On the statistical learning analysis of rain gauge data over the Natuna Islands

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Authors

Sandy Hardian Susanto Herho, Faiz Rohman Fajary, Dasapta Erwin Irawan 

Abstract

This article presents state-of-the-art statistical learning methods for analyzing rain gauge data over the Natuna Islands. By using shape preserving piecewise cubic interpolation, we managed to interpolate 671 null values from the daily precipitation data. Dominant periodicity analysis of daily precipitation signals using
Lomb-Scargle Power Spectral Density shows annual, intraseasonal, and interannual precipitation patterns over the Natuna Islands. Unsupervised anomaly analysis using the Isolation Forest algorithm shows there are 146 anomaly daily precipitation data points. We also conducted an experiment to predict the accumulation of monthly precipitation over the Natuna Islands using the Bayesian structural time series algorithm. The results show that the local linear trend with seasonality model is able to model the value of accumulated monthly precipitation for a twelve-month prediction horizon. The work presented here has profound implications for rainfall observations in this area.

DOI

https://doi.org/10.31223/X55D03

Subjects

Applied Statistics, Atmospheric Sciences, Longitudinal Data Analysis and Time Series, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistics and Probability

Keywords

observational tropical meteorology, cubic interpolation, Lomb- Scargle PSD, isolation forest, Bayesian structural time series, cubic interpolation, Lomb-Scargle PSD, Bayesian structural time series

Dates

Published: 2021-06-08 09:35

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
https://github.com/sandyherho/natunaRainStatAnal

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