GPS-Derived PWV for Rainfall Nowcasting in Tropical Region

• Published in: IEEE transactions on geoscience and remote sensing Vol. 56; no. 8; pp. 4835 - 4844
• Main Authors: Manandhar, Shilpa, Lee, Yee Hui, Meng, Yu Song, Yuan, Feng, Ong, Jin Teong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alarm systems; Algorithms; Data; Delays; Dependence; Detection; False alarms; Global Positioning System; Global positioning system (GPS); Global positioning systems; GPS; Microwave measurement; Monsoons; Nowcasting; Positioning systems; precipitable water vapor (PWV); Prediction algorithms; Rain; Rainfall; rainfall nowcasting; rainfall prediction; Satellite navigation systems; Statistical analysis; Tropical climate; Tropical environment; Tropical environments; Water vapor; Water vapour; Brazil; Singapore
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2018.2839899

In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate.