@article {29247,
	title = {A sequential assimilation strategy to improve short-and long-term forecasts in a hydropower application.},
	journal = {Geophysical Research Abstracts},
	volume = {21},
	year = {2019},
	abstract = {<p>
	Hydropower producers rely on hydrologic models and reliable forecasts of incoming water volumes to operate power plants and dams efficiently. These models are parameterized and, as such, are mere approximations of the real hydrologic system. Hence, initial states of the model might not always match the current states of the system after more than a few days of open-loop simulations. An analyst therefore usually adjusts model states or input variables such that the model meets the current states of the system. The automatized version of this process is called data assimilation. The short-term impacts of such adjustments are mostly predictable while long-term effects are harder to foresee. The performance of the assimilation is measured by comparing the improvement of forecasts compared to an open-loop simulation where no adjustment is applied. We will present a new strategy to update states of the\&nbsp;{\textellipsis}
</p>
},
	author = {J. Mai and R. Arsenault and B. Tolson and M. Latraverse and K. Demeester}
}
