Comprehensive evaluations of Earth Systems Science Prediction (ESSP) systems (e.g., numerical weather prediction, hydrologic prediction, climate, etc.) are essential to understand sources of prediction errors and to improve earth system models. However, numerous roadblocks limit the extent and depth of ESSP system performance evaluations. Observational data used for evaluation are often not representative of the physical structures that are being predicted. Satellite and other large spatial and temporal observations datasets can help provide this information, but the community lacks tools to adequately integrate these large datasets to provide meaningful physical insights on the strengths and weaknesses of predicted fields. ESSP system evaluations also require large storage volumes to handle model simulations, large spatial datasets, and verification statistics which are difficult to maintain. The development of innovative methods is needed to enable meaningful and informative model evaluations and comparisons for many large Earth science applications.

The purpose of this session is to bring experts together to discuss innovate methods for integrating, managing, evaluating, and disseminating information about the quality of ESSP fields in meaningful way. Presentations of these innovative methods applied to Earth science applications is encouraged. An outcome of this session is to develop a list of tools and techniques that could be developed and provided to the community in the future.