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

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 resealing 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-Pbased soil moisture time series for seasonal and sub seasonal (anomaly) components, respectively on optimal model-Pobservation integration are investigated. Experiments are performed using both well-Pcontrolled synthetic and real data test beds. Investigated experiments are based on resealing observations to a model using strategies with decreasing aggressiveness: 1) using the seasonality of the model directly while matching the variance of the observed anomaly component, 2) resealing the seasonality and the anomaly components separately, and 3) resealing the entire time series as one piece or for each monthly climatology. All experiments use a simple antecedent precipitation index model and assimilate observations via a Kalman filtering approach. Synthetic and real data assimilation results demonstrate that resealing observations more aggressively to the model is favorable when the model is more skillful than observations; however, resealing observations more aggressively to the model can degrade the Kalman filter analysis if observations are relatively more accurate.


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Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and orthogonality with respect to the truth) are required for TCA estimates to be unbiased. Even though soil moisture studies commonly acknowledge that these assumptions are required for an unbiased TCA, no study has sp...
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This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the Regional Atmospheric Modeling System (RAMS) provides an improved regional diagnosis of downward short- and longwave surface radiation fluxes and precipitation. An automatic procedure was developed to derive high-resolution (4 km 3 4 km) fields of fractional cloud cover from visible band Geostationary Operational Environmental Satellite (GOES) data using a tracking procedure to determine the clear-sk...
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It is well known that systematic differences exist between modeled and observed realizations of hydrological variables like soil moisture. Prior to data assimilation, these differences must be removed in order to obtain an optimal analysis. A number of rescaling approaches have been proposed for this purpose. These methods include rescaling techniques based on matching sampled temporal statistics, minimizing the least squares distance between observations and models, and the application of triple collocatio...
Effects of implementing MODIS land cover and albedo in MM5 at two contrasting US regions
Yücel, İsmail (American Meteorological Society, 2006-10-01)
This study implements a new land-cover classification and surface albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS) in the fifth-generation Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) Mesoscale Model (MM5) and investigates its effects on regional near-surface atmospheric state variables as well as the planetary boundary layer evolution for two dissimilar U.S. regions. Surface parameter datasets are determined by translating the 17-category MODIS clas...
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In this study, the hierarchical clustering technique, called Ward method, was applied for grouping common features of air temperature series, precipitation total and relative humidity series of 244 stations in Turkey. Results of clustering exhibited the impact of physical geographical features of Turkey, such as topography, orography, land-sea distribution and the high Anatolian peninsula on the geographical variability. Based on the monthly series of nine climatological observations recorded for the period...
Citation Formats
M. T. Yılmaz and D. Ryu, “Impact of Model Relative Accuracy in Framework of Resealing Observations in Hydrological Data Assimilation Studies,” JOURNAL OF HYDROMETEOROLOGY, pp. 2245–2257, 2016, Accessed: 00, 2020. [Online]. Available: