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.
Highlights
IntroductionSoil moisture is a very important variable for short and medium range weather predictions.The reason is the strong influence that it has on the partitioning between latent and sensible heat fluxes at the soil–atmosphere interface, and on the boundary layer development [1,2]
Soil moisture is a very important variable for short and medium range weather predictions.The reason is the strong influence that it has on the partitioning between latent and sensible heat fluxes at the soil–atmosphere interface, and on the boundary layer development [1,2]
Where sm is the state vector consisting of the soil moisture of the top three layers of the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model H-TESSEL, smb is the background state, yo is the observation vector, H is the non-linear observation operator projecting the model background into observation space, B is the error covariance matrix associated with the background soil moisture state and R is the observation error covariance matrix
Summary
Soil moisture is a very important variable for short and medium range weather predictions.The reason is the strong influence that it has on the partitioning between latent and sensible heat fluxes at the soil–atmosphere interface, and on the boundary layer development [1,2]. It is difficult to assign accurate errors to a blend of observations and model estimates which represent all the known sources of uncertainties (instrumental errors, simplifications of the algorithms, errors in model parameterisation, inaccurate atmospheric forcing, and representativeness errors). This is not a straightforward task and there is very little knowledge of the correct specification of these errors in soil moisture related data assimilation systems. 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 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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