Water Vapor Retrievals from Near-infrared Channels of the Advanced Medium Resolution Spectral Imager Instrument onboard the Fengyun-3D Satellite

Hu, Xiuqing; Xu, Na; Chen, Lin

doi:10.1007/s00376-020-0174-8

Cited by 21 publications

(22 citation statements)

References 38 publications

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“…A series of comprehensive prelaunch calibration have been operated to ensure the high quality of the products from MERSI-Ⅱ (Xu et al, 2018), which was from MERSI and has been significantly improved with high-precision on-board calibration and lunar calibration capabilities (Wu et al, 2020). Besides, MERSI-Ⅱ has 25 channels with a spectral coverage from 0.412 µm to 12.0 µm, and the NIR PWV products of FY-3D are retrieved with three absorption channels (bands 16, 17 and 18) and two nonabsorption channels (bands 15 and 19) in the 0.8-1.3 μm range with a spatial resolution of 1 km at nadir (Wang et al, 2021).…”

Section: Mersi-ⅱ Pwvmentioning

confidence: 99%

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Evaluation and Application of Precipitable Water Vapor Product from MERSI-II onboard the Fengyun-3D Satellite

Zhang

et al. 2021

Preprint

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Abstract. The evaluation of precipitable water vapor (PWV) derived from the advanced Medium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D is performed with the PWV from Integrated Global Radiosonde Archive (IGRA) based on 626 sites (54214 match-ups) in total during 2018–2021. The averaged PWVs from MERSI-II and IGRA both present the distribution opposite to latitude, with great PWV mostly found in the tropics. In general, a good consistency exists between the PWVs of MERSI-Ⅱ and IGRA, and their correlation coefficient is 0.9400 and root mean squared error (RMSE) is 0.31 cm. The peak values of mean bias (MB) and the mean relative bias (MRB) are 0.00 cm and −2.38 %, with the standard deviations of 0.25 cm and 16.8 %, respectively. For most sites, the PWV is underestimated with the MB between −0.28 cm and 0.05 cm. However, there is also overestimated PWV, which is mostly distributed in the surrounding areas of the Black Sea and the middle of South America. The peak values of MB are found in February and July over the Southern and Northern Hemisphere, respectively. More than 66.91 % of retrievals falling within the except error (EE) envelope during all months. Overall, the MRB and RMSE become larger with the increasing temporal and distance discrepancy, and it is contrast for EE and correlation coefficient. Besides, the distance discrepancy impacts the evaluation more. The application of PWV product over Qinghai-Tibet Plateau shows that the transport of water vapor along the Brahmaputra Grand Canyon is obvious and it is more significant in July.

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Section: Mersi-ⅱ Pwvmentioning

confidence: 99%

“…CC BY 4.0 License. two non-absorption channels (Gao and Kaufman, 2003;Wang et al, 2021). The setup of non-absorption channels of MERSI-II is same as that of MERSI but the absorption channels of MERSI-II are similar with those of MODIS.…”

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confidence: 99%

Evaluation and Application of Precipitable Water Vapor Product from MERSI-II onboard the Fengyun-3D Satellite

Zhang

et al. 2021

Preprint

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“…According to the spectral range of remote sensing data, the water vapor retrieval algorithms can be divided into three categories: microwave algorithm [14,18], thermal infrared (TIR) algorithm [19,20] and near-infrared (NIR) algorithm [15,21]. After decades of development, there are a large number of satellite sensors that can be used for water vapor retrieval, such as the medium resolution spectral imager-2 (MERSI-2) and MODIS for the NIR algorithm [15,22,23], the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Very High Resolution Radiometer (AVHRR) for the TIR algorithm [24][25][26][27] and the Microwave Radiation Imager (MWRI) and the CMR for the microwave algorithm [14,16,28,29]. Since the spatial distribution of water vapor changes rapidly with time, and the transit time of the above-mentioned sensors is different, all of the PWV products developed for the above sensors are very useful for the assessment of climate change and the atmospheric correction of remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

“…It has 25 spectral bands, including nine visible (VIS) bands, eight NIR bands, four mid-infrared (MIR) bands and four far-infrared (FIR) bands. Wang et al [22] developed four PWV datasets over land using five NIR bands of MERSI-2 with the wavelengths at around 1 micron. The above-mentioned PWV datasets have been adopted by China's National Satellite Meteorological Center (NSMC) and have been publicly released as official PWV products.…”

Section: Introductionmentioning

confidence: 99%

“…In order to facilitate the use of MERSI-2 PWV products, it is necessary to evaluate them comprehensively. The previous study has shown that the MERSI-2 PWV products have high accuracy and are in good agreement with ground-based PWV data [22]. However, the previous validation mainly focused on the evaluation of MERSI-2 PWV products in North America.…”

