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

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
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.

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

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.

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

2021 ◽  
Vol 14 (12) ◽  
pp. 7821-7834
Author(s):  
Wengang Zhang​​​​​​​ ◽  
Ling Wang ◽  
Yang Yu ◽  
Guirong Xu ◽  
Xiuqing Hu ◽  
...  
Keyword(s):  
Water Vapor ◽  
Hydrological Cycle ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Spatial Distance ◽  
The Black Sea ◽  
Radiation Balance ◽  
Global Evaluation ◽  
Medium Resolution ◽  
Spectral Imager

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.

An Empirical Model for Estimating Precipitable Water Vapor on the Tibetan Plateau

2019 ◽  
Vol 131 (1006) ◽  
pp. 125001 ◽  
Author(s):  
Xuan Qian ◽  
Yongqiang Yao ◽  
Lei Zou ◽  
Hongshuai Wang ◽  
Jia Yin
Keyword(s):  
Water Vapor ◽  
Tibetan Plateau ◽  
Empirical Model ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
The Tibetan Plateau

scholarly journals Spatial Distributions and Seasonal Variations of Tropospheric Water Vapor Content over the Tibetan Plateau

2013 ◽  
Vol 26 (15) ◽  
pp. 5637-5654 ◽  
Author(s):  
Yuwei Zhang ◽  
Donghai Wang ◽  
Panmao Zhai ◽  
Guojun Gu ◽  
Jinhai He
Keyword(s):  
Water Vapor ◽  
Tibetan Plateau ◽  
Seasonal Variations ◽  
Precipitable Water ◽  
High Water ◽  
Water Vapor Content ◽  
The Tibetan Plateau ◽  
Spatial Distributions ◽  
Vapor Flux ◽  
Horizontal Gradients

Abstract Spatial distributions and seasonal variations of tropospheric water vapor over the Tibetan Plateau and the surrounding areas are explored by means of water vapor products from the high-resolution Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite and the NASA Water Vapor Project (NVAP). Because NVAP has a serious temporal inhomogeneity issue found in previous studies, the AIRS retrieval product is primarily applied here, though similar seasonal variations can be derived in both datasets. Intense horizontal gradients appear along the edges of the plateau in the lower-tropospheric (500–700 hPa) water vapor and columnar precipitable water, in particular over the regions along the southeastern boundary. Rich horizontal structures are also seen within the plateau, but with a weaker gradient. In the mid- to upper troposphere (300–500 hPa), horizontal gradients are relatively weak. It is shown that there is always a deep layer of high water vapor content over the plateau with a peak around 500 hPa, which can extend from the surface to roughly 300 hPa and even to 100 hPa at some locations. This layer of high water vapor content has consistent influence on precipitating processes in the downstream regions such as the valleys of the Yellow and Yangtze Rivers. Estimated vertically integrated water vapor flux and moisture divergence in the two layers (500–700 and 300–500 hPa) further confirm the effect of the Tibetan Plateau on the downstream regions. In particular, the mid- to upper-layer water vapor (300–500 hPa) tends to play an essential role during both the warm and cold seasons, confirmed by the spatial distribution of seasonal-mean precipitation.

scholarly journals Evaluation of Precipitable Water Vapor from Four Satellite Products and Four Reanalysis Datasets against GPS Measurements on the Southern Tibetan Plateau

2017 ◽  
Vol 30 (15) ◽  
pp. 5699-5713 ◽  
Author(s):  
Yan Wang ◽  
Kun Yang ◽  
Zhengyang Pan ◽  
Jun Qin ◽  
Deliang Chen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Tibetan Plateau ◽  
Near Infrared ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
The Tibetan Plateau ◽  
Gps Measurements ◽  
Infrared Measurements ◽  
Southern Tibetan Plateau ◽  
Level 2

The southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV product appears to be most useful for constructing high-resolution and high-quality PWV datasets over the TP; particularly the AIRS Level-2 product has a relatively low bias (0.48 mm) and RMSE (1.83 mm) and correlates strongly with the GPS measurements ( R = 0.90). The four reanalysis datasets exhibit similar performance in terms of their correlation coefficients ( R = 0.87–0.90), bias (0.72–1.49 mm), and RMSE (2.19–2.35 mm). The key finding is that all the reanalyses have positive biases along the PWV seasonal cycle, which is linked to the well-known wet bias over the TP of current climate models.

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