Data

Supporting data for "Improving the reliability of sub-seasonal forecasts of high and low flows by using a flow-dependent non-parametric model" by McInerney et al. (2021)

Adelaide University
McInerney, David ; Thyer, Mark ; Kavetski, Dmitri
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25909/14604180.v1&rft.title=Supporting data for Improving the reliability of sub-seasonal forecasts of high and low flows by using a flow-dependent non-parametric model by McInerney et al. (2021)&rft.identifier=10.25909/14604180.v1&rft.publisher=The University of Adelaide&rft.description=This dataset contains post-processed rainfall forecast (hincast) data used in the study Improving the reliability of sub-seasonal forecasts of high and low flows by using a flow-dependent non-parametric model by McInerney et al. (2021).This dataset was produced by the Australian Bureau of Meteorology. Rainfall forecasts are produced using the Australian Community Climate Earth-System Simulator - Seasonal (ACCESS-S Version 1) (Hudson et al., 2017).The ACCESS-S forecasts are then post-processed to reduce biases and improve reliability (Schepen et al., 2018).ReferencesHudson, D., Alves, O., Hendon, H. H., Lim, E., Liu, G., Luo, J. J., MacLachlan, C., Marshall, A. G., Shi, L., Wang, G., Wedd, R., Young, G., Zhao, M. & Zhou, X. 2017. ACCESS-S1 The new Bureau of Meteorology multi-week to seasonal prediction system. Journal of Southern Hemisphere Earth System Sciences, 67, 132-159.McInerney, D., Thyer, M., Kavetski, D., Laugesen, R.,Woldemeskel, F., Tuteja, N. & Kuczera, G. Improving the reliability of short-term forecasts of high and low flows by using a flow-dependent non-parametric model (under review).Schepen, A., Zhao, T., Wang, Q. J. & Robertson, D. E. 2018. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments. Hydrol. Earth Syst. Sci., 22, 1615-1628.&rft.creator=McInerney, David &rft.creator=Thyer, Mark &rft.creator=Kavetski, Dmitri &rft.edition=1&rft_rights= https://creativecommons.org/licenses/by/4.0/&rft_subject=Hydrology not elsewhere classified&rft_subject=Water resources engineering&rft_subject=Climatology&rft_subject=ACCESS-S&rft_subject=sub-seasonal forecasting&rft_subject=rainfall post-processing&rft_subject=forecast rainfall&rft_subject=Hydrology&rft_subject=Water Resources Engineering&rft_subject=Climate Science&rft.type=dataset&rft.language=English Access the data
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Full description

This dataset contains post-processed rainfall forecast (hincast) data used in the study "Improving the reliability of sub-seasonal forecasts of high and low flows by using a flow-dependent non-parametric model" by McInerney et al. (2021).

This dataset was produced by the Australian Bureau of Meteorology.

Rainfall forecasts are produced using the Australian Community Climate Earth-System Simulator - Seasonal (ACCESS-S Version 1) (Hudson et al., 2017).

The ACCESS-S forecasts are then post-processed to reduce biases and improve reliability (Schepen et al., 2018).

References

Hudson, D., Alves, O., Hendon, H. H., Lim, E., Liu, G., Luo, J. J., MacLachlan, C., Marshall, A. G., Shi, L., Wang, G., Wedd, R., Young, G., Zhao, M. & Zhou, X. 2017. ACCESS-S1 The new Bureau of Meteorology multi-week to seasonal prediction system. Journal of Southern Hemisphere Earth System Sciences, 67, 132-159.

McInerney, D., Thyer, M., Kavetski, D., Laugesen, R.,Woldemeskel, F., Tuteja, N. & Kuczera, G. Improving the reliability of short-term forecasts of high and low flows by using a flow-dependent non-parametric model (under review).

Schepen, A., Zhao, T., Wang, Q. J. & Robertson, D. E. 2018. A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments. Hydrol. Earth Syst. Sci., 22, 1615-1628.

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Subjects
ACCESS-S | Climate Science | Climatology | Hydrology | Hydrology not elsewhere classified | Water Resources Engineering | Water resources engineering | forecast rainfall | rainfall post-processing | sub-seasonal forecasting |

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Identifiers
ACN 633 798 857