首页|NASA Langley Research Center Researcher Provides New Insights into Machine Learning (Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion)
NASA Langley Research Center Researcher Provides New Insights into Machine Learning (Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion)
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Data detailed on artificial intelligence have been presented. According to news reporting originating from Hampton, Virginia, by NewsRx correspondents, research stated, “Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the n1 vibrational band near 1300 cm-1 (7.7 mm); therefore, they can be used to monitor CH4 concentrations in the atmosphere.” Financial supporters for this research include Nasa 2017 Research Opportunities in Space And Earth Sciences; Nasa 2020 Roses Solicitation. Our news reporters obtained a quote from the research from NASA Langley Research Center: “However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements.”
NASA Langley Research CenterHamptonVirginiaUnited StatesNorth and Central AmericaAlkanesCyborgsEmerging TechnologiesMachine LearningMethane
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