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NEURAL NETWORK TECHNIQUES FOR THE ESTIMATION OF OZONE VERTICAL DISTRIBUTIONS FROM GOME DATA
| Content Provider | CiteSeerX |
|---|---|
| Author | Iapaolo, M. Casadio, S. Frate, F. Del |
| Abstract | The Global Ozone Monitoring Experiment (GOME) aboard ESA’s ERS-2 satellite measures the reflected and backscattered radiation from the Earth in the UV/VIS spectral range at moderate spectral resolution. In this paper a neural network based technique for atmospheric ozone profiles retrieval from radiance spectra measured by GOME is presented. Depending on the choice of the spectral interval used for the inversion, two different neural algorithms have been designed and their retrieval performance has been tested. The effectiveness of the retrieval algorithms has been evaluated comparing their results to that yielded by other instruments and inversion techniques. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Spectral Interval Radiance Spectrum Atmospheric Ozone Profile Retrieval Algorithm Ers-2 Satellite Neural Network Different Neural Algorithm Retrieval Performance Inversion Technique Neural Network Technique Uv Vi Spectral Range Moderate Spectral Resolution Global Ozone Monitoring Experiment |
| Content Type | Text |
| Resource Type | Article |
