Abstract
Abstract. Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.
Highlights
Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales (Bennett et al, 2016; Crochemore et al, 2017; Wang et al, 2011)
We have developed a novel method for post-processing daily rainfall amounts from seasonal forecasting global climate models (GCMs)
Compared to raw forecasts and quantile mapping (QM) post-processing, Rainfall Post-Processing method for Seasonal forecasts (RPP-S) performs significantly better in terms of correcting bias, reliability and skill
Summary
Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales (Bennett et al, 2016; Crochemore et al, 2017; Wang et al, 2011). Inclusion of climate information in seasonal streamflow forecasts enhances streamflow predictability (Wood et al, 2016). One strategy for integrating climate information into hydrological models is to conditionally resample historical rainfall Ensemble rainfall forecasts from GCMs (global climate models) are attractive for hydrological prediction in that they forecast multiple seasons ahead and have a well-established spatial and temporal forecast structure. A major issue with GCM forecasts at subseasonal to seasonal timescales is that the forecasts are often biased and lacking in predictability of local climate It is necessary to post-process GCM rainfall forecasts using statistical or dynamical methods before they can be used in hydrological models (Yuan et al, 2015)
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