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Using ERA-Interim reanalysis for creating datasets of?energy-relevant climate variables

机译:使用ERA-Interim重新分析创建与能源相关的气候变量的数据集

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The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6?h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.
机译:提出了使用ERA-Interim重新分析构建近地表气候变量的偏差调整数据集的方法。已经提出了许多不同的,依赖于变量的偏差调整方法。在这里,我们修改不同分布的参数(取决于变量),根据网格化站点或直接站点观测值调整ERA-Interim。这些变量是空气温度,露点温度,降水(仅每天),太阳辐射,风速和相对湿度。在1979-2016年期间,这些时间尺度的时间范围为3或6?h。可通过哥白尼气候变化数据存储(C3S)的气候数据存储(CDS)获得所得的偏差调整后的数据集,目前可从ftp://ecem.climate.copernicus.eu访问。通过将初始和偏差调整后的ERA-Interim数据与网格观测场进行比较,可以证明执行偏差调整的好处。

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