Global evaluation of the precipitable-water-vapor product from MERSI-II (Medium Resolution Spectral Imager) on board the Fengyun-3D satellite

Abstract. Atmospheric water vapor plays a key role in Earth's radiation balance and hydrological cycle, and the precipitable-water-vapor (PWV) product under clear-sky conditions has been routinely provided by the advanced Medium Resolution Spectral Imager (MERSI-II) on board Fengyun-3D since 2018. The global evaluation of the PWV product derived from MERSI-II is performed herein by comparing it with PWV from the Integrated Global Radiosonde Archive (IGRA) based on a total of 462 sites (57 219 matchups) during 2018–2021. The monthly averaged PWV from MERSI-II presents a decreasing distribution of PWV from the tropics to the polar regions. In general, a sound consistency exists between PWV values of MERSI-II and IGRA; their correlation coefficient is 0.951, and their root mean squared error (RMSE) is 0.36 cm. The histogram of mean bias (MB) shows that the MB is concentrated around zero and mostly located within the range from −1.00 cm to 0.50 cm. For most sites, PWV is underestimated with the MB between −0.41 and 0.05 cm. However, there is also an overestimated PWV, which is mostly distributed in the area surrounding the Black Sea and the middle of South America. There is a slight underestimation of MERSI-II PWV for all seasons with the MB value below −0.18 cm, with the bias being the largest magnitude in summer. This is probably due to the presence of thin clouds, which weaken the radiation signal observed by the satellite. We also find that there is a larger bias in the Southern Hemisphere, with a large value and significant variation in PWV. The binned error analysis revealed that the MB and RMSE increased with the increasing value of PWV, but there is an overestimation for PWV smaller than 1.0 cm. In addition, there is a higher MB and RMSE with a larger spatial distance between the footprint of the satellite and the IGRA station, and the RMSE ranged from 0.33 to 0.47 cm. There is a notable dependency on solar zenith angle of the deviations between MERSI-II and IGRA PWV products.

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An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

Remote Sensing ◽

10.3390/rs12213469 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3469

Author(s):

Bilawal Abbasi ◽

Zhihao Qin ◽

Wenhui Du ◽

Jinlong Fan ◽

Chunliang Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Water Vapor ◽

Near Infrared ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Optical Remote Sensing ◽

Statistical Regression ◽

Sensing Applications ◽

Medium Resolution ◽

Spectral Imager

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

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

Remote Sensing ◽

10.3390/rs13163246 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3246

Author(s):

Yanqing Xie ◽

Zhengqiang Li ◽

Weizhen Hou ◽

Jie Guang ◽

Yan Ma ◽

...

Keyword(s):

Water Vapor ◽

Near Infrared ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

China Meteorological Administration ◽

Medium Resolution ◽

The One ◽

Spectral Imager ◽

Meteorological Satellite

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

10.5194/amt-2021-236 ◽

2021 ◽

Author(s):

Wengang Zhang ◽

Ling Wang ◽

Yang Yu ◽

Guirong Xu ◽

Xiuqing Hu ◽

...

Keyword(s):

Water Vapor ◽

Correlation Coefficient ◽

Mean Squared Error ◽

Grand Canyon ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Tibet Plateau ◽

The Black Sea ◽

Surrounding Areas ◽

Spectral Imager

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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Analysis on Precipitable Water Vapor over the Tibetan Plateau Using FengYun-3A Medium Resolution Spectral Imager Products

Journal of Sensors ◽

10.1155/2019/6078591 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Shaoqi Gong ◽

Daniel F. T. Hagan ◽

Cunjie Zhang

Keyword(s):

