Combined IR–Microwave Satellite Retrieval of Temperature and Dewpoint Profiles Using Artificial Neural Networks

Abstract

Radiance measurements from satellites offer the opportunity to retrieve atmospheric variables at much higher spatial resolution than is presently afforded by in situ measurements (e.g., radiosondes). However, the accuracy of these retrievals is crucial to their usefulness, and the ill-posed nature of the problem precludes a straightforward solution. A number of retrieval approaches have been investigated, including empirical techniques, coupling with numerical weather prediction models, and data analysis techniques such as regression. In this paper, artificial neural networks are used to retrieve vertical temperature and dewpoint profiles from infrared and microwave brightness temperatures from a polar-orbiting satellite. This approach allows retrievals to be performed even in cloudy conditions?a limitation of infrared-only retrievals. In a direct comparison of this technique with results from the operational Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) retrievals, it was found that the neural-network temperature retrievals had larger errors than the ATOVS retrievals (though generally smaller than the first guess used in the ATOVS retrievals) but that the dewpoint retrievals showed consistent improvement over the comparable ATOVS retrievals.