A cloud classification method based on random forest for FY-4A

Yu, Zhuofu; Ma, Shuo; Han, Dejun; Li, Guanlin; Gao, Deyuan; Yan, Wei

doi:10.1080/01431161.2020.1871098

Cited by 11 publications

(8 citation statements)

References 53 publications

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“…The RF algorithm has been widely used in cloud classification research and is considered effective [25,26]. However, the combined use of the UFSC method and an RF model has rarely been seen.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, Zhang [25] used the cloud classification product generated by the Clouds from AVHRR Extended System (CLAVR-x) from the National Oceanic and Atmospheric Administration (NOAA) as the true value. Fengyun-4A is part of the new generation of Chinese geostationary meteorological satellites; it has high spatial and temporal resolutions, and its cloud type (CLT) product can also be used to examine cloud classification results [26,27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8

Wang,

Hu,

Ding

et al. 2023

Atmosphere

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It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8

Wang,

Hu,

Ding

et al. 2023

Atmosphere

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“…Forsythe et al proposed that clouds of the same type have the same or similar cloud heights and preliminarily verified the superiority of cloud height estimation schemes based on this assumption using satellite cloud type products and ground observation station cloud height data [ 64 ]. Wang and Yu et al further verified the feasibility of estimating cloud height based on the same cloud type on this basis [ 65 , 66 ]. Therefore, after nearest-neighbor spatiotemporal matching, this study performs the same cloud type matching between MODIS pixel points and CALIPSO data pixel points.…”

Section: Data and Matchingmentioning

confidence: 99%

Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

Cheng,

He,

Xue

et al. 2024

Sensors

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In order to enhance the retrieval accuracy of cloud top height (CTH) from MODIS data, neural network models were employed based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Three types of methods were established using MODIS inputs: cloud parameters, calibrated radiance, and a combination of both. From a statistical standpoint, models with combination inputs demonstrated the best performance, followed by models with calibrated radiance inputs, while models relying solely on calibrated radiance had poorer applicability. This work found that cloud top pressure (CTP) and cloud top temperature played a crucial role in CTH retrieval from MODIS data. However, within the same type of models, there were slight differences in the retrieved results, and these differences were not dependent on the quantity of input parameters. Therefore, the model with fewer inputs using cloud parameters and calibrated radiance was recommended and employed for individual case studies. This model produced results closest to the actual cloud top structure of the typhoon and exhibited similar cloud distribution patterns when compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) CTHs from a climatic statistical perspective. This suggests that the recommended model has good applicability and credibility in CTH retrieval from MODIS images. This work provides a method to improve accurate CTHs from MODIS data for better utilization.

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“…Dixon et al (2015) introduced deep neural network into the futures market and predicted the prices of various futures traded on the Chicago Mercantile Exchange based on this algorithm, with good prediction results and high accuracy of the model [5]. Yu Huanjie (2017) established a model using random forest algorithm to predict the illegal behaviors of market subjects and assist the government in market supervision based on the data of 120,000 enterprises in Beijing, greatly improving the supervision efficiency [6]. Zhang Ningjing et al (2018) analyzed the data of P2P online loan and used data mining Adaboost algorithm to identify the influencing factors of borrower default risk, based on the US online loan platform.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on application of machine learning algorithm in digital economy

Diao

Wang²,

Li³

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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This paper mainly uses support vector machine, random forest, logistic regression, and other machine learning algorithms to analyze the core data of listed companies, and then monitor their operating conditions and predict their bankruptcy probability. At the same time, the prediction effects of the three machine learning methods are evaluated by confusion matrix, ROC curve and other methods and indicators, and the best effect of the method is found. By predicting the bankruptcy probability of listed companies this study further supplements and improves the theory of machine learning algorithm on economic quantitative analysis.

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A cloud classification method based on random forest for FY-4A

Cited by 11 publications

References 53 publications

All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8

All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8

Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

Research on application of machine learning algorithm in digital economy

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