Journal article
Impact of model relative accuracy in framework of rescaling observations in hydrological data assimilation studies
MT Yilmaz, WT Crow, D Ryu
Journal of Hydrometeorology | AMER METEOROLOGICAL SOC | Published : 2016
Abstract
Soil moisture datasets vary greatly with respect to their time series variability and signal-to-noise characteristics. Minimizing differences in signal variances is particularly important in data assimilation to optimize the accuracy of the analysis obtained after merging model and observation datasets. Strategies that reduce these differences are typically based on rescaling the observation time series to match the model. As a result, the impact of the relative accuracy of the model reference dataset is often neglected. In this study, the impacts of the relative accuracies of model- and observation-based soil moisture time series-for seasonal and subseasonal (anomaly) components, respective..
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Awarded by EU
Awarded by Scientific and Technological Research Council of Turkey (TUBITAK)
Funding Acknowledgements
We thank Michael Cosh (michael.cosh@ars.usda.gov) of the U.S. Department of Agriculture for USDA ARS watershed soil moisture data sets, Robert Parinussa (r.m.parinussa@vu.nl) of Vrije Universiteit Amsterdam for LPRM datasets, and NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) for Noah and TRMM datasets (http://disc.sci.gsfc.nasa.gov/). These data can be acquired from their respective sources. We also thank the reviewers for their constructive comments which improved the presentation of the paper. Research was supported by EU Marie Curie Seventh Framework Programme FP7-PEOPLE-2013-CIG project number 630110 (principal investigator, M. Tugrul Yilmaz) and the Scientific and Technological Research Council of Turkey (TUBITAK) Grant 3501, Project 114Y676 (principal investigator, M. Tugrul Yilmaz).