Retrieving stratospheric water vapor from OMPS Limb Profiler measurements
Description
Stratospheric water vapor (SWV) plays an important role in atmospheric chemistry, dynamics, and radiative forcing. OMPS Limb Profiler (LP) provides daily near-global coverage and currently operates on two satellites, with plans to launch two additional platforms in the coming years. Developing a method to retrieve SWV from OMPS LP measurements would provide measurements of SWV into the 2030s. However, OMPS LP has low sensitivity to SWV which inhibits the application of traditional retrieval methods. We explored using neural networks to perform these retrievals.
Detailed example
Each CNN predicts a water vapor profile. The mean of the predictions comprises the reported OMPS LP water vapor profile, and the standard deviation of the predictions comprises the uncertainty in that water vapor profile.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
With the impending loss of Aura Microwave Limb Sounder (MLS) which has provided SWV measurements for two decades, our OMPS LP product will serve as a low-cost continuation of the MLS water vapor record until a successor instruments can be developed and launched.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
1. Aura MLS water vapor data product 2. OMPS LP Level-1 gridded radiance data product at select wavelengths 3. Atmospheric temperature and pressure profiles from the NASA Global Earth Observing System Forward Processing for Instrument Teams product