Next Article in Journal
Golden Seal Project: An IoT-Driven Framework for Marine Litter Monitoring and Public Engagement in Tourist Areas
Previous Article in Journal
A Robust Hybrid Forecasting Framework for the M3 and M4 Competitions: Combining ARIMA and Ata Models with Performance-Based Model Selection
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Synthetic Rainfall Modeling Using a Modified Hybrid Gamma-GP Distribution

1
Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea
2
KNU G-LAMP Research Center, Institute of Basic Sciences, Kyungpook National University, Daegu 41566, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(17), 9563; https://doi.org/10.3390/app15179563 (registering DOI)
Submission received: 27 July 2025 / Revised: 29 August 2025 / Accepted: 29 August 2025 / Published: 30 August 2025

Abstract

Stochastic weather generators are commonly employed to create synthetic sequences of daily weather variables across diverse fields, including hydrological, ecological, and agricultural studies. Realistic precipitation sequences, in particular, serve as essential inputs in numerous modeling frameworks. Generalized linear models (GLMs) that incorporate covariates to capture seasonality and teleconnections represent one effective approach for stochastic weather generation. However, these models often underestimate the interannual variability of seasonally aggregated variables, notably precipitation intensity during wet seasons. Recent methods developed to mitigate the issue of overdispersion have nevertheless struggled to adequately replicate observed precipitation intensities in wet seasons. To overcome this limitation, we propose integrating a modified hybrid gamma and generalized Pareto distribution into the GLM-based weather generator. This enhanced method was evaluated using daily precipitation data from Seoul, Korea, and successfully reproduced realistic precipitation intensities while effectively addressing the overdispersion issue.
Keywords: generalized linear model; modified hybrid gamma with generalized Pareto distribution; overdispersion; stochastic precipitation generator generalized linear model; modified hybrid gamma with generalized Pareto distribution; overdispersion; stochastic precipitation generator

Share and Cite

MDPI and ACS Style

Jin, H.G.; Hong, S.; Kim, Y. Synthetic Rainfall Modeling Using a Modified Hybrid Gamma-GP Distribution. Appl. Sci. 2025, 15, 9563. https://doi.org/10.3390/app15179563

AMA Style

Jin HG, Hong S, Kim Y. Synthetic Rainfall Modeling Using a Modified Hybrid Gamma-GP Distribution. Applied Sciences. 2025; 15(17):9563. https://doi.org/10.3390/app15179563

Chicago/Turabian Style

Jin, Hyang Gon, Seunghyun Hong, and Yongku Kim. 2025. "Synthetic Rainfall Modeling Using a Modified Hybrid Gamma-GP Distribution" Applied Sciences 15, no. 17: 9563. https://doi.org/10.3390/app15179563

APA Style

Jin, H. G., Hong, S., & Kim, Y. (2025). Synthetic Rainfall Modeling Using a Modified Hybrid Gamma-GP Distribution. Applied Sciences, 15(17), 9563. https://doi.org/10.3390/app15179563

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop