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Bias correction of daily precipitation over South Korea from the long-term reanalysis using a composite Gamma-Pareto distribution approach

机译:使用复合Gamma-Pareto分布方法从长期再分析中对韩国日降水量的偏差校正

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Long-term precipitation data plays an important role in climate impact studies, but the observation for a given catchment is very limited. To significantly expand our sample size for the extreme rainfall analysis, we considered ERA-20c, a century-long reanalysis daily precipitation provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Preliminary studies have already indicated that ERA-20c can reproduce the mean reasonably well, but rainfall intensity is underestimated while wet-day frequency is overestimated. Thus, we first adopted a relatively simple approach to adjust the frequency of wet-days by imposing an optimal threshold. Moreover, we introduced a quantile mapping approach based on a composite distribution of a generalized Pareto distribution for the upper tail (e.g. 95th and 99th percentile), and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases over the conventional method for the extremes. We suggested an interpolation method for the set of parameters of bias correction approach in ungauged catchments. A comparison of the corrected precipitation using spatially interpolated parameters shows that the proposed modelling scheme, particularly with the 99th percentile, can reliably reduce the systematic bias.
机译:长期降水数据在气候影响研究中起着重要作用,但是对给定集水区的观测非常有限。为了显着扩展极端降雨分析的样本量,我们考虑了ERA-20c,这是由欧洲中型天气预报中心(ECMWF)提供的长达一个世纪的再分析每日降水量。初步研究已经表明,ERA-20c可以很好地重现平均数,但是降雨强度被低估了,而湿日频率被高估了。因此,我们首先采用了一种相对简单的方法,通过施加最佳阈值来调整潮湿天气的频率。此外,我们基于上尾部的广义Pareto分布(例如95%和99%百分位数)和伽马分布内部分布的复合分布引入了分位数映射方法。与极端情况下的常规方法相比,建议的复合分布可显着降低偏差。我们提出了一种针对非流域汇流的偏差校正方法参数集的插值方法。使用空间插值参数对校正后的降水进行比较表明,所提出的建模方案(尤其是第99个百分位数)可以可靠地降低系统偏差。

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