Analyzing the Rainfall Pattern in Honduras Through Non-Homogeneous Hidden Markov Models
Pub. online: 22 February 2023
Type: Data Science In Action
Open Access
Open Access
Received
31 August 2022
31 August 2022
Accepted
19 February 2023
19 February 2023
Published
22 February 2023
22 February 2023
Abstract
One of the major climatic interests of the last decades has been to understand and describe the rainfall patterns of specific areas of the world as functions of other climate covariates. We do it for the historical climate monitoring data from Tegucigalpa, Honduras, using non-homogeneous hidden Markov models (NHMMs), which are dynamic models usually used to identify and predict heterogeneous regimes. For estimating the NHMM in an efficient and scalable way, we propose the stochastic Expectation-Maximization (EM) algorithm and a Bayesian method, and compare their performance in synthetic data. Although these methodologies have already been used for estimating several other statistical models, it is not the case of NHMMs which are still widely fitted by the traditional EM algorithm. We observe that, under tested conditions, the performance of the Bayesian and stochastic EM algorithms is similar and discuss their slight differences. Analyzing the Honduras rainfall data set, we identify three heterogeneous rainfall periods and select temperature and humidity as relevant covariates for explaining the dynamic relation among these periods.
Supplementary material
Supplementary MaterialData that support the findings are openly available in GitHub at https://github.com/gsabillon85/NHMM-Estimation and https://gsabillon85.shinyapps.io/PrecipitationNHMM/. The R codes used for simulation are openly available in GitHub at https://github.com/gsabillon85/NHMM-Estimation.
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