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Real-time temperature nowcasting using deep learning models across multiple locations

delete2025-03-08
delete0
PRE
AI
F
Farwa Zaka
I
Ibrahim Nafisah
J
Jianyi Lin *
M
Mohammed M. A. Almazah
I
Ijaz Hussain *
H
Hanen Louati
DOI：10.1007/s40808-025-02353-8delete
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Abstract

Abstract

En 中文
Air temperature is a crucial climatic indicator that significantly impacts various sectors, including the environment, hydrology, agriculture, and disaster management. Accurate and timely air temperature forecasting is essential for effective risk management and future planning. This study investigates the performance of Deep Learning (DL) models for nowcasting air temperature in various regions of Pakistan. We utilize hourly temperature data (2018-2023) from four meteorological sites (Murree, Swat, Multan, and Sukkur), representing different climate conditions. The models compared in this study include Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Feed Forward Neural Networks (FNN), and a hybrid CNN-LSTM architecture. The performance of these models is evaluated using several statistical criteria (MSE, MAE, RMSE, and MAPE) and visual comparisons. The results indicate that while the LSTM model performs best, the CNN, FNN, and hybrid CNN-LSTM models also show considerable promise. However, it can be concluded that the LSTM model outperforms other models. These findings highlight the adaptability of DL algorithms in predicting temperature across various climatic scenarios. The implications of this study are significant for sectors such as agriculture, transportation, and disaster relief, which depend on accurate temperature forecasts for effective resource allocation and climate risk management. By advancing nowcasting technologies in Pakistan, this research contributes to enhancing resilience to weather-related challenges.
Keywords:
Climate change
Meteorological weather nowcasting
Deep learning
Models climate risk management
Agriculture
Transportation
Disaster relief

Journal

E
Earth Systems and Environment
IF:
4.7
Papers:
1.3K
Citations:
2.1K

Organization

No organization information available