A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data

Pang, Ningqing; Kong, Hoiio; Wong, Chanseng; Li, Zijun; Du, Yu; Leung, Jeremy Cheuk-Hin

doi:10.3390/app15105321

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Open AccessArticle

A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data

by

Ningqing Pang

¹,

Hoiio Kong

^1,*

,

Chanseng Wong

²,

Zijun Li

¹,

Yu Du

^3,4

and

Jeremy Cheuk-Hin Leung

⁵

¹

Faculty of Data Science, City University of Macau, Macau 999078, China

²

Macau Meteorological Society, Macau 999078, China

³

School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China

⁴

Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

⁵

College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2025, 15(10), 5321; https://doi.org/10.3390/app15105321 (registering DOI)

Submission received: 28 March 2025 / Revised: 5 May 2025 / Accepted: 7 May 2025 / Published: 9 May 2025

(This article belongs to the Section Environmental Sciences)

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Abstract

Temperature is a core element of the regional climate system and plays a key role in energy exchange and weather evolution. The current reanalysis of temperature data faces difficulties in providing more accurate geographical temperature data due to insufficient spatial resolution (0.25° × 0.25°). In this study, a lightweight downscaling method incorporating a convolutional neural network is proposed to construct a high-resolution temperature prediction model for the Macau region based on ERA5 reanalysis data. Aiming at the existing data due to insufficient resolution, a two-stage convolutional feature extraction module is introduced to optimize the model parameters by combining them with the observation data of Macau meteorological stations. The experimental results show that the accuracy of this method is 21.4% higher than that of the traditional interpolation method in the instantaneous prediction, and the prediction effect in the next 3 h is also very good. The model is expected to be extended to other regions in the future, providing an effective solution for obtaining long-term high-resolution temperature data in other regions, which can support the refinement of meteorological services and climate research.

Keywords: ERA5 reanalysis data; convolutional neural network; temperature prediction

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MDPI and ACS Style

Pang, N.; Kong, H.; Wong, C.; Li, Z.; Du, Y.; Leung, J.C.-H. A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data. Appl. Sci. 2025, 15, 5321. https://doi.org/10.3390/app15105321

AMA Style

Pang N, Kong H, Wong C, Li Z, Du Y, Leung JC-H. A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data. Applied Sciences. 2025; 15(10):5321. https://doi.org/10.3390/app15105321

Chicago/Turabian Style

Pang, Ningqing, Hoiio Kong, Chanseng Wong, Zijun Li, Yu Du, and Jeremy Cheuk-Hin Leung. 2025. "A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data" Applied Sciences 15, no. 10: 5321. https://doi.org/10.3390/app15105321

APA Style

Pang, N., Kong, H., Wong, C., Li, Z., Du, Y., & Leung, J. C.-H. (2025). A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data. Applied Sciences, 15(10), 5321. https://doi.org/10.3390/app15105321

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

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A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data

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