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)

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).<br>This dataset was produced by the Australian Bureau of Meteorology. <br><br>Rainfall forecasts are produced using the Australian Community Climate Earth-System Simulator - Seasonal (ACCESS-S Version 1) (Hudson et al., 2017).<br>The ACCESS-S forecasts are then post-processed to reduce biases and improve reliability (Schepen et al., 2018).<br><br>References<br>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. &amp; 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.<br>McInerney, D., Thyer, M., Kavetski, D., Laugesen, R.,Woldemeskel, F., Tuteja, N. &amp; Kuczera, G. Improving the reliability of short-term forecasts of high and low flows by using a flow-dependent non-parametric model (under review).<br>Schepen, A., Zhao, T., Wang, Q. J. &amp; 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.<br>