Inter annual, spatial, seasonal, and diurnal variability of precipitable water vapour over northeast India using GPS time series

Barman, Prakash; Jade, Sridevi; Kumar, Ashok; Jamir, Wangshimenla

doi:10.1080/01431161.2016.1266110

Cited by 19 publications

(4 citation statements)

References 37 publications

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“…The PWV distribution in NW, NC and WH are similar in all four datasets. Our findings are coherent with that of Barman et al (2017), 41 where they find low values of PWV in the hilly and very high values in the coastal and NE regions. Chakraborty et al (2019) 42 also found heterogeneity in the distributions of PWV across India, with high values in the coastal and NE regions, and low values in NW arid and Himalayan regions.…”

Section: Resultssupporting

confidence: 93%

See 1 more Smart Citation

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: Resultssupporting

confidence: 93%

“…The PWV distribution in NW, NC and WH are similar in all four datasets. Our findings are coherent with that of Barman et al (2017),…”

Section: Climatology Of Pwvsupporting

confidence: 92%

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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“…Grubbs and Jain, in 2017, used nine years of data in Sweden to examine trends in PWV data obtained using radiometers, radiosondes, and GPS. Other researchers also conducted similar studies (Barman et al, 2017;Duan et al, 1996;Gradinarsky et al, 2002;Jin et al, 2009;Vey et al, 2009;Wagner et al, 2006). The use of machine learning (ML) methods in recent years to estimate an environmental or physical parameter based on its relationship with other factors has made significant progress.…”

Section: Introductionmentioning

confidence: 99%

GNSS-Derived Precipitable Water Vapor Modeling Using Machine Learning Methods

Izanlou

Amerian²,

Mousavi³

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Atmospheric water vapor plays a vital role in phenomena related to the global hydrological cycle and climate changes, and its Spatio-temporal modeling and prediction help to identify and predict climatic phenomena. Accordingly, in this study, hourly precipitable water vapor (PWV) data sets for 27 stations receiving Global Navigation Satellite Systems (GNSS) observations in one month and machine learning methods were used to estimate PWV. Machine learning methods used in this study 1. Random Forest Regression (RFR) method 2. Extreme Gradient Boosting Regression (XGBR). The root mean square error (RMSE) in PWV estimation with the RFR method (RFR PWV) is 2.42 mm, and in PWV estimation with the XGBR method (XGBR PWV) is 2.75 mm, and the R-squared (R2) of the RFR method is 0.74, and for the XGBR method, these values are equal to 0.71. The obtained results show the efficiency and accuracy of both models in estimating PWV, which shows that machine learning methods have been able to recognize the behavior and changes of precipitable vapor in a small spatial and temporal interval. Although both ways had high accuracies, the RFR model performed slightly better and had better accuracy than the XGBR model.

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“…The large amounts of water vapor in the atmosphere in low-latitude regions contribute to many meteorological phenomena such as tropical storms, El Niño and La Niña [11]. It plays a crucial role for the development of hazardous cumulus convection [12] and is generally the source of convective clouds [13]. Its variation and distribution largely affect the formation and development of the convective precipitation systems.…”

Section: Precipitable Water Vapor and Its Significancementioning

confidence: 99%

GPS signal derived precipitable water vapor and its applications in rainfall prediction

Manandhar¹

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I would like to take this opportunity to thank my supervisor, Prof. Lee Yee Hui, for supervising me during my PhD years. I got to learn a lot of technical and practical things under her guidance. She is a wonderful supervisor who has not only motivated me in doing my research but also has equally supported me in my other co-curricular interests. I feel lucky to have gotten the chance to do my PhD under her. I would like to thank my co-supervisor Dr. Yu Song Meng for his support during my PhD journey. He is a very hardworking and a dedicated researcher. Thank you Yusong for your effort in going through my works and giving me constructive feedback, always. I would like to thank Dr. Stefan Winkler and Prof Ong. for working closely with our project and guiding us in our work. I feel extremely fortunate to have a group of lovely lab mates with whom I spent most of my PhD days. I would like to thank my senior cum mentor Sutha Mam for her guidance during my initial days of PhD. Thank you Sutha Mam for your constant support and well wishes. I would like to thank my seniors Jun Xiang, Soumya, Siya, Yuan Feng and Florian. I had had most wonderful memories and have shared a warm bonding with Soumya and Yuan Feng. I would also like to thank my junior Shravan who extends his helping hands whenever needed. Apart from my lab-mates, I feel extremely lucky of having a bunch of friends who were there during my thick and thins. I would like to thank my friends Niroj, Milan, Dipu, Ujjal, Rakesh and Shiva for being around and supporting me. I would like to thank Anna and Vasilisa for all those unforgetful and fun-filled memories. And of course, Pradeep, thank you very much for your constant encouragement, support and unconditional love. Without the support of my friends, my PhD journey would vi not have been a joyous experience. My acknowledgement is incomplete without thanking my parents. I would like to thank my father and mother, Shree Hari Manandhar & Sarojani Manandhar for their love and constant support. Especially my mother who has always encouraged me to live my dreams and follow my passion. I am also thankful towards my sister, Sarbada Manandhar, not only for loving and supporting me but also for looking after my family during my PhD years when I am away from home. Also a huge thanks to my grandpa and grandma for their blessings and well-wishes. I would like to express my sincere gratitude towards each and every individual who has directly or indirectly helped me in reaching my goal. Thank you.

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Inter annual, spatial, seasonal, and diurnal variability of precipitable water vapour over northeast India using GPS time series

Cited by 19 publications

References 37 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)

GNSS-Derived Precipitable Water Vapor Modeling Using Machine Learning Methods

GPS signal derived precipitable water vapor and its applications in rainfall prediction

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