Simulation-based uncertainty quantification for atmospheric sounding(NPO-51511-1)

Complex retrieval algorithms for converting satellite-observed spectral radiances into estimates of geophysical quantities are common and important parts of the remote sensing data processing pipeline. The scientific utility of retrieval data products is bolstered by a thorough understanding of the retrieval system's operating characteristics. Each retrieval strategy presents unique challenges and requirements, but a common approach involves a physical and/or statistical model with a computational inverse method. Variability in atmospheric and surface processes, as well as in the satellite measurements themselves, contribute to the statistical properties of retrieval algorithms. Specific algorithm implementation choices can also impact the algorithm's accuracy and precision. This probabilistic and computational pipeline can be studied in depth with Monte Carlo simulation experiments of the satellite observing system using ensembles of realistic true states. This work develops methodology, software, and practices that facilitate design and post-processing of these simulation experiments.