A cloud optical and microphysical property product for the advanced geosynchronous radiation imager onboard China's Fengyun-4 satellites: The first version

Liu, Chao; Song, Yulong; Zhou, Ganning; Teng, Shiwen; Li, Bo; Xu, Na; Lu, Feng; Zhang, Peng

doi:10.1016/j.aosl.2023.100337

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…Moreover, the maximum predicted value is 13.05, whereas the maximum actual value is 20.78, indicating that the model lacks learning capability in the region of thick optical clouds (COT > 12). 2023) [11] used the classic dual-channel retrieval method based on FY4A AGRI products for COT retrieval and compared it with MODIS products, obtaining an R 2 of 0.58. In contrast, our CM4CR algorithm, based on the multichannel products provided by FY4A AGRI and employing a scientific data-matching method with CALIPSO products, achieved an R 2 of 0.75, demonstrating superior COT retrieval performance.…”

Section: Cot Retrieval Experiments Resultsmentioning

confidence: 99%

“…If the cloud is an ice cloud, the Voronoi Ice Crystal Scattering (ICS) model is used. Liu et al (2023) [11] employed the classical bi-spectral retrieval method for COT inversion based on FY4A AGRI products. They first use a machine learning algorithm (random forest) to detect the cloud mask (CMa) and cloud phase (CPH) as prerequisites for COT and CER retrieval.…”

Section: Retrieval Of Cotmentioning

confidence: 99%

“…With the advancement of satellite technology, satellite observations provide substantial support for obtaining cloud parameters and their spatiotemporal variations at regional and global scales. In recent years, research utilizing satellite observations to retrieve cloud microphysical parameters has received increasing attention [10][11][12][13][14][15][16]. The fundamental principle of satellite-based COT retrieval is that in the visible light spectrum, cloud reflectance primarily depends on the value of COT [17].…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data

Xu,

Song,

Chen

et al. 2024

Remote Sensing

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The traditional method for retrieving cloud optical thickness (COT) is carried out through a Look-Up Table (LUT). Researchers must make a series of idealized assumptions and conduct extensive observations and record features in this scenario, consuming considerable resources. The emergence of deep learning effectively addresses the shortcomings of the traditional approach. In this paper, we first propose a daytime (SOZA < 70°) COT retrieval algorithm based on FY-4A AGRI. We establish and train a Convolutional Neural Network (CNN) model for COT retrieval, CM4CR, with the CALIPSO’s COT product spatially and temporally synchronized as the ground truth. Then, a deep learning method extended from video prediction models is adopted to predict COT values based on the retrieval results obtained from CM4CR. The COT prediction model (CPM) consists of an encoder, a predictor, and a decoder. On this basis, we further incorporated a time embedding module to enhance the model’s ability to learn from irregular time intervals in the input COT sequence. During the training phase, we employed Charbonnier Loss and Edge Loss to enhance the model’s capability to represent COT details. Experiments indicate that our CM4CR outperforms existing COT retrieval methods, with predictions showing better performance across several metrics than other benchmark prediction models. Additionally, this paper also investigates the impact of different lengths of COT input sequences and the time intervals between adjacent frames of COT on prediction performance.

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Section: Cot Retrieval Experiments Resultsmentioning

confidence: 99%

Section: Retrieval Of Cotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data

Xu,

Song,

Chen

et al. 2024

Remote Sensing

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“…Notably, no cloud model was used in this paper, and relatively thick cirrus clouds can affect the simulation of bright temperatures. As a next step, we can explore the use of the fast RTM models developed for the Fengyun satellites (Yao et al., 2020), cloud‐phase products for AGRI instruments (Liu et al., 2023) and bias correction schemes with cloud variables (Feng & Pu, 2022; Okamoto et al., 2019) to further improve the effect of AGRI CER assimilation.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…Therefore, incorporating AGRI radiance assimilation affected by cirrus clouds can greatly improve the utilization of the data.Notably, no cloud model was used in this paper, and relatively thick cirrus clouds can affect the simulation of bright temperatures. As a next step, we can explore the use of the fast RTM models developed for the Fengyun satellites(Yao et al, 2020), cloud-phase products for AGRI instruments(Liu et al, 2023) and bias correction schemes with cloud variables(Feng & Pu, 2022;Okamoto et al, 2019) to further improve the effect of AGRI CER assimilation.On the other hand, the incorporation of radiosonde data below 400 hPa in the cirrus cloud region can also improve the effectiveness of AGRI assimilation. However, such observations are scarce and unavailable over the ocean Shi et al (2023).…”

mentioning

confidence: 99%

The Impact of Assimilating Cirrus‐Effected Infrared Satellite Radiance From the FY‐4A AGRI on Water Vapor Analysis and Rainstorm Forecasting

Xu,

Liu,

Cheng

et al. 2024

Geophysical Research Letters

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In this study, a method for assimilating FY4A advanced geostationary radiance imager (AGRI) cirrus‐effected radiances (CER) is investigated, and the impact of this method on water vapor analysis and rainstorm forecasting is examined through observing system simulation experiments and actual case experiments. The high proportion of inverted humidity profiles in the cirrus‐effected pixels is the main reason for the negative effect of assimilation in the mid‐to‐lower troposphere. To address this, relevant constraint conditions are incorporated into the cost function. The statistical results reveal that the addition of a CER assimilation improves the analysis increment of water vapor, with pattern correlation coefficients of 0.33, 0.35, and 0.20 at 200, 300, and 400 hPa, respectively, which are greater than those of a clear‐sky radiance assimilation (0.28, 0.33, and 0.17, respectively). Moreover, the inclusion of a CER assimilation greatly improves data utilization, and has a neutral to positive effect on precipitation forecasting.

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Fengyun Radiation Services for Solar Energy Meteorology: Status and Perspective

Xia,

Yang,

Shen

2024

Adv. Atmos. Sci.

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A cloud optical and microphysical property product for the advanced geosynchronous radiation imager onboard China's Fengyun-4 satellites: The first version

Cited by 9 publications

References 21 publications

Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data

Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data

The Impact of Assimilating Cirrus‐Effected Infrared Satellite Radiance From the FY‐4A AGRI on Water Vapor Analysis and Rainstorm Forecasting

Fengyun Radiation Services for Solar Energy Meteorology: Status and Perspective

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