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DOI	10.5194/hess-23-3823-2019
	Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts
	Laura Poletti M.; Silvestro F.; Davolio S.; Pignone F.; Rebora N.
发表日期	2019
ISSN	1027-5606
起始页码	3823
结束页码	3841
卷号	23 期号:9
英文摘要	Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12-24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2-8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field. © 2019 SPIE. All rights reserved.
语种	英语
scopus关键词	Blending; Catchments; Climate models; Digital storage; Floods; Rain; Runoff; Stream flow; Discharge predictions; Distributed approaches; Distributed hydrological model; High resolution numerical weather predictions; Hydrological forecast; Hydrological modeling; Meteorological modeling; Streamflow prediction; Weather forecasting; catchment; data assimilation; discharge; hydrological modeling; performance assessment; rainfall; streamflow; weather forecasting; Italy; Liguria
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/159810
作者单位	Laura Poletti, M., CIMA Research Foundation, Savona, Italy; Silvestro, F., CIMA Research Foundation, Savona, Italy; Davolio, S., Institute of Atmospheric Sciences and Climate, CNR ISAC, Bologna, Italy; Pignone, F., CIMA Research Foundation, Savona, Italy; Rebora, N., CIMA Research Foundation, Savona, Italy
推荐引用方式 GB/T 7714	Laura Poletti M.,Silvestro F.,Davolio S.,et al. Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts[J],2019,23(9).
APA	Laura Poletti M.,Silvestro F.,Davolio S.,Pignone F.,&Rebora N..(2019).Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts.Hydrology and Earth System Sciences,23(9).
MLA	Laura Poletti M.,et al."Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts".Hydrology and Earth System Sciences 23.9(2019).