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 climate 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

A 2019 seminar highlighted recent activities in data fusion and uncertainty quantification:

• Hai Nguyen
  “Spatial Statistical Data Fusion for Remote Sensing Applications” (CL#19-7476)
  Spatial statistical methods for combination of measurements from multiple instruments, including bias removal.
• Amy Braverman
  “Uncertainty Quantification for Science and Engineering Applications” (CL#19-7610)
  Overview of uncertainty quantification in science and engineering domains.

• Vineet Yadav
  “Real-time Modeling and Software Framework for Estimating Greenhouse Gas Emissions” [with movies: PowerPoint version] (CL#15-5676)
  Moving from CO2 concentrations to CO2 emissions using spatio-temporal transport models
• Jonathan Hobbs
  “Uncertainty Quantification for Remote Sensing Retrievals: Monte Carlo Experiments for OCO-2” (CL#15-5287)
  Theory and practice for computing well-justified error bars for OCO-2 retrievals
• Fred Calef
  “Science Data Localization and Access via webGIS: MSL Test Case”
  Putting all geo-located Mars data into an interactive interface for high-tempo scientifically-informed operations