ARMA model development and analysis for global temperature uncertainty

Authors

Mahmud Hasan, University of Mississippi
Gauree Wathodkar, University of MississippiFollow
Mathias Muia, University of Mississippi

Document Type

Article

Publication Date

4-4-2023

Abstract

Temperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean temperature anomaly X(t), which is a combination of a true (latent) global anomaly Y(t) for a year (t) and normal variable w(t). The uncertainty is taken as the variance of w(t), which was divided into land surface temperature (LST) uncertainty, sea surface temperature (SST) uncertainty, and the corresponding source of uncertainty. The ARMA model was analyzed and compared with autoregressive (AR) and autoregressive integrated moving average (ARIMA) for the data obtained from the NASA Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis. The statistical analysis of the autocorrelation function (ACF), partial autocorrelation function (PACF), normal quantile–quantile (normal Q-Q) plot, density of the residuals, and variance of normal variable w(t) shows that ARMA (1,1) fits better than AR (1) and ARIMA (1, d, 1) for d = 1, 2.

Relational Format

article

Comments

The Article Processing Charge (APC) for this article was partially funded by the UM Libraries Open Access Fund.

Recommended Citation

Hasan M, Wathodkar G and Muia M (2023) ARMA model development and analysis for global temperature uncertainty. Front. Astron. Space Sci. 10:1098345. doi: 10.3389/fspas.2023.1098345

DOI

doi: 10.3389/fspas.2023.1098345

Accessibility Status

Searchable text