The rich and growing legacy of land surface products from current and planned satellite earth observing systems, offers the global land surface modelling community an opportunity to improve process representation in models and drive prediction capability to ever increasing spatial and temporal resolution. In addition, inclusion of data assimilation into operational services produces a higher quality product, which leads to greater customer satisfaction. Assimilation of these satellite-derived products into models poses many complex challenges, including the development of observation models to bridge the model-observation scale disparity, error characterisation to make optimal use of the diverse range of data, efficient integration techniques and the high-performance computing resources to manage and process the huge volumes of data.
This session invites contributions on novel methods of satellite data assimilation into land surface and hydrology models. We especially invite contribution that demonstrate the utilisation of earth observation to drive high-resolution modelling and prediction capability, continentally or globally. The session can host a broad range of topics, including: model and observation uncertainty quantification; conceptual relationship between observation and model scales; the novel mathematical assimilation techniques; and approaches to overcome the potential constraints imposed by the computational software and hardware. We particularly encourage submissions that may lead towards the development of best-practice approaches and technologies or that demonstrate successful applications of assimilation delivering customer value in operational environments.
Key topics: Data assimiliation, Land surface modelling, Forecasting, Uncertainty