Algorithm and Performance of Precipitable Water Vapor Retrieval Using Multiple GNSS Precise Point Positioning Technology

Hu, Peng; Huang, Guanwen; Zhang, Qin; Wang, Xiaolei; Mao, Min

doi:10.1007/978-981-13-0005-9_12

Cited by 4 publications

(8 citation statements)

References 11 publications

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“…Microwave radiometers can provide reliable PWV readings only under no rainfall conditions [4]. They are not able to give accurate readings for all-weather conditions [5]. It might even require site or season-specific calibrations [6,7].…”

Section: Introductionmentioning

confidence: 99%

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

2019

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Global positioning system (GPS) satellite delay is extensively used in deriving the precipitable water vapor (PWV) with high spatio–temporal resolution. One of the recent applications of GPS derived PWV values are to predict rainfall events. In the literature, there are rainfall prediction algorithms based on GPS-PWV values. Most of the algorithms are developed using data from temperate and sub-tropical regions. Mostly these algorithms use maximum PWV rate, maximum PWV variation and monthly PWV values as a criterion to predict the rain events. This paper examines these algorithms using data from the tropical stations and proposes the use of maximum PWV value for better prediction. When maximum PWV value and maximum rate of increment criteria are implemented on the data from the tropical stations, the false alarm ( F A ) rate is reduced by almost 17% as compared to the results from the literature. There is a significant reduction in F A rates while maintaining the true detection ( T D ) rates as high as that of the literature. A study done on the varying historical length of data and lead time values shows that almost 80% of the rainfall can be predicted with a false alarm of 26 . 4 % for a historical data length of 2 hours and a lead time of 45 min to 1 hour.

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Section: Introductionmentioning

confidence: 99%

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

2019

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“…PWV values can be determined through the measurement data using various different instruments like radiosondes, microwave-radiometers, satellite-based instruments and GPS signal delays. The radiosonde and microwave radiometers are the traditional techniques [14][15][16], which are useful in validating the PWV values from GPS and satellite-based instruments. This section provides a detailed literature review on how these techniques are used as well as the state-of-the art of these techniques.…”

Section: State-of-the-art Techniques For Precipitable Water Vapor Retmentioning

confidence: 99%

“…Although, the microwave radiometers have found to derive the PWV values with good accuracy and with a high temporal resolution, the ground-based microwave radiometers are restricted due to its poor spatial coverage. Microwave radiometers have sparse station distribution [16]. It has high instrument cost and hence it cannot be deployed easily [14].…”

Section: Microwave Radiometers For Pwv Retrievalmentioning

confidence: 99%

“…Microwave radiometers can provide reliable PWV readings only under no rain-fall conditions [31]. They are not able to perform in all-weather conditions [16]. It might even require site or season specific calibrations [14,15].…”

Section: Microwave Radiometers For Pwv Retrievalmentioning

confidence: 99%

“…With the rapid development of the current GNSS constellation, there is a promising prospect for the real-time retrieval of tropospheric parameters like wet delays and PWV [45,46]. Therefore, GPS meteorology offers an improved spatial and temporal resolutions for water vapor retrieval compared to the traditional techniques [16,20]. GPS is widely being used as an all-weather, low-cost remote sensing instrument for weather forecasting and climatology.…”

Section: Gps-derived Pwv Valuesmentioning

confidence: 99%

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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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Assessment of GNSS PPP-Based Zenith Tropospheric Delay

Abdelazeem

El-Rabbany

2020

Artificial Satellites

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This study assesses the precision of zenith tropospheric delay (ZTD) obtained through triple-constellation global navigation satellite system (GNSS) precise point positioning (PPP). Various ZTD estimates are obtained as by-products from GPS-only, GPS/Galileo, GPS/BeiDou, and triple-constellation GPS/Galileo/BeiDou PPP solutions. Triple-constellation GNSS observations from a number of globally distributed reference stations are processed over a period of seven days in order to investigate the daily performance of the ZTD estimates. The estimated ZTDs are then validated by comparing them with the International GNSS Service (IGS) tropospheric products and the University of New Brunswick (UNB3m) model counterparts. It is shown that the ZTD estimates agree with the IGS counterparts with a maximum standard deviation (STD) of 2.4 cm. It is also shown that the precision of estimated ZTD from the GPS/Galileo and GPS/Galileo/BeiDou PPP solutions is improved by about 4.5 and 14%, respectively, with respect to the GPS-only PPP solution. Moreover, it is found that the estimated ZTD agrees with the UNB3m model with a maximum STD of 3.1 cm. Furthermore, the GPS/Galileo and GPS/Galileo/BeiDou PPP enhance the precision of the ZTD estimates by about 6.5 and 10%, respectively, in comparison with the GPS-only PPP solution.

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Algorithm and Performance of Precipitable Water Vapor Retrieval Using Multiple GNSS Precise Point Positioning Technology

Cited by 4 publications

References 11 publications

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

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

Assessment of GNSS PPP-Based Zenith Tropospheric Delay

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