An extension of data assimilation into the short-term hydrologic forecast for improved prediction reliability
An extension of data assimilation into the short-term hydrologic forecast for improved prediction reliability
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DOI：
10.1016/j.advwatres.2019.103443
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发表时间：
2019-12
影响因子：
4.7
通讯作者：
James M. Leach;P. Coulibaly
中科院分区：
文献类型：
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作者：
James M. Leach;P. Coulibaly
通常，当使用数据同化来改善水文预报时，观测值被同化到预报的开始。这样做是为了提供更准确的状态和参数估计，这反过来又允许更好的预测。我们提出了一个扩展到传统的数据同化方法，允许同化继续到预测，以进一步提高预报的性能和可靠性。这种方法进行了测试，在加拿大南部的安大略，两个小的，高度城市化的流域，顿河和黑溪流域。使用强迫数据，模式状态，预测的径流，径流观测数据库，查找功能被用来提供一个观测在预测过程中，可以被同化。这允许一种间接的方式来同化数值天气预报强迫数据。这种方法可以帮助解决预测的不确定性，因为可以从数据库中提取与给定先前信息的预测概率密度函数相对应的先前观测的集合。结果表明，将数据同化扩展到预报中可以提高这些城市流域的预报性能，结果表明，预报可靠性可提高78%。
Typically, when using data assimilation to improve hydrologic forecasting, observations are assimilated up to the start of the forecast. This is done to provide more accurate state and parameter estimates which, in turn, allows for a better forecast. We propose an extension to the traditional data assimilation approach which allows for assimilation to continue into the forecast to further improve the forecast's performance and reliability. This method was tested on two small, highly urbanized basins in southern Ontario, Canada; the Don River and Black Creek basins. Using a database of forcing data, model states, predicted streamflow, and streamflow observations, a lookup function was used to provide an observation during the forecast which can be assimilated. This allows for an indirect way to assimilate the numerical weather prediction forcing data. This approach can help in addressing prediction uncertainty, since an ensemble of previous observations can be pulled from the database which correspond to the forecast probability density function given previous information. The results show that extending data assimilation into the forecast can improve forecast performance in these urban basins, and it was shown that the forecast reliability could be improved by up to 78%.