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Spatial-temporal Characterization of Hurricane Path using GNSS-derived Precipitable Water Vapor: Case Study of Hurricane Matthew in 2016
Hoda Tahami, Jihye Park
Pages - 1 - 17     |    Revised - 31-05-2020     |    Published - 30-06-2020
Volume - 7   Issue - 1    |    Publication Date - June 2020  Table of Contents
GNSS Meteorology, Precipitable Water Vapor (PWV), Principle Component Regression (PCR), Hurricane.
Global Navigation Satellite System (GNSS) precise point positioning (PPP) technique is capable of monitoring Precipitable Water Vapor (PWV) in high accuracy with low cost. As PWV is related to the initiation and development of a severe weather convective system, this study analyzed the characteristics of PWV variations over time and space to monitor and predict the path and the intensity of a severe rainfall during a hurricane. The PWV measurements are obtained by processing ground based GNSS data. The spatial and temporal variation of PWV and other meteorological variables are characterized for the time frames of before, during, and after the severe precipitation. The correlation effect between meteorological variables were mitigated by adapting a principle component analysis (PCA) and multivariate regression analysis. The method allows determining the expected movement of the rainfall up to 24 hours in advance. The proposed method was validated by analyzing the distribution pattern of the predicted PWV residual, its magnitude, and the actual observed PWV in the region. As a case study, we adopted one of the destructive and long-lived hurricane along the Florida, Georgia, North Carolina and South Carolina coast, namely, Hurricane Matthew, occurred in October 2016. From the experiment, we identified the areas closely fitting the prediction model by computing the residuals between the GNSS derived PWV measurements at each station in the test site. The residual of the predicted model is used for determining the track of extreme hurricane precipitation and potentially applied to evaluate its intensity. This study proved the effectiveness of the statistical model for forecasting the hurricane rainfall path that is potentially applied to a hazard early warning system.
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Mrs. Hoda Tahami
Civil Engineering Department, Oregon State University, Corvallis Oregon, 97331 - United States of America
Dr. Jihye Park
Civil Engineering Department, Oregon State University, Corvallis Oregon, 97331 - United States of America