Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142750
Title: GPS-PWV based improved long-term rainfall prediction algorithm for tropical regions
Authors: Manandhar, Shilpa
Lee, Yee Hui
Meng, Yu Song
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Manandhar, S., Lee, Y. H., & Meng, Y. S. (2019). GPS-PWV based improved long-term rainfall prediction algorithm for tropical regions. Remote Sensing, 11(22), 2643-. doi:10.3390/rs11222643
Journal: Remote Sensing
Abstract: 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.
URI: https://hdl.handle.net/10356/142750
ISSN: 2072-4292
DOI: 10.3390/rs11222643
Rights: © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
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