Spatial delineation approach to weather derivatives with three multivariate manners

The delineation of natural hazard with adequate manners is an important factor and a dominant process in designing weather derivatives. This research conducts and compares three delineation techniques with real-time weather data from 2013 to 2015, collected from monitoring stations of the Central We...

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Bibliographic Details
Published in:Natural hazards (Dordrecht) 2022, Vol.110 (2), p.1227-1245
Main Authors: Shao, Ai-Ju, Yu, Tai-Yi
Format: Article
Language:English
Subjects:
Agricultural production
Agriculture
Analysis
Civil Engineering
Climate change
Cluster analysis
Delineation
Developing countries
Dimensions
Drought
Earth and Environmental Science
Earth Sciences
Eigenvalues
Environmental Management
Environmental protection
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Insurance policies
LDCs
Meteorological data
Monitoring
Monitoring systems
Multivariate analysis
Natural Hazards
Original Paper
Principal components analysis
Rain
Temperature
Wavelet transforms
Weather
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Summary:The delineation of natural hazard with adequate manners is an important factor and a dominant process in designing weather derivatives. This research conducts and compares three delineation techniques with real-time weather data from 2013 to 2015, collected from monitoring stations of the Central Weather Bureau and Environmental Protection Administration in Taiwan. Three multivariate analyses are performed, including principal component analysis (PCA), positive matrix factorization (PMF), and hierarchical cluster analysis (HCA). Results reveal the optimal number for temperature hazard delineation in Taiwan is three, composed of north of central Taiwan, the southwest, and southeast Taiwan; eigenvalues of the three sub-regions are greater than 1 and explanatory power of the variance accounted for temperature is 44.6%, 27.5%, and 24.7%, respectively. Though the regional delineation conducted by HCA is not as good as PCA, however, HCA is a useful technique to provide objective numbers of delineation. Analysis with PMF lacks uniqueness due to several stations having high factor loadings at distinct principal components. Additionally, PMF is not appropriate for high-dimension problems and non-uniform distributions of monitoring sites.
ISSN:0921-030X
1573-0840
DOI:10.1007/s11069-021-04989-y