Reducing water imbalance in land data assimilation: Ensemble filtering without perturbed observations

Yılmaz, Mustafa Tuğrul
Yilmaz, M. Tugrul
It is well known that the ensemble Kalman filter (EnKF) requires updating each ensemble member with perturbed observations in order to produce the proper analysis-error covariances. While increased accuracy in a mean square sense may be preferable in many applications, less accuracy might be preferable in other applications, especially if the variables being assimilated obey certain conservation laws. In land data assimilation, for instance, the update in soil moisture often produces a water balance residual, in the sense that the input water is not equal to output water. This study shows that suppressing the perturbation of observations in the EnKF and in the weakly constrained ensemble Kalman filter significantly improves the water balance residuals without significantly increasing the state errors.
Journal of Hydrometeorology


Improving land data assimilation performance with a water budget constraint
Yılmaz, Mustafa Tuğrul; Houser, Paul R. (American Meteorological Society, 2011-10-01)
A weak constraint is introduced in ensemble Kalman filters to reduce the water budget imbalance that occurs in land data assimilation. Two versions of the weakly constrained filter, called the weakly constrained ensemble Kalman filter (WCEnKF) and the weakly constrained ensemble transform Kalman filter (WCETKF), are proposed. The strength of the weak constraint is adaptive in the sense that it depends on the statistical characteristics of the forecast ensemble. The resulting filters are applied to assimilat...
Evaluation of Assumptions in Soil Moisture Triple Collocation Analysis
Yılmaz, Mustafa Tuğrul (American Meteorological Society, 2014-06-01)
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...
Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios
OKKAN, UMUT; İnan, Gül (Wiley, 2015-09-01)
In this study, statistical downscaling of general circulation model (GCM) simulations to monthly inflows of Kemer Dam in Turkey under A1B, A2, and B1 emission scenarios has been performed using machine learning methods, multi-model ensemble and bias correction approaches. Principal component analysis (PCA) has been used to reduce the dimension of potential predictors of National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. Then, the reasonabl...
Optimally merging precipitation to minimize land surface modeling errors
Yılmaz, Mustafa Tuğrul; Shrestha, Roshan; Anantharaj, Valentine G. (American Meteorological Society, 2010-03-01)
This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minim...
Impact of Model Relative Accuracy in Framework of Resealing Observations in Hydrological Data Assimilation Studies
Yılmaz, Mustafa Tuğrul; Ryu, D. (American Meteorological Society, 2016-08-01)
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 of...
Citation Formats
M. T. Yılmaz and M. T. Yilmaz, “Reducing water imbalance in land data assimilation: Ensemble filtering without perturbed observations,” Journal of Hydrometeorology, pp. 413–420, 2012, Accessed: 00, 2020. [Online]. Available: