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arXiv · 2509.01269

A hybrid dynamical-stochastic model of maximum temperature time series of Imphal, Northeast India incorporating nonlinear feedback and noise diagnostics

Mairembam Kelvin Singh·Athokpam Langlen Chanu·R. K. Brojen Singh·Moirangthem Shubhakanta Singh
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Abstract

Climate variability is a complex phenomenon resulting from numerous interacting components of a climate system across a wide range of temporal and spatial scales. Although significant advances have been made in understanding global climate variability, there are relatively less studies on regional climate modeling, particularly in developing countries. In this work, we propose a framework of data driven hybrid dynamical stochastic modeling to investigate the variability of maximum temperature recorded for the capital city of Imphal in the state of Manipur, located in the Northeast India. In light of increasing concerns over global warming, studying maximum temperature variability over varying time scales is an important area of research. Analysis using publicly available climate data over the course of 73 years, our approach yields key insights into the temperature dynamics, such as a positive increase in temperature in the region during the period investigated. Our hybrid model, combining spectral analysis and Fourier decomposition methods with stochastic noise terms and nonlinear feedback mechanisms, is found to effectively reproduce the observed dynamics of maximum temperature variability with high accuracy. Our results are validated by robust statistical and qualitative tests. We further derive Langevin and Fokker-Planck equations for the maximum temperature dynamics, offering the theoretical ground and analytical interpretation of the model that links the temperature dynamics with underlying physical principles.

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Mairembam Kelvin Singh, Athokpam Langlen Chanu, R. K. Brojen Singh, Moirangthem Shubhakanta Singh. 2026-02-05. A hybrid dynamical-stochastic model of maximum temperature time series of Imphal, Northeast India incorporating nonlinear feedback and noise diagnostics. https://doi.org/10.1016/j.chaos.2026.117971

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