Abstract. SURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surfaces: nature, town, inland water and ocean. It is mostly based on pre-existing, well-validated scientific models that are continuously improved. The motivation for the building of SURFEX is to use strictly identical scientific models in a high range of applications in order to mutualise the research and development efforts. SURFEX can be run in offline mode (0-D or 2-D runs) or in coupled mode (from mesoscale models to numerical weather prediction and climate models). An assimilation mode is included for numerical weather prediction and monitoring. In addition to momentum, heat and water fluxes, SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. The main principles of the organisation of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally, the main applications of the code are summarised. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage.

[1] 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.

Abstract. The ALADIN System is a numerical weather prediction (NWP) system developed by the international AL-ADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 partner institutes of this consortium. These configurations are called the ALADIN canonical model configurations (CMCs). There are currently three CMCs: the AL-ADIN baseline CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations.The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs, (iii) to document their most recent versions, and (iv) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the partner institutes of the AL-ADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.

Root-zone soil moisture constitutes an important variable for hydrological and weather forecast models. Microwave radiometers like the L-band instrument on board the European Space Agency’s (ESA) future Soil Moisture and Ocean Salinity (SMOS) mission are being designed to provide estimates of near-surface soil moisture (0–5 cm). This quantity is physically related to root-zone soil moisture through diffusion processes, and both surface and root-zone soil layers are commonly simulated by land surface models (LSMs). Observed time series of surface soil moisture may be used to analyze the root-zone soil moisture using data assimilation systems. In this paper, various assimilation techniques derived from Kalman filters (KFs) and variational methods (VAR) are implemented and tested. The objective is to correct the modeled root-zone soil moisture deficiencies of the newest version of the Interaction between Soil, Biosphere, and Atmosphere scheme (ISBA) LSM, using the observations of the surface soil moisture of the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) over a 4-yr period (2001–04). This time period includes contrasting climatic conditions. Among the different algorithms, the ensemble Kalman filter (EnKF) and a simplified one-dimensional variational data assimilation (1DVAR) show the best performances. The lower computational cost of the 1DVAR is an advantage for operational root-zone soil moisture analysis based on remotely sensed surface soil moisture observations at a global scale.