Inflation method for ensemble Kalman filter in soil hydrology

Bauser, Hannes H.; Berg, Daniel; Klein, Ole; Roth, Kurt

doi:10.5194/hess-22-4921-2018

Cited by 28 publications

(24 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A primary drawback of the EnKF is filter inbreeding caused by the underestimations of forecast errors. To overcome this, inflation techniques need to be applied (Zhang et al, 2017b;Bauser et al, 2018). For example, Zhang et al (2017b) employed the EnKF with a multiplicative inflation algorithm to estimate both parameters and state variables from two LSMs and reported improved model performance in the calibration and evaluation period.…”

Section: Introductionmentioning

confidence: 99%

Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau

Peng

Sun

2020

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

To provide scientific support for improvements in land surface modeling on the Tibetan Plateau (TP) by reducing uncertainties in the physical parameters of models, comprehensive uncertainty and sensitivity evaluations were performed for the simulation of surface soil moisture (SSM). Five observational stations were selected for the study. The conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach and the Common Land Model (CoLM) with 28 uncertain parameters were employed to evaluate the maximal uncertainty of the simulated SSM. The uncertainty analysis indicated that the parameter errors could induce large uncertainties. These uncertainties in the SSM generally fluctuated over the range from 0.33 to 0.64 m 3 m À3 in terms of absolute changes and from 235% to 510% in terms of percentage changes. The uncertainty analysis addressed the necessity of decreasing the uncertainties in the parameters. When resources are limited, the most important and sensitive parameter set should first be identified in order to reduce uncertainties. To find this parameter set, a sensitivity analysis framework based on the CNOP-P approach was applied. The results showed that the most sensitive and important combinations of 4 parameters changed slightly among the study sites and consisted of soil texture-related parameters. Although the most sensitive and important parameter combinations only had 4 elements, the uncertainties that they could induce accounted for a large proportion of the uncertainties caused by all 28 uncertain parameters. Furthermore, the decreases in parameter errors, which were derived from the CNOP-Ps of the most sensitive and important parameter combinations, led to the maximal reductions in the uncertainties of the simulated SSM. The above results imply that we should prioritize reducing the uncertainty of sensitive parameters or parameter combinations in order to improve prediction and simulation abilities.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau

Peng

Sun

2020

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

show abstract

“…The PF component of the proposed scheme could be extended to a more general sequential importance sampling algorithm involving arbitrary proposal sampling density. It could also be adapted to the case of spatio‐temporal inflation vectors, for which, for instance, the PF could be replaced by the multiple PF (Ait‐El‐Fquih and Hoteit, ), then compared with existing spatio‐temporal adaptive inflation schemes (e.g., Anderson, ; Bauser et al ., ; Gharamti, ). This algorithm could also integrate estimation of the localization radii and observation noise variance.…”

Section: Resultsmentioning

confidence: 97%

A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter

Ait‐El‐Fquih

Hoteit

2020

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

An adaptive covariance inflation scheme is proposed for the ensemble Kalman filter (EnKF) to mitigate the loss of ensemble variance. Adaptive inflation methods are mostly based on a Bayesian approach, which considers the inflation factor as a random variable with a given prior probability distribution and then combines it with the inflation likelihood through Bayes' rule to obtain its posterior distribution. In this work, we introduce a numerical implementation of this generic Bayesian approach that uses a particle filter (PF) to compute a Monte Carlo approximation of the inflation posterior distribution. To alleviate the sample attrition issue, the proposed PF employs an artificial dynamical model for the inflation factor based on the well‐known smoothing‐kernel West and Liu model. The positivity constraint on the inflation factor is further imposed through an inverse‐Gamma transition density, with parameters that suggest analytical expressions. The resulting PF–EnKF scheme is straightforward to implement, and can use different numbers of particles in its EnKF and PF components. Numerical experiments are conducted with the Lorenz‐96 model to demonstrate the effectiveness of the proposed method under various experimental scenarios.

show abstract

“…Anderson (2009) has proposed to add inflation as a parameter in the control vector leading to inflation being updated at each EnKF analysis. Bauser et al (2018) has successfully applied this approach to a soil hydrology problem. Other approaches based on consistency diagnostics developed by Desroziers et al (2005) (Li et al, 2009;Miyoshi, 2011) or reformulated EnKFs (Bocquet, 2011;Bocquet and Sakov, 2012) have gained popularity.…”

Section: The Question Of Cross-covariancesmentioning

confidence: 99%

An Ensemble Square Root Filter for the joint assimilation of surface soil moiture and leaf area index within LDAS-Monde: application over the Euro-Mediterranean region

Bonan

Albergel

Zheng

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. This paper introduces an Ensemble Square Root Filter (EnSRF), a deterministic Ensemble Kalman Filter, to the context of assimilating jointly observations of surface soil moisture (SSM) and leaf area index (LAI) in the Land Data Assimilation System LDAS-Monde. By ingesting those satellite-derived products, LDAS-Monde constrains the Interaction between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM), coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP), to improve the reanalysis of land surface variables (LSVs). To evaluate its ability to produce improved LSVs reanalyses, the EnSRF is compared with the Simplified Extended Kalman, which has been routinely operated in LDAS-Monde, in a real case over the well-studied Euro-Mediterranean region at a 0.25° spatial resolution between 2008 and 2017. Both data assimilation approaches provide a positive impact on SSM and LAI estimates with respect to the model alone, putting them closer to assimilated observations. SEKF and EnSRF have a similar behaviour for LAI showing performances that are influenced by the vegetation type. For SSM, EnSRF estimates tend to be closer to observations than SEKF. The impact of assimilating SSM and LAI is also assessed on unobserved soil moisture in the other layers of soil. Unobserved control variables are updated in the EnSRF through covariances and correlations sampled from the ensemble linking them to observed control variables. In our context, a strong correlation between SSM and soil moisture in deeper soil layers is exhibited, as expected, showing seasonal patterns that vary geographically. Moderate correlation and anti-correlations are also noticed between LAI and soil moisture in spring, summer and autumn, their absolute value tending to be larger for soil moisture in root-zone areas, showing that assimilating LAI can have an influence on soil moisture. Finally an independent evaluation of both assimilation approaches is conducted using satellite estimates of evapotranspiration and gross primary production (GPP) as well as measures of river discharges from gauging stations. The EnSRF shows a systematic albeit moderate improvement for evapotranspiration and GPP and a highly positive impact on river discharges, while the SEKF exhibits a more contrasting performance.

show abstract

Inflation method for ensemble Kalman filter in soil hydrology

Cited by 28 publications

References 51 publications

Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau

Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau

A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter

An Ensemble Square Root Filter for the joint assimilation of surface soil moiture and leaf area index within LDAS-Monde: application over the Euro-Mediterranean region

Contact Info

Product

Resources

About