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Water 2018, 10(7), 890; https://doi.org/10.3390/w10070890

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

1
European Centre for Medium Range Weather Forecasts, Shinfield Road, Reading RG2 9AX, UK
2
CNRM UMR 3589, Météo-France/CNRS, 31057 Toulouse, France
*
Author to whom correspondence should be addressed.
Received: 9 April 2018 / Revised: 15 June 2018 / Accepted: 22 June 2018 / Published: 4 July 2018
(This article belongs to the Special Issue Monitoring and Predicting Soil Moisture and Drought Conditions)
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Abstract

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. View Full-Text
Keywords: soil moisture; SMOS; data assimilation soil moisture; SMOS; data assimilation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Muñoz-Sabater, J.; de Rosnay, P.; Albergel, C.; Isaksen, L. Sensitivity of Soil Moisture Analyses to Contrasting Background and Observation Error Scenarios. Water 2018, 10, 890.

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