Contribution of soil moisture retrievals to land data assimilation products

Reichle, Rolf H.; Crow, Wade T.; Koster, Randal D.; Sharif, Hatim O.; Mahanama, Sarith

doi:10.1029/2007gl031986

Cited by 86 publications

(68 citation statements)

References 17 publications

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“…Variability in the fast reservoirs (surface soil moisture and skin temperature), by the way, is of particular interest in the modern era because it is potentially retrievable via satellite remote sensing (e.g. Reichle et al 2008).…”

Section: Experimental Designmentioning

confidence: 99%

Seasonal variation of land–atmosphere coupling strength over the West African monsoon region in an atmospheric general circulation model

Yamada

Kanae

Oki

et al. 2013

Hydrological Sciences Journal

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The seasonal variation of land-atmosphere coupling strength has been examined using an extended series of atmospheric general circulation model (AGCM) simulations. In the Western Sahel of Africa, strong coupling strength for precipitation is found in April and May, just prior to and at the beginning of the monsoon season. At this time, heat and water fluxes from the surface are strongly controlled by land conditions, and the unstable conditions in the lower level of the troposphere, as induced by local land state, allow the surface fluxes to influence the variability of convective precipitation-and thus the timing of monsoon onset.

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Section: Experimental Designmentioning

confidence: 99%

Seasonal variation of land–atmosphere coupling strength over the West African monsoon region in an atmospheric general circulation model

Yamada

Kanae

Oki

et al. 2013

Hydrological Sciences Journal

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“…We refer to this experiment as the ''uncoupled'' filter. Many data assimilation studies in hydrology adopted this strategy (Crow et al 2001;Reichle et al 2008;Pan et al 2008) when there is evidence suggesting the dynamics is horizontally uncoupled. This strategy is fast, but the effect of horizontal decoupling needs to be investigated by comparing its results to the benchmark full-rank EnKF and EnMSF.…”

Section: Synthetic Data Assimilation Experiments and Resultsmentioning

confidence: 99%

A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part I: Implementation and Synthetic Experiment

Pan

Wood

McLaughlin

et al. 2009

Journal of Hydrometeorology

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The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a hydrologic data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas-Red River basin, located in the central United States (;645 000 km 2 ), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A synthetic data assimilation experiment is performed, driven by meteorological forcing fields downscaled from the ensemble forecasts made by the NOAA/National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The classic full-rank ensemble Kalman filter is used as the benchmark to evaluate the multiscale filter performance, and comparisons are also made with a horizontally uncoupled filter. It is demonstrated that the multiscale filter is able to closely approximate the full-rank solution with a low computational cost (;1/20 of the full-rank filter) in an experiment in which the top-layer soil moisture is assimilated, whereas the horizontally uncoupled filter fails to approximate the full-rank solution.

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“…In this study, we used the EnKF, which is a relatively simple and flexible technique for assimilating satellite data into land surface models (e.g. Draper et al, 2012;Reichle et al, 2002Reichle et al, , 2008Kumar et al, 2008Kumar et al, , 2009Pipunic et al, 2008;Crow and Wood, 2003; …”

Section: Data Assimilation Frameworkmentioning

confidence: 99%

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

Naz

Kurtz

Montzka

et al. 2018

Preprint

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Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated 15 ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm 3 /mm 3 in open-loop simulations to 0.004 mm 3 /mm 3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring. 25

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Contribution of soil moisture retrievals to land data assimilation products

Cited by 86 publications

References 17 publications

Seasonal variation of land–atmosphere coupling strength over the West African monsoon region in an atmospheric general circulation model

Seasonal variation of land–atmosphere coupling strength over the West African monsoon region in an atmospheric general circulation model

A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part I: Implementation and Synthetic Experiment

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

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