Description:

In the last two decades, earth-atmosphere system sensors and models have generated unprecedented amounts of observations and data. However, these observations and datasets have drastically different spatial and temporal characteristics. Novel data fusion and analysis methodologies and software systems are essential for realizing the full potential of earth-atmosphere observations and model datasets for basic research and applications. Recent advances in computing have led to petabytes of earth-atmosphere system observations hosted on platforms such as Google Earth Engine that provide both access and cloud computing analysis capabilities. Observations collected by other agencies, and citizen science activities are also available from disparate cloud-accessible sources. This session solicits papers that utilize statistical, machine learning, and physical modeling to fuse and analyze observations and model datasets in support of basic research and decision-making. Software systems that enable such methodologies, especially those adopting open science principles, are also of interest.