Application of Cumulative Distribution Functions to Compositing Precipitable Water with Low Earth Orbit Satellite Data

Heo, Jun-Hyung; Ryu, Geun-Hyeok; Sohn, Eun-Ha; Jang, Jae-Dong

doi:10.1007/s13143-019-00113-0

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…The CDF method was first proposed by Reichle and Koster [31] to reduce the bias of satellite soil moisture. It has been widely used to remove systematic biases between observation data and reference data [32]- [34]. The CDF of the satellite-derived SST should be the same as that of the in situ SST, so the bias of the satellite observations could be defined as the difference between the satellite-retrieved SST and the in situ SST at the same percentiles.…”

Section: Bias Correctionmentioning

confidence: 99%

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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: Bias Correctionmentioning

confidence: 99%

“…In the previous study, different periods were selected to generate reference data or train data for the similar method [23], [34], [35]. Here, simple tests are performed to decide the best period for calculating regression coefficients using the data in January and July in 2019, respectively.…”

Section: Bias Correctionmentioning

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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“…Thus, merging AOD products derived from both GEO and LEO satellite sensors could yield more comprehensive results [37]. Therefore, the spatiotemporal differences between different AOD products should be minimized to ensure a more comprehensive representation of natural phenomenon [38]. However, retrieving high-resolution AOD at varying scales is still a challenging task due to the low signal-to-noise ratio in sensing, algorithmic synthesis constraints, downscaling issues, and data gaps resulting from adverse impacts such as cloud contamination [39,40].…”

Section: Introductionmentioning

confidence: 99%

Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

Ahn

Chung

et al. 2021

Remote Sensing

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This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.

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Application of Cumulative Distribution Functions to Compositing Precipitable Water with Low Earth Orbit Satellite Data

Cited by 2 publications

References 19 publications

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

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

Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

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