An updated treatment of soil texture and associated hydraulic properties in a global land modeling system

Lannoy, Gabriëlle De; Koster, Randal D.; Reichle, Rolf H.; Mahanama, Sarith; Qing, Liu

doi:10.1002/2014ms000330

Cited by 114 publications

(129 citation statements)

References 69 publications

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“…The CLSM simulations use GEOS-5 parameters (Mahanama et al, 2015;De Lannoy et al, 2014a) similar to those used in the SMAP L4_SM product, and are forced with 1/2 • × 2/3 • GEOS-5 forcing data from MERRA (Rienecker et al, 2011) bilinearly interpolated to the model grid. The study domain covers most of North America, with the northwestern corner at (125 • W, 55 • N) and the southeastern corner at (60 • W, 24 • N).…”

Section: Soil Moisture and Brightness Temperature Modelingmentioning

confidence: 99%

Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

Lannoy¹,

Reichle²

2016

Hydrol. Earth Syst. Sci.

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118

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Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and rootzone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 • incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

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Section: Soil Moisture and Brightness Temperature Modelingmentioning

confidence: 99%

Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

Lannoy¹,

Reichle²

2016

Hydrol. Earth Syst. Sci.

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118

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“…NASA's Catchment land surface model was run over the 9 km EASE grid cells encompassing the center of each watershed site, using atmospheric forcing fields from Modern Era Retrospective-Analysis for Research (MERRA; Rienecker et al, 2011) and recently improved soil parameters (De Lannoy et al, 2014). The model initial conditions were first spun-up from January 1993 to January 2002 using a single member without perturbations.…”

Section: The Soil Moisture Data Setsmentioning

confidence: 99%

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Draper

Reichle

2015

Hydrol. Earth Syst. Sci.

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Abstract. A 9 year record of Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SM short ), mean seasonal (SM seas ), and inter-annual (SM long ) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10 −3 m 3 m −3 ) 2 , of which 19 % was in SM long , 26 % in SM seas , and 55 % in SM short . The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SM long , 24 % in SM seas , and 38 % in SM short . For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SM short , assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SM seas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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“…If w = 1, then v standing = 1. Equation (19) shows that if w = p, then v saturated = 1; if w = 1, then v saturated = 0. Equation (17) is thus equivalent to:…”

Section: Multi-phase Dielectric Mixing Modelmentioning

confidence: 99%

“…All of the information that is necessary for calculating TB is available in the land surface model (such as soil temperature, soil moisture [18] and soil texture [19]). Thus, the new mixing model can be used in the future within a forward operator for determining TB.…”

Section: Introductionmentioning

confidence: 99%

New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves

et al. 2017

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Microwave remote sensing techniques are used, among others, for temporally and spatially highly-resolved observations of land-surface properties, e.g., for the management of agricultural productivity and water resource, as well as to improve the performances of numerical weather prediction and climate simulations with soil moisture data. In this context, the effective dielectric constant of the soil is a key variable to quantify the land surface properties. We propose a new approach for the effective dielectric constant of the multiphase soil that is based on an arithmetic average of the dielectric constants of the land-surface components with damping. The results show, on average, better agreement with experimental data than previous approaches. Furthermore, the proposed new model overcomes the theoretical limitation of previous models in the incorporation of non-physical parameters to simulate measured data experimentally with satisfactory accuracy. For microwave remote sensing such as SMAP (Soil Moisture Active Passive), SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), the physical-based model in our study showed a 23-35% RMSE (root-mean-square error) reduction compared to the most prevalent refractive mixing model in the prediction of the dielectric constant for the real and imaginary part, respectively. Furthermore, in radiowave bands used in portable soil sensors such as TDR (time-domain reflectometer) and GPR (ground-penetrating radar) the new dielectric mixing model reduced RMSE by up to 53% in the prediction of the dielectric constant. We found that the permittivity over the saturation point (porosity of dry soil) has a very different and varying pattern compared to that measured in the unsaturated condition. However, in our study, this pattern was mathematically derived from the same mixing rule applied for the unsaturated condition. It is expected that the new dielectric mixing model might help to improve the accuracy of flood monitoring by satellite.

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An updated treatment of soil texture and associated hydraulic properties in a global land modeling system

Cited by 114 publications

References 69 publications

Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves

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