The Uncertainty Quantification and Statistical Analysis Group carries out research and technology development in computational techniques to extract knowledge from science data.

What we do: Model the statistical variability of physical systems like oceans and atmospheres; infer parameters, recognize patterns, and quantify uncertainty in massive data sets taken from such systems.

Uncertainty Quantification

• Uncertainty quantification (UQ) for retrievals from Earth-observing instruments including OCO-2, MLS, ECOSTRESS, and MAIA
• Uncertainty assessment and propagation for a large-scale hydrological routing model, and uncertainty assessment for groundwater using models and gravimetric observations

Statistical Data Fusion and Science Data Analysis

• Spatial and spatio-temporal data fusion for atmospheric fields in a Bayesian context, with applications to near surface temperature products from AIRS
• Developed a leading system for recognizing, grouping, and tracking solar active regions used for SOHO and distributed as a data product for SDO
• Proprioceptive and appearance-based terrain classification for autonomous rover navigation (DARPA, ARO)
• Developed multi-observation compressed summaries for MISR and AIRS, which have been distributed as a Level 3 data product
• Developing new statistical methods for assessing agreement between Earth system model simulations and observations in a distributed data context

Physical Modeling and Advanced Inversion Algorithms

• Inversion of cosmic microwave background to recover spherical harmonic power, and structural parameters of cosmological models (Wilkinson Microwave Anisotropy Probe and Planck)
• Data assimilation and Bayesian risk assessment for highly nonlinear weather systems