Spectral performance evaluation of high-spectral resolution infrared atmospheric sounder onboard FY-3D

Qi-feng, 陆其峰 Lu; Fang, 周 方 Zhou; Chengli, 漆成莉 Qi; Xiuqing, 胡秀清 Hu; Hanlie, 徐寒列 Xu; Chunqiang, 吴春强 Wu

doi:10.3788/ope.20192710.2105

Cited by 6 publications

(1 citation statement)

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“…Due to the weak transmission of IR on cloud, the impact of cloud is severe [34,35] and as a result the cloud-affected pixels need to be removed. The L2 products (Feng-Yun-3D/MERSI-II 5-min granule cloud amount) of the MERSI instrument which is mounted on the FengYun-3D satellite platform were used to determine whether the HIRAS observation pixel is affected by cloud [36][37][38]. Those products include 5 min granule cloud amount and high cloud amount.…”

Section: Cloud-screeningmentioning

confidence: 99%

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

Bao

Liu

et al. 2021

Remote Sensing

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The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.

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Section: Cloud-screeningmentioning

confidence: 99%

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

Bao

Liu

et al. 2021

Remote Sensing

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The development and application of satellite remote sensing for atmospheric compositions in China

Zhang

Wang

et al. 2020

Atmospheric Research

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An improved band design framework for atmospheric pollutant detection and its application to the design of satellites for CO2 observation

Rao

et al. 2023

Journal of Quantitative Spectroscopy and Radiative Transfer

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Spectral performance evaluation of high-spectral resolution infrared atmospheric sounder onboard FY-3D

Cited by 6 publications

References 0 publications

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

The development and application of satellite remote sensing for atmospheric compositions in China

An improved band design framework for atmospheric pollutant detection and its application to the design of satellites for CO2 observation

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