Seasonal Variation ◽

Water Vapor ◽

Tibetan Plateau ◽

Mean Absolute Error ◽

Precipitable Water ◽

Absolute Error ◽

The Tibetan Plateau ◽

Medium Resolution ◽

The Mean ◽

Spectral Imager

The Tibetan Plateau is the largest and highest plateau in the world, and its complex terrain affects the distribution of precipitable water vapor (PWV) in the atmosphere, which plays an important role in the weather and climate of East Asia. In this paper, the characteristics of PWV over the Tibetan Plateau are studied using the FengYun-3A Medium Resolution Spectral Imager (MERSI) water vapor products, which are retrieved from the MERSI raw images of Chinese second-generation polar orbit meteorological satellite. Firstly, the accuracy of the MERSI 5-minute water vapor product is validated using three referenced water vapor data from TERRA/MODIS, ground-based GPS, and AERONET sun photometer over the Tibetan Plateau. Then, the spatial distribution and seasonal variation of PWV over the plateau are analyzed, and the effects of topographic factors on PWV are discussed. The results indicate that the MERSI 5-minute water vapor product has a good accuracy over the Tibetan Plateau, which the mean absolute error of MERSI water vapor product is in the range of 28.91%-37.54%, the mean absolute error range between 1.87 and 2.76 millimeter (mm), and the mean bias is between -1.14 and 0.64 mm comparing three referenced data. The PWV content appears as a typical spatial pattern over the Tibetan Plateau where there is a decrease from east to west of the Tibetan Plateau with increasing elevation, with the highest values over the south of Tibet. A second pattern also appears over the eastern part of the Tibetan Plateau, where the PWV content in the Qaidam Basin and the south of Tarim Basin are also considerably high. The seasonal variation of PWV content over the Tibetan Plateau presents to be highest in summer, followed by autumn and spring, and lowest in winter. The PWV content changes periodically during the year, which fits with a quadratic polynomial over monthly scales. The topographical factors of the Tibetan Plateau were found to affect the water vapor, where the altitude and latitude are negatively correlated with water vapor, while the slope and longitude show a positive correlation with water vapor; however, the aspect does not appear to have any significant influence on water vapor.

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KORELASI MUSIMAN ANTARA ZENITH WET DELAY (ZWD) DAN DEBIT SUNGAI DI DAS CIKAPUNDUNG HULU, KAWASAN BANDUNG UTARA, JAWA BARAT

Seminar Nasional Geomatika ◽

10.24895/sng.2018.3-0.1033 ◽

2019 ◽

Vol 3 ◽

pp. 741

Author(s):

Wedyanto Kuntjoro ◽

Z.A.J. Tanuwijaya ◽

A. Pramansyah ◽

Dudy D. Wijaya

Keyword(s):

Water Vapor ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Zenith Wet Delay

Kandungan total uap air troposfer (precipitable water vapor) di suatu tempat dapat diestimasi berdasarkan karakteristik bias gelombang elektromagnetik dari satelit navigasi GPS, berupa zenith wet delay (ZWD). Pola musiman deret waktu ZWD sangat penting dalam studi siklus hidrologi khususnya yang terkait dengan kejadian-kejadian banjir. Artikel ini menganalisis korelasi musiman antara ZWD dan debit sungai Cikapundung di wilayah Bandung Utara berdasarkan estimasi rataan pola musimannya. Berdasarkan rekonstruksi sejumlah komponen harmonik ditemukan bahwa pola musiman ZWD memiliki kemiripan dan korelasi yang kuat dengan pola musiman debit sungai. Pola musiman ZWD dan debit sungai dipengaruhi secara kuat oleh fenomena pertukaran Monsun Asia dan Monsun Australia. Korelasi linier di antara keduanya menunjukkan hasil yang sangat kuat, dimana hampir 90% fluktuasi debit sungai dipengaruhi oleh kandungan uap air di troposfer dengan level signifikansi 95%. Berdasarkan spektrum amplitudo silang dan koherensi, kedua kuantitas ini nampak didominasi oleh siklus monsun satu tahunan disertai indikasi adanya pengaruh siklus tengah tahunan dan 4 bulanan.

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HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

Remote Sensing ◽

10.3390/rs13112179 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2179

Author(s):

Pedro Mateus ◽

Virgílio B. Mendes ◽

Sandra M. Plecha

Keyword(s):

Water Vapor ◽

Relative Humidity ◽

Real Time ◽

Prediction Models ◽

Weather Prediction ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Global Navigation Satellite Systems ◽

International Gnss Service ◽

Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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