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首页> 外文期刊>Hydrology and Earth System Sciences Discussions >A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12?Australian catchments
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A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12?Australian catchments

机译：一种贝叶斯建模方法，用于对全球气候模型中的每日次季节到季节性降雨预报进行后处理，并评估12个澳大利亚流域

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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.
机译：降雨预报是亚季节到季节时间尺度上水文预报系统的组成部分。在季节预报中，全球气候模型（GCM）现在是降雨量预报的首选来源。但是，对于水文应用，GCM预测的不确定性散布经常有偏差且不可靠，因此在使用前需要进行校准。有先进的统计技术可用于校准预测的每月和季节性汇总。但是，在每日时间步长校准季节性预报通常使用非常简单的统计方法或气候类似方法。这些方法通常缺乏对每日量和季节性累积总量进行无偏，可靠和一致的预测的技巧。在这项研究中，我们提出并评估了用于季节性预报的降雨后处理方法（RPP-S），该方法基于用于校准每日预报的贝叶斯联合概率建模方法和用于连接不同日集合成员的Schaake Shuffle的方法。交货时间。我们将该方法应用于澳大利亚12个常年和临时流域以及12个初始化日期的ACCESS-S预测后处理。 RPP-S大大减少了原始预测中的偏差，并提高了技能和可靠性。与从ACCESS-S预测得出的预测相比，RPP-S预测也更熟练和可靠，ACCESS-S预测已使用分位数映射进行了后处理，尤其是对于月度和季节累积。确定了一些提高RPP-S的鲁棒性和技能的机会。新的RPP-S后处理预报将用于整体次季节到季节性流量应用。

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