Uncertainty Quantification for NASA's Microwave Limb Sounder

NASA's Microwave Limb Sounder (MLS) has been collecting data on the chemistry and dynamics of the upper troposphere, stratosphere, and mesosphere since its launch aboard EOS-Aura in July 2004. MLS scans the "forward" limb, and ground data processing software retrieves vertical profiles of temperature, water vapor, and other constituents, along the Aura orbit track. Sets of individual retrievals are performed simultaneously in 15-degree "chunks" along the orbit using all along-track sequences of soundings belonging to the chunk and in a surrounding along-track buffer region. The current MLS retrieval algorithm uses standard optimal estimation methodology as one component of an uncertainty estimation procedure for individual retrievals. However, these uncertainty estimates do not account for potential dependencies in uncertainty across space (geographically) or due to the state of the atmosphere. In this talk, we describe our approach to quantifying not only the individual per-sounding uncertainties but also across-sounding, spatially-dependent uncertainties. The latter are essential for estimating uncertainties in quantities derived from multiple individual retrievals, such as long-term trends, spatial gradients, etc.