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  • 标题:HydroGFD3.0 (Hydrological Global Forcing Data): a 25 km global precipitation and temperature data set updated in near-real time
  • 本地全文:下载
  • 作者:Peter Berg ; Fredrik Almén ; Denica Bozhinova
  • 期刊名称:Earth System Science Data (ESSD)
  • 印刷版ISSN:1866-3508
  • 电子版ISSN:1866-3516
  • 出版年度:2021
  • 卷号:13
  • 期号:4
  • 页码:1531-1545
  • DOI:10.5194/essd-13-1531-2021
  • 出版社:Copernicus
  • 摘要:Abstract. HydroGFD3 (Hydrological Global Forcing Data) is a data set of bias-adjusted reanalysis data for daily precipitation and minimum, mean, and maximum temperature. It is mainly intended for large-scale hydrological modelling but is also suitable for other impact modelling. The data set has an almost global land area coverage, excluding the Antarctic continent and small islands, at a horizontal resolution of 0.25∘, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period, currently 1979 until 5 d ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number of wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly such that no additional input data about the number of wet days are necessary. The daily mean temperature is adjusted toward the monthly mean of the observations and applied to 1 h time steps of the ERA5 reanalysis. Daily mean, minimum, and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access the data set at https://doi.org/10.5281/zenodo.3871707 (Berg et al., 2020).
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