Estimation of rainfall from the amount of water vapor in the air of Thailand
Keywords:
amount of water vapor, rainfall, upper air data, surface data, mathematical modelAbstract
This research aims to estimate rainfall amounts in different regions of Thailand using temperature and relative humidity data obtained from meteorological stations in each region of the country between 2014 to 2024. The data was then used to calculate the amount of water vapor in the air. The calculated water vapor content was then correlated with the rainfall measured at the same station. The results showed that the relationship between rainfall and water vapor in the air was relatively reliable and could be represented in a mathematical model, which would vary depending on the region. When tested with an independent dataset from the year 2025, the monthly average rainfall from the model a good agreement in distributions and trends with the measured values, showing no significant difference at the 0.05 level. The root mean square error (RMSE) was 38.693 mm.
References
Wang, R., Fu, Y., Xian, T., Chen, F., Yuan, R., Li, R., and Liu, G. (2017). Evaluation of Atmospheric Precipitable Water Characteristics and Trends in Mainland China from 1995 to 2012. Journal of Climate, vol. 30, pp. 8673-8688.
Xian, T., Su, K., Zhang, J., Hu, H., and Wang, H. (2025). Precipitable Water Vapor Retrieval Based on GNSS Data and Its Application in Extreme Rainfall. Remote Sensing, vol. 17, pp. 1-16.
Kelsey, V., Riley, S., and Minschwaner, K. (2022). Atmospheric precipitable water vapor and its correlation with clear-sky infrared temperature observations, Atmospheric Measurement Techniques, vol. 15, pp. 1563-1576.
Phokate, S. and Atyotha, V. (2018). Determimation of the Amount of Water Vapor in the Troposphere over Thailand using Surface Data. Kasem Bundit Engineering Journal, vol. 8, pp. 364-372.
Zhang, Y., Xu, J., Yang, N. and Lan, P. (2018). Variability and Trends in Global Precipitable Water Vapor Retrieved from COSMIC Radio Occultation and Radiosonde Observations. Atmosphere, vol. 9(5), pp. 1-14.
Praveena, R., Ganesh Babu, T.R., Birunda, M., Sudha, G., Sukumar, P. and Gnanasoundharam, J., (2023). Prediction of Rainfall Analysis Using Logistic Regression and Support Vector Machine. Journal of Physics: Conference Series, vol. 2466, pp. 1-9.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Journal of Engineering and Innovative Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
