australia

Jones, David A.; Wang, William Yang; Fawcett, Robert

doi:10.22499/2.5804.003

Cited by 1,204 publications

(1,075 citation statements)

References 17 publications

(40 reference statements)

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“…We employ monthly rainfall dataset from the Australian Water Availability Project (AWAP) on a 0.25° × 0.25° horizontal grid (Jones et al 2009;Raupach et al 2009). In addition, the state-averaged rainfall time series in austral spring (September-November, SON) in four states of the Northern Territory, Queensland, New South Wales and Victoria (see Fig.…”

Section: Methodsmentioning

confidence: 99%

The influence of boreal winter extratropical North Pacific Oscillation on Australian spring rainfall

Song

Li²,

Duan

2015

Clim Dyn

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Section: Methodsmentioning

confidence: 99%

The influence of boreal winter extratropical North Pacific Oscillation on Australian spring rainfall

Song

Li²,

Duan

2015

Clim Dyn

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“…The h r was approximated in our model by a Taylor series expansion 41s((T b þ T rad )/2 þ 273.15) 3 with emissivity 1 (0.95) and Stefan-Boltzmann constant s. These models are now part of the NicheMapR biophysical modelling package (v. 1.0, https://github.com/mrke/NicheMapR/ releases) for R (v. 3.2.2; R Foundation for Statistical Computing) and the script implementing them for this study, 'bearded dragon field heating rate.R', can be found at https://github.com/mrke/ bearded-dragons.git. We used the NicheMapR microclimate model, integrated with a continent-wide daily weather database [46], which we queried for the study site for all days in the year 2013 to obtain hourly estimates of air temperature and wind rspb.royalsocietypublishing.org Proc. R. Soc.…”

Section: (E) Biophysical Simulations Of Heating Ratesmentioning

confidence: 99%

Colour change on different body regions provides thermal and signalling advantages in bearded dragon lizards

et al. 2016

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Many terrestrial ectotherms are capable of rapid colour change, yet it is unclear how these animals accommodate the multiple functions of colour, particularly camouflage, communication and thermoregulation, especially when functions require very different colours. Thermal benefits of colour change depend on an animal's absorptance of solar energy in both UV-visible (300-700 nm) and near-infrared (NIR; 700-2600 nm) wavelengths, yet colour research has focused almost exclusively on the former. Here, we show that wild-caught bearded dragon lizards (Pogona vitticeps) exhibit substantial UV-visible and NIR skin reflectance change in response to temperature for dorsal but not ventral (throat and upper chest) body regions. By contrast, lizards showed the greatest temperature-independent colour change on the beard and upper chest during social interactions and as a result of circadian colour change. Biophysical simulations of heat transfer predicted that the maximum temperature-dependent change in dorsal reflectivity could reduce the time taken to reach active body temperature by an average of 22 min per active day, saving 85 h of basking time throughout the activity season. Our results confirm that colour change may serve a thermoregulatory function, and competing thermoregulation and signalling requirements may be met by partitioning colour change to different body regions in different circumstances.

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“…3; Table 1). The inputs were monthly rainfall and air temperature (minimum, maximum and mean) values (Table 2) originally collated from on-site, historical observations (Jones et al, 2009;Lavery et al, 1997). Quality controls tests were already done to adjust the raw dataset for inhomogeneities caused by station relocations and adverse exposures using objective statistical methods (Torok and Nicholls, 1996) including detection and removal of gross single-day errors and metadata inhomogeneities.…”

Section: Climate Datamentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

267

102

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The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

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High-quality spatial climate data-sets for Australia

Cited by 1,204 publications

References 17 publications

The influence of boreal winter extratropical North Pacific Oscillation on Australian spring rainfall

The influence of boreal winter extratropical North Pacific Oscillation on Australian spring rainfall

Colour change on different body regions provides thermal and signalling advantages in bearded dragon lizards

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

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