Water vapor study using MODIS and GPS data at 64 continuous GPS stations (2002–2017) in indian subcontinent

Jade, Sridevi; Shrungeshwara, T.S.; Anil, Boddapati

doi:10.1016/j.jastp.2019.105138

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…In the Indian subcontinent, a first-time PWV study was conducted in 2004 based on the global positioning system (GPS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite measurements. 19 Due to different geographical and climatic conditions, the PWV variability across Indian regions is very diverse. For example, Jade et al (2019) 19 showed that the interannual variation of PWV is about 9–22% for southern India, 9–19% for Himalaya and about 3–10% for northeast India.…”

Section: Introductionmentioning

confidence: 99%

“…19 Due to different geographical and climatic conditions, the PWV variability across Indian regions is very diverse. For example, Jade et al (2019) 19 showed that the interannual variation of PWV is about 9–22% for southern India, 9–19% for Himalaya and about 3–10% for northeast India. Mishra (2019) 20 showed a positive trend of about 1.69% dec −1 in PWV for the period 1980–2018 in India.…”

Section: Introductionmentioning

confidence: 99%

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Long-term changes in precipitable water vapour over India derived from satellite and reanalysis data for the past four decades (1980–2020)

Sarkar

Kuttippurath

Patel

2023

Environ. Sci.: Atmos.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long-term changes in precipitable water vapour over India derived from satellite and reanalysis data for the past four decades (1980–2020)

Sarkar

Kuttippurath

Patel

2023

Environ. Sci.: Atmos.

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“…YUAN experimentally confirmed that high-precision PWV could be obtained based on GNSS real-time data in 2014, and the accuracy was better than 3mm [7] . In 2019, Sridevi studied the relationship between average GNSS-PWV and GNSS station height in India and found that the two were inversely proportional [8] . In 2021, XU studied the Marine and land colorimeter mounted on the Sentinel-3 series satellites and compared it with GNSS PWV and found that the root-mean-square error of PWV obtained by them was 3.154mm [9] .…”

Section: Introductionmentioning

confidence: 99%

Estimation of ground GNSS precipitable water based on atmospheric water vapor region model

Yan,

Yang,

Hou

et al. 2024

International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024)

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“…PWV can be converted from the ZWD by multiplying it by a conversion factor, which is a function of atmospheric weighted mean temperature (T m ) [13]. In the past three decades, the methods of retrieving PWV from GNSS (GNSS-PWV) have been given much attention, including the determination of the ZHD and T m [14][15][16][17][18][19], and the accuracy of GNSS-PWV [20][21][22][23]. Nowadays, GNSS-PWV is used to investigate the evolution of several types of weather events [24][25][26][27][28] and climate change [29][30][31] due to its advantage of low cost and all-weather high resolution and high accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Rainfall Forecast Based on GNSS-PWV

Zhang

et al. 2022

Remote Sensing

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Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall.

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Water vapor study using MODIS and GPS data at 64 continuous GPS stations (2002–2017) in indian subcontinent

Cited by 14 publications

References 28 publications

Long-term changes in precipitable water vapour over India derived from satellite and reanalysis data for the past four decades (1980–2020)

Long-term changes in precipitable water vapour over India derived from satellite and reanalysis data for the past four decades (1980–2020)

Estimation of ground GNSS precipitable water based on atmospheric water vapor region model

An Improved Method for Rainfall Forecast Based on GNSS-PWV

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