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)
datasetposted on 09.06.2021, 11:01 authored by David McInerneyDavid McInerney, Mark ThyerMark Thyer, Dmitri KavetskiDmitri Kavetski
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).
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.