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Abstract
Abstract. Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of near surface soil moisture in a footprint area, and can be used to reduce bias of model estimates (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals, as they are without any scaling or pre-processing, to improve the estimated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.
Highlights
Soil moisture plays an important role in the energy and water exchange between the atmosphere and the land surface, as well as in agricultural applications and water resource management
The control run (Control), which represents the baseline performance of the Noah model, was driven by the GDAS forcing and all the parameter fields in their unperturbed states
Given the objective of this study which is to improve the mean of analyzed soil moisture fields, basin averaged daily bias and root mean square errors (RMSE) were used to evaluate the assimilation results
Summary
Soil moisture plays an important role in the energy and water exchange between the atmosphere and the land surface, as well as in agricultural applications and water resource management. Model simulated soil moisture fields are often biased due to uncertainties in model input parameters and model physics. The existence of model bias can be seen in several model inter-comparison studies which showed that model estimated soil moisture is significantly different from each other, even when identical forcing data are used (Mitchell et al, 2004; Wood et al, 1998). Recognizing the significant disparity between the models, Mitchell et al (2004) concluded that there was a “stringent need for good absolute states of soil moisture”. Reducing the bias in model estimated soil moisture fields has been shown to have a positive impact on other physical processes. Reducing the bias in model estimated soil moisture fields has been shown to have a positive impact on other physical processes. Dirmeyer (2000) demonstrated that the rainfall patterns and the near surface air temperature can be improved by using a mean soil moisture data set derived from a global soil moisture data bank
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