Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

Lee, Ju Hyoung

doi:10.2136/vzj2014.06.0060

Cited by 9 publications

(14 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the estimation of dielectric constant, both satellite retrievals and models require a similar type of soil property information. For example, if the clay fraction in soil maps used for both land surface model and satellite retrievals is overestimated [5,46], then this is escalated to the overestimation of wilting point or field capacity in the land surface models and adversely affects the satellite retrievals to convert dielectric constant to soil moisture, resulting in the similar overestimation of soil moisture in both satellite retrieval and land surface models [18,47,48].…”

Section: Triple Collocation (Tc) Methodsmentioning

confidence: 99%

“…There are two different approaches in retrieval ensembles: (1) the deterministic approach to assemble multiple retrieval models [63]; and (2) the probabilistic approach to process various retrieval input data with the same retrieval algorithm, where the input errors are stochastically defined in a form of PDF (Probability Density Function) [48,52,64]. These are also called PDF methods.…”

Section: The Concept and Type Of Retrieval Ensemblesmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

2017

Self Cite

View full text Add to dashboard Cite

Abstract:To apply satellite-retrieved soil moisture to a short-range weather prediction, we review a stochastic approach for reducing foot print scale biases and estimating its uncertainties. First, we discuss a challenge of representativeness errors. Before describing retrieval errors in more detail, we clarify a conceptual difference between error and uncertainty in basic metrological terms of the International Organization for Standardization (ISO), and briefly summarize how current retrieval algorithms deal with a challenge of land surface heterogeneity. As compared to relative approaches such as Triple Collocation, or cumulative distribution function (CDF) matching that aim for climatology stationary errors at time-scale of years, we address a stochastic approach for reducing instantaneous retrieval errors at time-scale of several hours to days. The stochastic approach has a potential as a global scheme to resolve systematic errors introducing from instrumental measurements, geo-physical parameters, and surface heterogeneity across the globe, because it does not rely on the ground measurements or reference data to be compared with.

show abstract

Section: Triple Collocation (Tc) Methodsmentioning

confidence: 99%

Section: The Concept and Type Of Retrieval Ensemblesmentioning

confidence: 99%

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pauwels et al (2009) were one of the first to optimize soil hydraulic parameters of a land surface model by data assimilation, assimilating synthetic aperture radar data. Lee (2014) used synthetic aperture radar soil moisture data to estimate soil hydraulic properties at the Tibetan plateau using the EnKF and a soil-vegetationatmosphere transfer model. Bateni and Entekhabi (2012) assimilated land surface temperature with an ensemble Kalman smoother and achieved a better estimate of the partitioning of energy between sensible and latent heat fluxes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

Baatz¹,

Franssen²,

Han³

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km 2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm 3 cm −3 for the assimilation period and 0.05 cm 3 cm −3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm 3 cm −3 ). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

show abstract

“…It was previously shown that the DEnKF performs better with a reduced ensemble size and converges better than the standard EnKF [23], [31]. For ensemble generation, please see [23], [24]. A SVAT model propagated soil moisture ensembles for 40 days.…”

Section: ) Sequential Ensemble Kf (Denkf)mentioning

confidence: 99%

EnOI Optimization for SMOS Soil Moisture Over West Africa

Lee

Pellarin²,

Kerr

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

In land surface or numerical weather prediction (NWP) models, a soil moisture initialization scheme is important not to drift the prognostic variables to errors. We propose a novel approach for a stationary data assimilation scheme of ensemble optimal interpolation (EnOI) effective for soil moisture and ocean salinity (SMOS) soil moisture initialization. For the optimization of EnOI, the satellite retrieval error specification was conducted rather than ensemble evolution. As combining two ensembles generated from a satellite retrieval and a land surface model, this approach is termed as "two-step EnOI" in this study: (first step) the SMOS soil moisture retrieval ensembles (i.e., errors in brightness temperature, landscape, and geophysical parameters) were merged with SMOS L3 data; (second step) the data assimilation result from the first step was further used for the observations of the EnOI. This two-step EnOI was compared with a sequential ensemble Kalman filter (EnKF) evolving model state ensembles over time but assuming global constant a priori random errors for the SMOS observations. The point-scale comparison results showed that two-step EnOI was better matched with the field measurements than the SMOS L3 data and a sequential ensemble KF scheme. On meso-scale, a spatial average of two-step EnOI reached that of a sequential ensemble KF with the significantly reduced ensemble size. These results suggest that the performance of two-step EnOI is comparable to a sequential ensemble KF but computationally more effective. From this, it is illustrated that appropriate error specification of satellite retrieval is more important than a sequential evolution of model state ensembles, and brightness temperature ensemble mean can reduce the SMOS retrieval biases without sequential evolution.Index Terms-Brightness temperature errors, ensemble kalman filter (EnKF), ensemble optimal interpolation (EnOI), soil moisture and ocean salinity (SMOS) soil moisture, West Africa.

show abstract

Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

Cited by 9 publications

References 85 publications

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

EnOI Optimization for SMOS Soil Moisture Over West Africa

Contact Info

Product

Resources

About