A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data

Liu, Lei; Ye, Jin; Li, Shulei; Zhou, Tianjun; Wang, Qi

doi:10.3390/rs14112589

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…(1) Quality control [43]: AERI radiation spectra may contain outliers in the measuring process, so some spectral features with obvious outliers, e.g., negative radiation and smoothed spectra, were removed for obtaining a good quality. (2) Cloud mask: the presence of clouds can significantly impact the observed AERI radiation, so the laser cloud altimeter and the AERI radiation data themselves were used for cloud detection to select clear sky samples.…”

Section: Data Preprocessingmentioning

confidence: 99%

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

et al. 2023

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

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Section: Data Preprocessingmentioning

confidence: 99%

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

et al. 2023

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View full text Add to dashboard Cite

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Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey

Nie,

Li,

Paletta

et al. 2024

Renewable and Sustainable Energy Reviews

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Retrieval of temperature and humidity profiles from ground-based high-resolution infrared observations using an adaptive fast iterative algorithm

Huang,

Liu,

Yang

et al. 2023

Atmos. Meas. Tech.

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Abstract. Various retrieval algorithms have been developed for retrieving temperature and water vapor profiles from Atmospheric Emitted Radiance Interferometer (AERI) observations. The physical retrieval algorithm, named AERI Optimal Estimation (AERIoe), outperforms other retrieval algorithms in many aspects except the retrieval time, which is significantly increased due to the complex radiative transfer process. The calculation of the Jacobian matrix is the most computationally intensive step of the physical retrieval algorithm. Interestingly, an analysis of the change in AERI observations' information content with respect to Jacobians revealed that the AERIoe algorithm's performance presents negligible dependence on these metrics. Thus, the Jacobian matrix could remain unchanged when the variation in the atmospheric state is small in the retrieval process to reduce the most time-consuming computation. On the basis of the above findings, a fast physical–iterative retrieval algorithm was proposed by adaptively recalculating Jacobians in keeping with the changes in the atmospheric state. Experiments with synthetic observations demonstrate that the proposed method experiences an average reduction in retrieval time by an impressive 59 % compared to the original AERIoe algorithm while achieving maximum root-mean-square errors of less than 0.95 K and 0.22 log(ppmv) for heights below 3 km for the temperature and water vapor profile, respectively. Further analyses revealed that the fast-retrieval algorithm reached an acceptable convergence rate of 98.7 %, marginally lower than AERIoe's 99.9 % convergence rate for the 826 cases used in this study.

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A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data

Cited by 3 publications

References 48 publications

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

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

Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey

Retrieval of temperature and humidity profiles from ground-based high-resolution infrared observations using an adaptive fast iterative algorithm

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