Key points are not available for this paper at this time.
Two analysis schemes are developed within an off‐line version of the land surface scheme ISBA for the initialization of soil water content and temperature in numerical weather prediction models. The first soil analysis is based on optimal interpolation that is currently operational in a number of weather centers. The second soil analysis is an extended Kalman filter (EKF) which will allow the assimilation of satellite observations. First, it is shown, by comparing the Kalman gain of both analysis schemes, that it is possible to assimilate screen level temperature and relative humidity in an off‐line system. This is of great interest for future combined assimilations of conventional and satellite data. The reduced computing time in running the land surface scheme outside the atmospheric model makes Kalman filter approaches compatible with operational requirements. The methodology for coupling the land surface data assimilation with the atmospheric analysis system is explained in order to highlight the existing feedbacks between the two systems (in comparison to fully decoupled land data assimilation systems). The linearity of the observation operator Jacobians estimated by finite differences and the relevance of the soil prognostic variables to be initialized are assessed. Finally, the two systems are compared over western Europe for the month of July 2006 by assimilating screen level temperature and relative humidity every 6 h. The EKF has been simplified by keeping the covariance matrix of background errors constant. The two soil analysis schemes behave similarly in response to screen level atmospheric errors. The EKF is superior in identifying situations where the near‐surface atmosphere is sensitive to soil perturbations, which leads to better use of observations. Over France, the capability of both systems to moisten the soil when rain events are absent from the forcing is demonstrated.
Mahfouf et al. (Fri,) studied this question.