Assessment and calibration of MODIS precipitable water vapor products based on GPS network over China

Bai, Jingna; Lou, Yidong; Zhang, Weixing; Zhou, Yaozong; Zhang, Zhenyi; Shi, Chuang

doi:10.1016/j.atmosres.2021.105504

Cited by 34 publications

(10 citation statements)

References 34 publications

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“…Our evaluation result was consistent with previous studies. In addition, most previously published studies indicated that MODIS PWV products tended to overestimate PWV (Bennouna et al, 2013;Vaquero-Martínez et al, 2017;Shi et al, 2018;He and Liu, 2019;Bai et al, 2021;Xu and Liu, 2022c), which was in good agreement with our result in this work.…”

Section: Comparison Between Our Research With Previous Studiessupporting

confidence: 93%

“…PWV product from MODIS instrument has been validated extensively worldwide. For instance, the RMSE between MODIS/Terra PWV and GPS PWV was 5.35 mm in China (Bai et al ., 2021) and 5.480 mm in North America (He and Liu, 2019). Our work showed that the RMSE between MODIS PWV and GPS PWV was 4.358 mm for Aqua and 4.903 mm for Terra in Australia.…”

Section: Discussionmentioning

confidence: 99%

“…The number of in situ GPS sites across the world is also larger than that of the radiosonde stations (Vaquero‐Martínez et al ., 2018). Thus, GPS PWV observations are more easily accessible and have been widely employed as in situ reference to validate other water vapour observation instruments (Vaquero‐Martínez et al ., 2017; Bai et al ., 2021; Xu and Liu, 2022a).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Comparison Between Our Research With Previous Studiessupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…The hourly ERA5 air pressure, geopotential height, air temperature and relative humidity with horizontal resolution of 0.25 • at 37 pressure levels from 0.1 to 1000 hPa covering the period from 7 August to 13 August 2019 were used to calculate PWV at GPS stations following the same method as described in Section 2.2. According to [35], the average RMS of ERA5-derived PWV error is about 1.73 mm over China.…”

Section: Era5 Reanalysis Datamentioning

confidence: 99%

Spatio-Temporal Variations of Precipitable Water Vapor and Horizontal Tropospheric Gradients from GPS during Typhoon Lekima

Zhang

Bai

et al. 2021

Remote Sensing

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GPS data during Typhoon Lekima at 700 stations in China were processed by the Precise Point Positioning (PPP) method. A refined regional Tm model was used to derive the precipitable water vapor (PWV) at these GPS stations. Spatio-temporal variations of PWV with the typhoon process were analyzed. As the typhoon approached, PWV at stations near the typhoon center increased sharply from about 50 mm to nearly 80 mm and then dropped back to about 40–50 mm as the typhoon left. Comparisons of GPS, radiosonde, the Global Data Assimilation System (GDAS) Global Forecast System (GFS) analysis products and ERA5 reanalysis products at four matched GPS-RS stations show overall overestimations of PWV from radiosonde, GFS and ERA5 compared with GPS in a statistical perspective. An empirical orthogonal functions (EOF) analysis of the PWV during the typhoon event revealed some different patterns of variability, with both the first EOF (~36.1% of variance) and second EOF (~30.3% of variance) showing distinctively large anomalies over the typhoon landing locations. The typhoon caused a large horizontal tropospheric gradient (HTG) with the magnitude reaching 5 mm and the direction pointing to the typhoon center when it made a landfall on mainland China. The magnitude and the consistency of the HTG direction decreased overall as the typhoon weakened.

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“…Zhao et al [18] proposed a PWV fusion model in China based on the polynomial fitting and spherical harmonic function, and the results showed that the mean root square (RMS) of the hybrid PWV fusion model was less than 3 mm in any areas of China in all four seasons. Bai et al [23] proposed a linear calibration model which uses GNSS PWV to calibrate MODIS PWV in mainland China, and the results showed obvious improvements in calibrated MODIS PWV. Some of the above methods are based on interpolation approach, which may inevitably impose biases or inaccurate information due to the imperfect assumptions made for the interpolation [24].…”

Section: Introductionmentioning

confidence: 99%

Fusing Precipitable Water Vapor Data in CHINA at Different Timescales Using an Artificial Neural Network

et al. 2021

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Global climate change has noticeable influences on the water vapor redistribution in China, which is embodied by the fact that both wetting and drying tendencies were observed across China. This poses the necessity to monitor and understand the water vapor evolution in China. However, observations of water vapor from different techniques are subjected to systematic biases, different spatiotemporal resolutions and coverages, and different accuracy, which would hamper their joint use, potentially leading to contradictory conclusions when using different techniques. Data fusion is a promising way to address this problem. Some scholars have proposed several methods to fuse multi-source PWV data in China region, such as the enhanced spatial and temporal adaptive reflectance fusion model, the hybrid PWV fusion model, and the linear calibration model. Although these models can produce PWV products with improved accuracy, they still have some shortcomings, such as no consideration for spatial or temporal variations in bias or inevitably impose some biases inaccurate information since assumptions made for interpolations are imperfect. In this study, we use the high-quality Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) to calibrate and optimize the Moderate-resolution Imaging Spectroradiometer (MODIS) and the European Centre for Medium-Range Weather Forecasts ReAnalyses 5 (ERA5) PWV in 2018–2019 through a Generalized Regression Neural Network (GRNN) at annual, quarterly, and monthly timescales. Validation results demonstrate that modifying the MODIS and ERA5 PWV at the monthly timescale results in the best accuracy. In the monthly experiment, the average bias, standard deviation (STD), and root mean square (RMS) error of modified MODIS PWV are 0.0 mm, 2.6 mm, and 2.6 mm, respectively. The percentage improvement is as high as 50% in terms of RMS compared to the original MODIS PWV. It becomes 0.0 mm, 1.7 mm, and 1.7 mm for the modified ERA5 PWV and the percentage improvement is 40%. Since the biases among different products are well-calibrated and the accuracy of MODIS and ERA5 PWV is improved to the same level of GNSS PWV, we can fuse them by simply merging them. Finally, we generate a new product of PWV in China with a temporal resolution of 1 day, a spatial resolution better than 31 km, and an accuracy better than 2.7 mm, which will serve as a high-quality product for investigating the water vapor redistribution under a changing climate.

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Assessment and calibration of MODIS precipitable water vapor products based on GPS network over China

Cited by 34 publications

References 34 publications

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

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

Spatio-Temporal Variations of Precipitable Water Vapor and Horizontal Tropospheric Gradients from GPS during Typhoon Lekima

Fusing Precipitable Water Vapor Data in CHINA at Different Timescales Using an Artificial Neural Network

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