Section: Introductionmentioning

confidence: 99%

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Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET

Xie

Hou

et al. 2021

Remote Sensing

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The medium resolution spectral imager-2 (MERSI-2) is one of the most important sensors onboard China’s latest polar-orbiting meteorological satellite, Fengyun-3D (FY-3D). The National Satellite Meteorological Center of China Meteorological Administration has developed four precipitable water vapor (PWV) datasets using five near-infrared bands of MERSI-2, including the P905 dataset, P936 dataset, P940 dataset and the fusion dataset of the above three datasets. For the convenience of users, we comprehensively evaluate the quality of these PWV datasets with the ground-based PWV data derived from Aerosol Robotic Network. The validation results show that the P905, P936 and fused PWV datasets have relatively large systematic errors (−0.10, −0.11 and −0.07 g/cm2), whereas the systematic error of the P940 dataset (−0.02 g/cm2) is very small. According to the overall accuracy of these four PWV datasets by our assessments, they can be ranked in descending order as P940 dataset, fused dataset, P936 dataset and P905 dataset. The root mean square error (RMSE), relative error (RE) and percentage of retrieval results with error within ±(0.05+0.10∗PWVAERONET) (PER10) of the P940 PWV dataset are 0.24 g/cm2, 0.10 and 76.36%, respectively. The RMSE, RE and PER10 of the P905 PWV dataset are 0.38 g/cm2, 0.15 and 57.72%, respectively. In order to obtain a clearer understanding of the accuracy of these four MERSI-2 PWV datasets, we compare the accuracy of these four MERSI-2 PWV datasets with that of the widely used MODIS PWV dataset and AIRS PWV dataset. The results of the comparison show that the accuracy of the MODIS PWV dataset is not as good as that of all four MERSI-2 PWV datasets, due to the serious overestimation of the MODIS PWV dataset (0.40 g/cm2), and the accuracy of the AIRS PWV dataset is worse than that of the P940 and fused MERSI-2 PWV datasets. In addition, we analyze the error distribution of the four PWV datasets in different locations, seasons and water vapor content. Finally, the reason why the fused PWV dataset is not the one with the highest accuracy among the four PWV datasets is discussed.

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Water vapour products from ERA5, MERSI‐II/FY‐3D, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra in Australia: A comparison against in situGPS water vapour data

Liu

2023

Quart J Royal Meteoro Soc

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Australia is a region of high sensitivity to the El Niño–Southern Oscillation events in association with atmospheric water vapour, which is an essential atmospheric parameter in hydrological cycle, energy transport and climate monitoring. In this article, we thoroughly validated the quality of multisource precipitable water vapour (PWV) products sourced from ERA5 reanalysis, advanced Medium Resolution Spectral Imager (MERSI‐II) sensor on the FY‐3D satellite, Ocean and Land Colour Instrument (OLCI) sensor on the Sentinel‐3A and Sentinel‐3B satellites, and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua and Terra satellites. The PWV estimates measured by 453 in situ Global Positioning System (GPS) sites from 1 June 2019 to 31 May 2020 over Australia were employed as the common reference. The validation results show that all the reanalysis‐based and satellite‐based PWV products had a good agreement with reference GPS‐based PWV data. ERA5 reanalysis had the highest PWV accuracy with a root‐mean‐square error (RMSE) of 2.016 mm and a relative RMSE (RRMSE) of 12.038%, while the MODIS/Terra instrument had the lowest PWV accuracy (RMSE = 4.903 mm and RRMSE = 34.187%). In contrast to the MERSI‐II/FY‐3D instrument that underestimated PWV, the PWV data from ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra overestimated the water vapour values. The quality of reanalysis‐based and satellite‐based PWV measurements showed significant dependencies on several variables – location, PWV, solar zenith angle, season, elevation, land surface cover and latitude. It is expected that this research can help enhance the understanding of the quality of reanalysis and satellite water vapour products in Australia, that is, ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra. This work could provide an insight into improving the accuracy of reanalysis‐based and satellite‐based PWV data products after exploring the dependency factors that affect PWV performance as presented in our work.

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Water Vapor Retrievals from Near-infrared Channels of the Advanced Medium Resolution Spectral Imager Instrument onboard the Fengyun-3D Satellite

Cited by 21 publications

References 38 publications

Evaluation and Application of Precipitable Water Vapor Product from MERSI-II onboard the Fengyun-3D Satellite

Evaluation and Application of Precipitable Water Vapor Product from MERSI-II onboard the Fengyun-3D Satellite

Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET

Water vapour products from ERA5, MERSI‐II/FY‐3D, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra in Australia: A comparison against in situGPS water vapour data

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