Multi-Disciplinary Collaborations Enabling Uncertainty Quantification for Climate Science

Advances in understanding the rapidly changing global climate system have been enabled by expanding diversity of data sources, including in situ and remote-sensing data. These complementary data sources combine comprehensive spatial and temporal coverage, but harnessing their potential requires expertise across multiple disciplines. In particular, remote sensing for climate science needs contributions from physical and mathematical sciences. This presentation will highlight activities within the Statistical and Applied Mathematical Sciences Institute (SAMSI) and the Institute for Mathematical and Statistical Innovation (IMSI) that fostered collaboration among multi-disciplinary teams to advance uncertainty quantification (UQ) for remote sensing of the climate system. The end-to-end science traceability for remote sensing data products often includes one or more inferences that combine complex data with physical models, and these collaborative efforts have enabled methodological and scientific advances that will be highlighted.