Assimilation of passive and active microwave soil moisture retrievals

Draper, C.; Reichle, Rolf H.; Lannoy, Gabriëlle J. M. De; Liu, Q.

doi:10.1029/2011gl050655

Cited by 240 publications

(225 citation statements)

References 18 publications

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“…However, without bias correction, it is not possible to conduct meaningful comparisons between in situ, satellite-retrieved and modelled SM for validation and optimal data assimilation (Yilmaz and Crow, 2013). Standard bias correction methods are now increasingly being applied to SM assimilation in land models (Reichle et al, 2007;Kumar et al, 2012;Draper et al, 2012), numerical weather prediction (Drusch et al, 2005;Scipal et al, 2008a) and hydrologic models (Brocca et al, 2012). proposed matching statistical moments of the data, while linear methods based on simple regression and matching dynamic ranges have also been considered (e.g.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale analysis of bias correction of soil moisture

Ryu

2015

Hydrol. Earth Syst. Sci.

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Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatiotemporal variability of the target variable, inter-sensor differences with variable measurement supports. One such example is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that is increasingly being used to address this issue, but is only applicable to the linear regime, whereas the non-linear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatio-temporal scales, we propose a multi-timescale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multiscale bias correction and data-adaptive, non-linear de-noising via wavelet thresholding.

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Section: Introductionmentioning

confidence: 99%

Multi-scale analysis of bias correction of soil moisture

Ryu

2015

Hydrol. Earth Syst. Sci.

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“…All SMOS observations were bias corrected by accounting for the climatological differences (mean and standard deviation) between SMOS T B observations and the model equivalents obtained with a low frequency passive microwave radiative transfer model. To obtain rescaling coefficients accounting for the seasonal meteorology, a seasonal linear rescaling approach was conducted following the same approach as in [26]. More information about the bias correction scheme applied to SMOS data will be published later in [27].…”

Section: Experiments Typesmentioning

confidence: 99%

Sensitivity of Soil Moisture Analyses to Contrasting Background and Observation Error Scenarios

Albergel

Rosnay

Muñoz-Sabater

et al. 2018

Water

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Soil moisture is a crucial variable for numerical weather prediction. Accurate, global initialization of soil moisture is obtained through data assimilation systems. However, analyses depend largely on the way observation and background errors are defined. In this study, a wide range of short experiments with contrasted specifications of the observation error and soil moisture background were conducted. As observations, screen-level variables and brightness temperatures from the Soil Moisture and Ocean Salinity (SMOS) mission were used. The region of interest is North America, given the good availability of in situ observations and mixture of different climates, making it a good test for global applications. The impact of these experiments on soil moisture and the atmospheric layer near the surface were evaluated. The results highlighted the importance of assimilating observations sensitive to soil moisture for air temperature and humidity forecasts. The benefits on predicting the soil water content were more noticeable with increasing the SMOS observation error, and with the introduction of soil texture dependency in the soil moisture background error.

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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%

“…The daily CCI-SM product (v03.2) is produced at 0.25° spatial resolution from the microwave retrieved surface soil moisture data and is merged from multiple sensors (Dorigo et al, 2017;Liu et al, 2012;Liu et al, 2011;Wagner et al, 2012; http://www.esa-5 oilmoisture-cci.org). For the study period of 2000 to 2006, the CCI-SM data are based on passive microwave observations (i.e.…”

Section: Esa CCI Microwave Soil Moisturementioning

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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Assimilation of passive and active microwave soil moisture retrievals

Cited by 240 publications

References 18 publications

Multi-scale analysis of bias correction of soil moisture

Multi-scale analysis of bias correction of soil moisture

Sensitivity of Soil Moisture Analyses to Contrasting Background and Observation Error Scenarios

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

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