Real-time precise point positioning-based zenith tropospheric delay for precipitation forecasting

Zhao, Qingzhi; Yao, Yibin; Yao, Wanqiang

doi:10.1038/s41598-018-26299-3

Cited by 49 publications

(42 citation statements)

References 37 publications

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“…The true detection and false alarm rates achieved by our proposed approach show a significant improvementespecially in terms of false alarm rates -over those reported in [10], [11], [41], see Table II. 75 60-70 [11] 80 66 [41] 85 66 Table III shows a detailed comparison between the proposed method and the results reported in [35]. For all years (2012)(2013)(2014)(2015), by incorporating the effect of SR, HoD and DoY with the P W V feature, the false alarm rate can be significantly lowered.…”

Section: Benchmarkingmentioning

confidence: 59%

A Data-Driven Approach for Accurate Rainfall Prediction

Manandhar

Dev

Lee

et al. 2019

IEEE Trans. Geosci. Remote Sensing

115

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In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features like Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a four-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

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Section: Benchmarkingmentioning

confidence: 59%

A Data-Driven Approach for Accurate Rainfall Prediction

Manandhar

Dev

Lee

et al. 2019

IEEE Trans. Geosci. Remote Sensing

115

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“…The study of the water vapor in the earth's atmosphere is becoming popular in the remote sensing community for a variety of applications and domains especially in the fields of weather prediction [1,2]. As the amount of water vapor in the atmosphere is linked to the formation of rain clouds and rain, it is being studied as a useful parameter for rainfall prediction [3][4][5] and cloud micro-physics.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Therefore, PWV values from these instruments are biased towards a particular weather condition only. To overcome the drawbacks of these systems, GPS signal is extensively being used to retrieve the PWV values for many applications [1,6,31,[40][41][42].…”

Section: Modis-derived Pwv Valuesmentioning

confidence: 99%

“…GPS is widely being used as an all-weather, low-cost remote sensing instrument for weather forecasting and climatology. Particularly in the field of remote sensing and weather forecasting, the GPS-derived PWV has been used for the analysis of severe weather conditions such as storms, floods [42,47], heavy rainfall events monitoring, rainfall now-casting, cloud micro physics and dynamics studies [1,3,4,31,48]. Table 2.2, summarizes and inter-compares the advantages and disadvantages of different technologies.…”

Section: Gps-derived Pwv Valuesmentioning

confidence: 99%

“…2.5. The ZHD contributes about 90 % of the total zenith delay [1,54] and it generally depends on the surface pressure (P s ), temperature and refractive index of the troposphere [55] and empirical models are available to estimate it [56]. Unlike ZHD, it is difficult to estimate the ZW Ds because the wet delays are a function of the atmospheric water vapor profile and humidity, which is random and changes with temperature.…”

Section: Gps Signal Delaysmentioning

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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Regression models for predicting daily IGS zenith tropospheric delays in West Africa: Implication for GNSS meteorology and positioning applications

Osah

Acheampong

Fosu

et al. 2021

Meteorological Applications

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The ability to precisely and accurately model and predict tropospheric delay is essential for precise global navigation satellite system (GNSS) and meteorological applications. The International GNSS Service (IGS) provides highly accurate and highly reliable daily time series zenith tropospheric delay (ZTD) products for all its member sites using data from each IGS site. Nevertheless, if for reasons such as poor internet connectivity, equipment failure, and power outages the IGS station is inaccessible, gaps are created in the data archive, resulting in degrading the quality of the ZTD estimation, as well as inhibits the quality of precipitable water vapour (PWV) estimation, needed for precise positioning applications, meteorological studies, and weather forecasting. To address this challenge, five regression models are proposed in this study to model and predict daily ZTDs using daily datasets from four IGS stations in West Africa over a period of 5 years (2015)(2016)(2017)(2018)(2019). The site-specific Vienna Mapping Functions 3 (VMF3) products (ZTD, pressure, temperature, water vapour partial pressure) and stations' coordinates (latitudes and longitudes) are used as the predictors, while the IGS final ZTD product as the response variable in fitting the models. Several performance measures are calculated to compare the predictive performance of the models.The results show that the five regression models performed outstandingly and agree very well with the IGS-ZTD data, and hence provide a useful alternative for ZTD predictions and also in the event the West African IGS stations' ZTD data are unavailable. Nonetheless, the support vector regression model outperformed the remaining four models.

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Real-time precise point positioning-based zenith tropospheric delay for precipitation forecasting

Cited by 49 publications

References 37 publications

A Data-Driven Approach for Accurate Rainfall Prediction

A Data-Driven Approach for Accurate Rainfall Prediction

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

Regression models for predicting daily IGS zenith tropospheric delays in West Africa: Implication for GNSS meteorology and positioning applications

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