English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

AbstractSoil 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, respectively—on optimal model–observation integration are investigated. Experiments are performed using both well-controlled synthetic and real data test beds. Investigated experiments are based on rescaling observations to a model using strategies with decreasing aggressiveness: 1) using the seasonality of the model dir...

Impact of Model Relative Accuracy in Framework of Rescaling Observations in Hydrological Data Assimilation Studies