Spatiotemporal variability of water vapor over Turkey from GNSS observations during 2009–2017 and predictability of ERA-Interim and ARMA model

Ansari, Kutubuddin; Corumluoǧlu, O.; Panda, Sampad Kumar; Verma, Payal

doi:10.1186/s41445-018-0017-4

Cited by 16 publications

(15 citation statements)

References 52 publications

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“…Extracting the atmospheric delay of the wet part of the troposphere is also possible nowadays. Ground-based GNSS receivers can estimate Integrated Precipitable Water Vapor (IPWV) and Precipitable Water Vapor in all weather conditions [1][2][3][4][5][6]. GNSS signals also consider the impact of rainfall, theoretical results pertaining to which have been discussed by Solheim et al [7].…”

Section: Introductionmentioning

confidence: 99%

Total Electron Content Variation Over HALY (Al-Jouf), Saudi Arabia and Comparison with IRI-2012 and IRI-2016 Models

Sharma¹

2020

Preprint

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Ionospheric perdition studies are very few over Saudi Arabia due to less availability of data measurement. Although such kind of studies have been carried out all over the world, there still remains scope to ascertain prediction error in this country. Hence, in the current study, the ionospheric variation from April 2016 to February 2018 (almost 22 months) was studied over a GPS site HALY (29.140N; 36.10 0E), Al-Jouf, Saudi Arabia. Diurnal, monthly and seasonal ionospheric variations were investigated and compared with the existing global IRI (IRI 2012 and IRI 2016) models. Percentage deviation between observed and modeled TEC variation values indicated largescale deviation around 200% during the time of storm. Results showed that the IRI 2012 model had the lowest Root Mean Square Error (RMSE) value (2.7437) during the September Equinox while IRI-2016 showed the highest RMSE magnitudes (3.0166) during the December Solstice. In some seasons, the RMSE values were observed to be better for IRI 2012 while on other occasions, it emerged that IRI 2016 yielded more accurate results. Such variations indicate that even the most updated version of the IRI 2016 model is unable to provide perfect estimation and the requirement of further research and improvement in this field cannot be denied.

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Section: Introductionmentioning

confidence: 99%

Total Electron Content Variation Over HALY (Al-Jouf), Saudi Arabia and Comparison with IRI-2012 and IRI-2016 Models

Sharma¹

2020

Preprint

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“…They are not able to give accurate readings for all-weather conditions [5]. It might even require site or season-specific calibrations [6,7]. PWV values can also be measured using satellite-based instruments like moderate resolution imaging spectroradiometer (MODIS) and from sun photometer based stations like aerosol robotic network (AERONET).…”

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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“…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%

“…The major shortcoming of this instrument is its lower spatial and temporal resolutions inspite of its high operational costs. [14,23,26]. Due to its lower temporal resolution, very limited data are available for the understanding of different complex tropospheric phenomenon [26].…”

Section: Radiosonde-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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Spatiotemporal variability of water vapor over Turkey from GNSS observations during 2009–2017 and predictability of ERA-Interim and ARMA model

Cited by 16 publications

References 52 publications

Total Electron Content Variation Over HALY (Al-Jouf), Saudi Arabia and Comparison with IRI-2012 and IRI-2016 Models

Total Electron Content Variation Over HALY (Al-Jouf), Saudi Arabia and Comparison with IRI-2012 and IRI-2016 Models

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

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

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