A Fast Retrieval of Cloud Parameters Using a Triplet of Wavelengths of Oxygen Dimer Band around 477 nm

Abstract

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.

Spectral replacement using machine learning methods for continuous mapping of the Geostationary Environment Monitoring Spectrometer (GEMS)

“…The interpolation error seriously affects Level 2 products for which the spectral fitting windows are overlapped with bad-pixel areas. For instance, cloud properties and aerosol effective height (AEH) of GEMS are retrieved from O 2 -O 2 absorption bands around 477 nm (Choi et al, 2021; where the cluster of bad pixels is located (Defect 3). During the IOT, Defect 3 caused spatial discontinuity to the retrieved cloud and AEH distribution, which made the fitting window of the products moved to avoid bad-pixel effects.…”

“…In this study, however, radiance at each wavelength for a targeted spectral region is an important output to be reproduced with machine learning models, artificial neural network (ANN) and multivariate linear regression. Theoretically, ANN can accurately emulate non-linear relations with a simple model structure using large training data (Cybenko, 1989;Hornik et al, 1989). Machine learning methods also have a high chance to successfully process hyperspectral data because the abundant datasets make the training process more efficient after breaking the curse of dimensionality with a proper preprocessing step (Gewali et al, 2018).…”