Development and evaluation of regional SST regression algorithms for FY-3C/VIRR data in the western north pacific

He, Quanjun; Zhang, Yuewei; Wang, Jiechun

doi:10.1080/2150704x.2020.1823034

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…To minimize errors from the outliers, different methods are used to remove the outliers from matchups by researchers [26]- [28]. In this study, the outliers are removed using three times the robust standard deviation (RSD) from the median of the satellite SST minus in situ SST [15].…”

Section: B Matchup Samples Extractionmentioning

confidence: 99%

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

He¹,

Hu²,

Wu³

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The advanced geosynchronous radiation imager (AGRI) aboard the Chinese Fengyun-4A (FY-4A) satellite can provide operational hourly sea surface temperature (SST) product. However, the temporal and spatial variation of the errors for this product is still unclear. In this study, FY-4A/AGRI SST is evaluated using the in situ SST from 2019-2021, and a cumulative distribution function (CDF) matching method is adopted to reduce the errors. Statistical results show that the mean bias and root mean square error (RMSE) of FY-4A/AGRI SST are -0.37 ℃ and 0.98 ℃, the median and robust standard deviation (RSD) are -0.30 ℃ and 0.90 ℃. The variations in daily and monthly errors are large and there are no prominent seasonal variations during the period analyzed. There are negative biases exceeding -1.0 ℃ in low-mid latitude regions and larger positive biases in southern high latitude region. There are dependencies of satellite SST minus in situ SST on satellite zenith angle and on SST itself. After the bias correction, the bias and RMSE are reduced to -0.02 ℃ and 0.72 ℃, and the median and RSD are reduced to 0.00 ℃ and 0.60 ℃. On the time scale, the fluctuation ranges of bias and median are smaller. The difference of satellite SST minus in situ SST can reflect the diurnal variation of SST. The biases are generally within ±0.2 ℃ in full disk. The error dependencies on satellite zenith angle and SST are also greatly reduced.

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Section: B Matchup Samples Extractionmentioning

confidence: 99%

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

He¹,

Hu²,

Wu³

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The key meteorological parameters, including atmospheric precipitable water and surface temperature, could be detected using the infrared (IR) channels from the Medium Resolution Spectral Imager-Low Light (MERSI-LL) (Table I) [2], [7]. As the collaborative observation performed by FY3C, FY3D and FY3E, they are also suitable for detecting diurnal variation and distribution evolution of extreme climate events with their similar spatial and spectral specifications of the popular sunsynchronous meteorological sensors [6], [10], [21]. MERSI-LL has improved its capability in detecting key parameters of atmosphere, land and ocean, and the spatial resolution of thermal infrared channels increased to 250m.…”

Section: Introductionmentioning

confidence: 99%

Radiometric Calibration Analysis for Thermal Infrared Data From MERSI-LL Onboard the Dust-Dawn Orbiting Satellite FY3E

Li,

Lin,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…It can take any value within the output range. 19 Continuous values are the mathematical equivalent of a graph, where the direction of the graph is calculated from existing data, such as temperature projections, gross domestic product growth estimates. In the process of building mathematical model by unsupervised learning, the algorithm only has input data and does not contain the data of output label.…”

Section: Introductionmentioning

confidence: 99%

“…Regression algorithms are named after continuous output. It can take any value within the output range 19 . Continuous values are the mathematical equivalent of a graph, where the direction of the graph is calculated from existing data, such as temperature projections, gross domestic product growth estimates.…”

Section: Introductionmentioning

confidence: 99%

Application of visual mechanical signal detection and loading platform with super‐resolution based on deep learning

Ding

Zhao

Zhang

et al. 2022

Int J of Intelligent Sys

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A visual mechanical signal detection and loading platform with super-resolution based on deep learning is designed to improve the detection accuracy of mechanical signals. The visual mechanical signal detection and loading platform with super-resolution include three-dimensional (3D) biological force quantitative detection platform and the mechanical signal loading platform with 3D magnetic distortion and ultrahigh resolution. In the 3D biological force quantitative detection platform, four 3D force sensors are used to collect mechanical signals, and the improved fuzzy clustering fusion method is used to fuse the mechanical signals collected by 3D force sensors to improve the detection accuracy of mechanical signals. The mechanical signal loading platform of 3D magnetic distortion and ultrahigh resolution technology connects the 3D magnetic distortion instrument and microscope, collects images through high-speed scanning components and distorted magnetic field, reconstructs the collected images by deep learning method, obtains ultrahigh-resolution mechanical signal visual images, and triggers mechanical signal loading and release by synchronous interactive system. The consequences of the experiment demonstrate that the designed platform can display the super-

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Development and evaluation of regional SST regression algorithms for FY-3C/VIRR data in the western north pacific

Cited by 6 publications

References 31 publications

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

Radiometric Calibration Analysis for Thermal Infrared Data From MERSI-LL Onboard the Dust-Dawn Orbiting Satellite FY3E

Application of visual mechanical signal detection and loading platform with super‐resolution based on deep learning

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