Work

New Statistical Approach to Forecasting Earth’s Skin Temperature from MERRA-2 Satellite Using Semiparametric Time Series Regression with Mixed Additive Spline Fourier (STSR-MASF)

Year: 2026

Type: article

Abstract: We introduce the Semiparametric Time Series Regression with Mixed Additive Spline Fourier (STSR–MASF) model as an innovative approach for analyzing time series data with complex patterns. The model combines the flexibility of the spline estimator in capturing nonlinear variations across specific sub-intervals and the strength of the Fourier series in representing periodically recurring patterns. Within the semiparametric regression framework, STSR–MASF integrates both linear parametric and nonparametric components, with the optimal number of knots and oscillations determined using the Generalized Cross-Validation (GCV) criterion. The model was trained and tested using Earth’s skin temperature data from the National Aeronautics and Space Administration (NASA) MERRA-2 for East Kalimantan, Indonesia, a tropical rainforest region. The results demonstrate that the STSR–MASF model provides more accurate estimations and forecasts compared to six previous methods proposed in earlier studies with highly accurate predictions. This innovation not only offers methodological advancements in nonlinear time series modeling, but also contributes practical insights into understanding variations in Earth’s skin temperature in tropical regions, supporting broader efforts toward global climate change mitigation.

Source: Forecasting

DOI: 10.3390/forecast8010006

Authors: Andrea Tri Rian Dani, Nur Chamidah, I. Nyoman Budiantara, Budi Lestari, Dursun Aydin

Institutions: Universitas Airlangga, Mulawarman University, Institut Teknologi Sepuluh Nopember, Universitas Jember, Muğla University

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,172