edu.ucar.opensky::articles:19585 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-26T02:36:09.623584 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7mp552g https://doi.org/10.5065/D6MW2F4D https Related Dataset #1 A large-sample watershed-scale hydrometeorological dataset for the contiguous USA https://doi.org/10.5065/D6TH8JR2 https Related Dataset #2 Gridded Ensemble Precipitation and Temperature Estimates over the Contiguous United States Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States 2017-01-31T00:00:00Z publication https://n2t.org/ark:/85065/d7mp552g https://doi.org/10.5194/hess-21-635-2017 Huang, Chengcheng author Newman, Andrew J. author Clark, Martyn P. author Wood, Andrew author Zheng, X. author UCAR/NCAR - Library publisher In this study, we examine the potential of snow water equivalent data assimilation (DA) using the ensemble Kalman filter (EnKF) to improve seasonal streamflow predictions. There are several goals of this study. First, we aim to examine some empirical aspects of the EnKF, namely the observational uncertainty estimates and the observation transformation operator. Second, we use a newly created ensemble forcing dataset to develop ensemble model states that provide an estimate of model state uncertainty. Third, we examine the impact of varying the observation and model state uncertainty on forecast skill. We use basins from the Pacific Northwest, Rocky Mountains, and California in the western United States with the coupled Snow-17 and Sacramento Soil Moisture Accounting (SAC-SMA) models. We find that most EnKF implementation variations result in improved streamflow prediction, but the methodological choices in the examined components impact predictive performance in a non-uniform way across the basins. Finally, basins with relatively higher calibrated model performance (> 0.80 NSE) without DA generally have lesser improvement with DA, while basins with poorer historical model performance show greater improvements. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License None eng geoscientificInformation