Climate Data Imputation and Quality Improvement Using Satellite Data

Document Type

Article

Source of Publication

Journal of Data Science and Intelligent Systems

Publication Date

4-11-2025

Abstract

Combating climate change has emerged as a global concern recently, and meteorological data remain an important measure for analyzing and predicting climate trends. However, ground weather stations and sensors can be impacted by faults due to accidents and unreliability, often resulting in, for example, missing data and lowering the overall quality of the data. This paper explores the impact of using satellite data as an input feature for machine learning algorithms. In particular, temperature, pressure, wind speed, and global horizontal radiation data are imputed using various machine learning algorithms to overcome potential data quality issues resulting from the ground stations. The results from two experiments highlight that the performance of the algorithms significantly increases by using satellite data as input features. For instance, the incorporation of satellite data improved the R2 values for temperature prediction using Random Forest and XGBoost to 0.86 and 0.84, respectively, demonstrating a notable enhancement compared to models without satellite data. The paper discusses several implications of these findings and outlines future research directions to further enhance the predictive accuracy of meteorological data imputation using satellite inputs.

ISSN

2972-3841

Publisher

BON VIEW PUBLISHING PTE

Volume

3

Issue

2

First Page

87

Last Page

97

Disciplines

Computer Sciences

Keywords

climate change, machine learning, meteorological data imputation, renewable energy, solar irradiance forecasting, weather data cleaning

Scopus ID

105020763447

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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