Deep Retrieval Architecture of Temperature and Humidity Profiles from Ground-Based Infrared Hyperspectral Spectrometer

نویسندگان

چکیده

Temperature and humidity profiles in the atmospheric boundary layer are essential for climate studies. The ground-based infrared hyperspectral spectrometer has advantage of measuring radiances emitted from atmosphere at a high temporal moderate vertical resolution. In this article, retrieval temperature observations is exploited. Although existing inversion algorithms based on physical models or statistical learning have made some progress, they still suffer computational complexity poor performance. Motivated by strength deep learning, we present architecture (DReA) skillfully designing light-weight one-dimensional convolution neural network (CNN) to retrieve profiles. Experiments were conducted using radiance interferometer (AERI) radiosonde data demonstrate superiority proposed DReA. validation DReA with radiosonde, 802 37 layers below 3 km, presents an excellent ability root mean square error (RMSE) 0.87 K 1.06 g/kg water vapor mixing ratio. Furthermore, thorough comparison commonly used methods such as traditional back propagation (BP) eigenvector (EV) regression method, shows that our method obtains leading solution retrieving

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2023

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs15092320