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

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 (FA) rate is reduced by almost 17% as compared to the results from the literature. There is a significant reduction in FA rates while maintaining the true detection (TD) 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.

[1]  Gunnar Elgered,et al.  Geodesy by radio interferometry - Water vapor radiometry for estimation of the wet delay , 1991 .

[2]  Yee Hui Lee,et al.  Performance of Site Diversity Investigated Through RADAR Derived Results , 2011, IEEE Transactions on Antennas and Propagation.

[3]  Menas Kafatos,et al.  Retrieval of water vapor using SSM/I and its relation with the onset of monsoon , 2004 .

[4]  Witold Rohm,et al.  Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Junhong Wang,et al.  A near-global, 2-hourly data set of atmospheric precipitable water from ground-based GPS measurements , 2007 .

[6]  S. Panda,et al.  Spatiotemporal variability of water vapor over Turkey from GNSS observations during 2009–2017 and predictability of ERA-Interim and ARMA model , 2018 .

[7]  Jing-Shan Hong,et al.  Determining the precipitable water vapor thresholds under different rainfall strengths in Taiwan , 2017 .

[8]  Shilpa Manandhar,et al.  A Data-Driven Approach for Accurate Rainfall Prediction , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Joao P. S. Catalao,et al.  On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall , 2015 .

[10]  Shilpa Manandhar,et al.  A Simplified Model for the Retrieval of Precipitable Water Vapor From GPS Signal , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Feng Yuan,et al.  GPS-Derived PWV for Rainfall Nowcasting in Tropical Region , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Giovanna Venuti,et al.  Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers , 2018, Earth, Planets and Space.

[13]  Yibin Yao,et al.  Establishing a method of short-term rainfall forecasting based on GNSS-derived PWV and its application , 2017, Scientific Reports.

[14]  Fadwa Alshawaf,et al.  Accurate Estimation of Atmospheric Water Vapor Using GNSS Observations and Surface Meteorological Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Yu Zheng,et al.  Evaluation of radiosonde, MODIS-NIR-Clear, and AERONET precipitable water vapor using IGS ground-based GPS measurements over China , 2017 .

[17]  Yibin Yao,et al.  GPS-based PWV for precipitation forecasting and its application to a typhoon event , 2018 .

[18]  Shuanggen Jin,et al.  Seasonal variability of GPS‐derived zenith tropospheric delay (1994–2006) and climate implications , 2007 .

[19]  Ashutosh Kumar Singh,et al.  Variability of GPS derived water vapor and comparison with MODIS data over the Indo-Gangetic plains , 2013 .

[20]  Wayan Suparta,et al.  Estimation of thunderstorm activity in Tawau, Sabah using GPS data , 2017 .

[21]  Xinghua Zhou,et al.  Precipitable water vapor characterization in the coastal regions of China based on ground-based GPS , 2017 .

[22]  Witold Rohm,et al.  Detecting Severe Weather using GPS Tomography: An Australian Case Study , 2012 .

[23]  Yang Gao,et al.  Real-Time GPS Precise Point Positioning-Based Precipitable Water Vapor Estimation for Rainfall Monitoring and Forecasting , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Qin Zhang,et al.  Algorithm and Performance of Precipitable Water Vapor Retrieval Using Multiple GNSS Precise Point Positioning Technology , 2018 .

[25]  Andrew L. Pazmany,et al.  Single Aircraft Integration of Remote Sensing and In Situ Sampling for the Study of Cloud Microphysics and Dynamics , 2012 .

[26]  Anup K. Prasad,et al.  Validation of MODIS Terra, AIRS, NCEP/DOE AMIP‐II Reanalysis‐2, and AERONET Sun photometer derived integrated precipitable water vapor using ground‐based GPS receivers over India , 2009 .