## 6 Conclusions  
This review presents some examples of how Earth Observations can successfully drive model development for Earth monitoring and prediction systems, such as those used for weather and climate. Often the link between observing a process, reaching a physical understanding and designing parameterisations that can be used in models is not explicitly made but is nonetheless fundamental. Recent land surface modelling developments, aimed at increased hydrological consistency, have been substantially guided by water-dedicated missions such as SMOS and SMAP. Surface temperatures have begun to be used in models despite the resolution barriers and large systematic model errors, which had previously limited the observation uptake. This list is non-exhaustive and aims to exemplify the great opportunities offered by remote sensing of the Earth surface combined with in situ observations. Emphasizing the link between observations and model development is important as global Earth system modelling moves to the kilometer scale. Existing models need to evolve to reduce model-data discrepancies at the surface, and data assimilation needs to evolve to coupled surface-atmosphere data assimilation. Scientists working in the field can benefit from more than a decade of research in regional weather and climate models. The data volume required for a high resolution representation of the Earth surface (close to 1 billion points if 1/120 degree resolution is considered) requires smart data reductions and prioritisation of benchmarks. Surface temperature-sensitive and water-sensitive remote sensing products are key data to drive model improvements. Products that combine unique remote sensing capabilities at coarser resolution (e.g., SMOS/SMAP for soil moisture, GOME-2 for vegetation) with high resolution imagers are promising for the support and further development of the next generation of weather and climate models which will be approaching kilometer-scale resolution at the surface. To ensure continuity of the existing observation capabilities to preserve and extend complementarity and diverse and covariant information, i.e., for low frequency passive microwave measurements a follow-on mission for SMOS or SMAP is needed. While the examples given here clearly demonstrate the benefit of investing further in EO-driven model development, the gaps are substantial. In addition, the lack of coordinated efforts to attain such a goal using current and future observations can be a major obstacle. Forming application-focused model development working groups such as the ISWG will help to channel efforts and gather modelling requirements for a more effective data uptake. Three recommendations include:  
* The improvement of parameterisation schemes over land and water surfaces to reduce systematic model errors that are clearly associated to missing processes (e.g., lack or spatial resolution in horizontal and vertical dimension, inadequate representation of local physiography) that impair the realism of surface fluxes partitioning. * The development of observation operators that map the observed satellite radiance into physical quantities that are represented in models, particularly for thermal infrared imagers (e.g., for LST) and low-frequency microwave radiometer/radars (e.g., for L-band brightness temperature). * The improvement of assimilation schemes capable of handling the models and observations uncertainties related to surface heterogeneities and processes and associated to the diverse remote sensing resolutions (e.g., LST and L-band). Data assimilation shall make use of observations for constraining both surface state-variables and fluxes. To ensure progress in those areas, the synergetic use of in situ observations and satellite data is of paramount importance and should be developed in parallel so that ground-based observations can provide a solid anchor for the physical interpretation of satellite radiances in diverse biomes, ecosystems and climates.