气候变化领域集成服务门户(CCPortal): Near-real-time adjusted reanalysis forcing data for hydrology

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DOI	10.5194/hess-22-989-2018
	Near-real-time adjusted reanalysis forcing data for hydrology
	Berg P.; Donnelly C.; Gustafsson D.
发表日期	2018
ISSN	1027-5606
起始页码	989
结束页码	1000
卷号	22 期号:2
英文摘要	Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of high-quality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data–ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model. © 2018 Author(s).
语种	英语
scopus关键词	Forecasting; Climatological observations; Forecasting modeling; Hydrological forecast; Hydrological forecasting; Hydrological prediction; Hydrological simulations; Meteorological condition; Meteorological forcing; Digital storage; calibration; data set; forecasting method; hydrological modeling; hydrology; meteorology; numerical model; precipitation (climatology); simulation; temperature effect; Arctic; Europe
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160131
作者单位	Berg, P., Hydrology Research Unit, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, Norrköping, 601-76, Sweden; Donnelly, C., Hydrology Research Unit, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, Norrköping, 601-76, Sweden; Gustafsson, D., Hydrology Research Unit, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, Norrköping, 601-76, Sweden
推荐引用方式 GB/T 7714	Berg P.,Donnelly C.,Gustafsson D.. Near-real-time adjusted reanalysis forcing data for hydrology[J],2018,22(2).
APA	Berg P.,Donnelly C.,&Gustafsson D..(2018).Near-real-time adjusted reanalysis forcing data for hydrology.Hydrology and Earth System Sciences,22(2).
MLA	Berg P.,et al."Near-real-time adjusted reanalysis forcing data for hydrology".Hydrology and Earth System Sciences 22.2(2018).