Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

Pauwels, Valentijn R. N.; Lannoy, Gabriëlle J. M. De; Franssen, Harrie‐Jan Hendricks; Vereecken, Harry

doi:10.5194/hess-17-3499-2013

Cited by 37 publications

(33 citation statements)

References 41 publications

(55 reference statements)

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“…Furthermore, recent studies have shown the more optimal correction of biases when applied to soil moisture anomaly observations (Albergel et al, 2012) and when applied over multi-temporal scales (Su & Ryu, 2015). Alternatively, bias correction could also be implemented online through estimation within a data assimilation scheme (De Lannoy et al, 2007;Pauwels, De Lannoy, Hendricks Franssen, & Vereecken, 2013). A thorough analysis of more comprehensive bias correction methods, including anomaly corrections and multi-scale analysis in a real data assimilation experiment could be an important subject for future research.…”

Section: Bias Removalmentioning

confidence: 97%

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

Lievens

Tomer

Bitar

et al. 2015

Remote Sensing of Environment

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202

148

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This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Données SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25 km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058 m 3 /m 3 to 0.046 m 3 /m 3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale.

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Section: Bias Removalmentioning

confidence: 97%

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

Lievens

Tomer

Bitar

et al. 2015

Remote Sensing of Environment

Self Cite

202

148

View full text Add to dashboard Cite

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“…In our SARbased WLOs, no significant bias between the SAR-derived and gauge levels could be detected (Mason et al, 2012b). For alternative processing chains, if WLO bias was found significant, and observation bias-aware DA scheme could potentially deal with this problem (e.g., Dee, 2005;Pauwels et al, 2013). …”

Section: Observation Quality Controlmentioning

confidence: 97%

Satellite-supported flood forecasting in river networks: A real case study

García-Pintado

Mason

Dance

et al. 2015

Journal of Hydrology

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s u m m a r ySatellite-based (e.g., Synthetic Aperture Radar [SAR]) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive regarding whether in an operational situation the simultaneous estimation of friction and bathymetry helps the current forecast. Overall, the results indicate the feasibility of standalone EO-based operational flood forecasting.

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“…The bias-blind state update innovations (i.e., the O 2 F residuals) are used to measure the forecast bias for the bias update, based on the assumption that the observations are unbiased, and persistence is used to predict the forecast bias. Pauwels et al (2013) recently extended the theory of the two-stage forecast bias and state estimation filter to simultaneously estimate separate observation and forecast biases. In their approach, demonstrated with synthetic experiments, the bias-blind state update innovation measures the observation bias plus the forecast bias, which is partitioned into the separate bias terms by calibration.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System

Draper

Reichle

Lannoy

et al. 2015

Journal of Hydrometeorology

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In land data assimilation, bias in the observation-minus-forecast (O 2 F) residuals is typically removed from the observations prior to assimilation by rescaling the observations to have the same long-term mean (and higher-order moments) as the corresponding model forecasts. Such observation rescaling approaches require a long record of observed and forecast estimates and an assumption that the O 2 F residuals are stationary. A two-stage observation bias and state estimation filter is presented here, as an alternative to observation rescaling that does not require a long data record or assume stationary O 2 F residuals. The twostage filter removes dynamic (nonstationary) estimates of the seasonal-scale mean O 2 F difference from the assimilated observations, allowing the assimilation to correct the model for subseasonal-scale errors without adverse effects from observation biases. The two-stage filter is demonstrated by assimilating geostationary skin temperature T skin observations into the Catchment land surface model. Global maps of the estimated O 2 F biases are presented, and the two-stage filter is evaluated for one year over the Americas. The twostage filter effectively removed the T skin O 2 F mean differences, for example, the Geostationary Operational Environmental Satellite (GOES)-West O 2 F mean difference at 2100 UTC was reduced from 5.1 K for a bias-blind assimilation to 0.3 K. Compared to independent in situ and remotely sensed T skin observations, the two-stage assimilation reduced the unbiased root-mean-square difference (ubRMSD) of the modeled T skin by 10% of the open-loop values.

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Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

Cited by 37 publications

References 41 publications

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

Satellite-supported flood forecasting in river networks: A real case study

A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System

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