Developing a Statistical Thermodynamic Profiling Retrieval for the AERI

Currently, the standard methods for observing tropospheric temperature and moisture profiles are radiosondes and satellite-based profilers. Forecasting high impact events such as severe weather outbreaks often require higher temporal resolution profiles near the surface. Ground based passive thermodynamic profiling systems are a promising tool for fulfilling this necessity. Two systems are often used for this task: microwave radiometers (MWR) and the Atmospheric Emitted Radiance Interferometer (AERI). Most commercial multi-channel MWRs use a statistical retrieval to back out temperature and moisture vertical profiles from the observed radiance data. While MWRs are able to retrieve fairly accurate profiles in the lower troposphere, the AERI has more spectral channels that allow more information about the profile to be derived. Thermodynamic and moisture profiles are commonly retrieved in AERI studies via an iterative physical retrieval. Although AERI physical retrievals are useful in research, they are much more computationally expensive and thus somewhat slow for use in an operational setting. This research will address this issue by developing multiple statistical retrieval methods for the AERI. We will evaluate the accuracy and error characteristics of these different statistical retrieval methods, and identify possible methods whose implementation may help introduce the AERI instrument into an operational setting.