Hidden Markov Modeling of Daily Climate Regimes in Sleman Regency
Kata Kunci:
Hidden Markov model, climate regime, rainfall, sleman, monsoon, time-series statisticsAbstrak
This article develops a Hidden Markov Model (HMM) framework for identifying daily climate regimes in Sleman Regency, Special Region of Yogyakarta, Indonesia. The observable vector combines log-transformed rainfall, mean air temperature, relative humidity, sunshine duration, and wind speed, while the latent process represents dry-hot, transition, and wet-humid regimes. The method includes multivariate Gaussian emissions, Markov transition probabilities, forward-backward inference, Viterbi decoding, expected regime duration, and seasonal transition extensions. Because the user did not supply a verified daily station dataset, the empirical section is explicitly presented as a reproducible illustration using 3,653 synthetic daily observations for 2015–2024 calibrated to the documented monsoonal characteristics of Yogyakarta and the location of the BMKG climatology station in Mlati, Sleman. The illustration yields regime occupancies of 41.20% for dry-hot, 19.57% for transition, and 39.23% for wet-humid conditions. Mean rainfall is 0.67, 5.43, and 14.81 mm/day, respectively, while expected regime durations are 43.0, 9.4, and 32.5 days. Wet-humid occupancy exceeds 90% in January, February, and December, whereas dry-hot occupancy exceeds 90% from June to September. The framework is data-ready for replacement with BMKG or validated reanalysis observations and provides a statistically interpretable basis for climate monitoring, agricultural planning, drought preparedness, and hydrometeorological risk management in Sleman.
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