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首页> 外文期刊>Regional Environmental Change >Reducing bias-corrected precipitation projection uncertainties: a Bayesian-based indicator-weighting approach
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Reducing bias-corrected precipitation projection uncertainties: a Bayesian-based indicator-weighting approach

机译：减少偏差校正的降水预测的不确定性：基于贝叶斯的指标加权方法

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In this paper, a Bayesian-based indicator-weighting approach is developed to reduce the uncertainty resulting from bias-correcting projection outputs from multiple general circulations models (GCMs). The approach decides whether or not a projection from a given GCM output should be used depending on how close output from the GCM's retrospective run was to past observation (bias criterion) and agrees with the consensus (convergence criterion) estimate of all future GCM projections in a "truth-centered" statistical framework. Indicator weights are derived by equating present day versus future changes in mean precipitation of individual GCM output to the one obtained from the posterior distribution of all GCMs using a Markov Chain Monte Carlo algorithm. Use of GCMs outputs in hydrological impact studies requires downscaling and/or bias correction steps in order to account for discrepancies between small and large scale land-atmospheric processes. One of the most popular techniques for bias-correcting retrospective GCM outputs is the cumulative distribution functions matching approach based on observed precipitation. Future GCM projections are then adjusted depending on the bias correction results of retrospective outputs. In this sense, the bias correction process introduces variability/uncertainty into GCM outputs resulting in a wide range of projected values. If more than one GCM is used, the range of variability/uncertainty further increases. The approach that is used to reduce this uncertainty is demonstrated using 23 GCM outputs of CMIP5 model runs for west central Florida.
机译：在本文中，开发了一种基于贝叶斯的指标加权方法，以减少由于对多个通用循环模型（GCM）的投影输出进行偏差校正而产生的不确定性。该方法决定是否应使用GCM既定输出的预测，具体取决于GCM回顾性运行的输出与过去的观察结果之间的接近程度（偏见标准），并与所有未来GCM预测中的共识估计（收敛标准）一致。一个“以事实为中心”的统计框架。指标权重是通过使用马尔可夫链蒙特卡洛算法将各个GCM输出的平均降水的当前变化与未来变化等价于从所有GCM的后验分布获得的平均降水得出的。在水文影响研究中使用GCM输出需要缩小规模和/或进行偏差校正步骤，以解决小规模和大规模陆地大气过程之间的差异。偏差校正追溯GCM输出的最受欢迎的技术之一是基于观测降水的累积分布函数匹配方法。然后根据追溯输出的偏差校正结果调整未来的GCM预测。从这个意义上讲，偏差校正过程将可变性/不确定性引入到GCM输出中，从而产生了很大范围的投影值。如果使用多个GCM，则可变性/不确定性的范围会进一步增加。通过在佛罗里达州中西部地区运行的CMIP5模型的23个GCM输出，证明了用于减少这种不确定性的方法。

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