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Machine Learning Integration for Improved Accuracy and Efficiency in Atmospheric Forecasting

Saravana M K1 Dhanush B2 Harish Potadar3 Lakshman S4 Roshan Zameer5
1Professor, Department of Computer Science and Design, Dayananda Sagar Academy of Technology & Management, Bengaluru, Karnataka, India. 2Undergraduate Students, Department of Computer Science and Design, Dayananda Sagar Academy of Technology & Management, Bengaluru, Karnataka, India.

Published Online: May-August 2025

Pages: 52-60

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References

[1] Concept of a Numerical Forecast Model. Accessed: Aug. 10, 2023. [Online]. Available:
http://web.kma.go.kr/aboutkma/intro/supercom/model/model_concept.jsp
[2] P. Davis, C. Ruth, A. A. Scaife, and J. Kettleborough, ‘‘A large ensemble seasonal forecasting system: GloSea6,’’ Dec. 2020, vol. 2020.
[3] M. Howison, Q. Koziol, D. Knaak, J. Mainzer, and J. Shalf, ‘‘Tuning HDF5 for Lustre file systems,’’ Lawrence Berkeley Nat. Lab., Berkeley,
CA, USA, Tech. Rep. LBNL-4803E, 2010.
[4] Babak Behzad, Huong Luu, AstraZeneca, Joseph Huchette ndSuren Byna, ‘‘Taming parallel I/O complexity with auto-tuning,’’ in Proc.
Int. Conf. High Perform. Comput., Netw., Storage Anal., 2013,p. 68.
[5] S. Robert, S. Zertal, and G. Goret, ‘‘Auto-tuning of IO accelerators using black-box optimization,’’ in Proc. Int. Conf. High Perform.
Comput. Simulation (HPCS), Jul. 2019, pp. 1022–1027, doi: 10.1109/HPCS48598.2019.9188173.
[6] A. Bağbaba, X. Wang, C. Niethammer, and J. Gracia, ‘‘Improving the I/O performance of applications with predictive modeling based
auto-tuning,’’ in Proc. Int. Conf. Eng. Emerg. Technol. (ICEET), Oct. 2021.doi:10.1109/ICEET53442.2021.9659711.
[7] S. Valcke and R. Redler, ‘‘The OASIS coupler,’’ in Earth System Modelling, vol. 3. Berlin, Germany: Springer, 2012, pp. 23–32,
doi:10.1007/978-3-642-23360-9_4.
[8] P. Carns, R. Latham, R. Ross, K. Iskra, S. Lang, and K. Riley, ‘‘24/7 characterization of petascale I/O workloads,’’ in Proc. 2009 Workshop
Interfaces Archit. Sci. Data Storage, Sep. 2009, pp. 1–10.
[9] SAHBI BOUBAKER 1,2, MOHAMED BENGHANEM³, ADEL MELLIT 4,5, AYOUB LEFZA4, OMAR KAHOULI, AND LIQUA KOLSI6,
"Deep Neural Networks for Predicting Solar Radiation at Hail Region, Saudi Arabia," IEEE Access, vol., pp., 2021.
[10] Desirée Arias-Requejo 1,2,3,4, Belarmino Pulido4, (Member, IEEE), Marcus M. Keane 1,2,3, And Carlos J. Alonso-González4, "Clustering
and Deep-Learning for Energy Consumption Forecast in Smart Buildings," IEEE Access, vol. , pp. , 2023.
[11] M. M. Hassan, M. A. T. Rony, M. A. R. Khan, M. M. Hassan, F. Yasmin, A. Nag, T. H. Zarin, A. K. Bairagi, S. Alshathri, and W. El-Shafai,
"Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness," IEEE Access, vol. 11,
pp. 3333876, 2023.
[12] C. Zoremsanga and J. Hussain, "Particle Swarm Optimized Deep Learning Models for Rainfall Prediction: A Case Study in Aizawl,
Mizoram," IEEE Access, vol. 12, pp. 3390781, 2024
[13] S. Wang, Y. Li, B. Yang, and R. Duan, "Short-Term Forecasting of Convective Weather Affecting Civil Aviation Operations Using Deep
Learning," IEEE Access, vol. 12, pp. 116603-116614, 2024.
[14] ABDULMAJID LAWAL 1, SHAFIQUR REHMAN 2,3, LUAI M. ALHEMS2, AND MD. MAHBUB ALAM 4, "Wind Speed Prediction Using
Hybrid 1D CNN and BLSTM Network," IEEE Access, vol. 9, pp. 156677 - 156687, 2021
[15] Shanmin Yang, Qing Ren, Ningfang Zhou, Yan Zhang, and Xi Wu, "Deep Learning for Near-Surface Air Temperature Estimation From
FengYun 4A Satellite Data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, 2024, pp. 13108-
13118.
[16] A. Saeed, C. Li, M. Danish, S. Rubaiee, G. Tang, Z. Gan, and A. Ahmed, "Hybrid Bidirectional LSTM Model for Short-Term Wind Speed
Interval Prediction," IEEE Access, vol. 8, pp. 182283–182294, Oct. 2020, doi: 10.1109/ACCESS.2020.3027977.
[17] M. Moishin, R. C. Deo, R. Prasad, N. Raj, and S. Abdulla, "Designing Deep-Based Learning Flood Forecast Model With ConvLSTM
Hybrid Algorithm," IEEE Access, vol. 9, pp. 50982–50997, Apr. 2021, doi: 10.1109/ACCESS.2021.3065939.
[18] Y. Dong, S. Ma, H. Zhang, and G. Yang, "Wind Power Prediction Based on Multi-Class Autoregressive Moving Average Model with
Logistic Function," Journal of Modern Power Systems and Clean Energy, vol. 10, no. 5, pp. 1184–1194, Sept. 2022, doi:
10.35833/MPCE.2021.000717.[19] Y.-X. Wu, Q.-B. Wu, and J.-Q. Zhu, "Data-Driven Wind Speed Forecasting Using Deep Feature Extraction and LSTM," IET Renewable
Power Generation, vol. 13, no. 12, pp. 2062–2069, Dec. 2019, doi: 10.1049/iet-rpg.2018.5917.
[20] H. Rezaie, C. H. Chung, and N. Safari, “Short-Term Wind Forecasting With Optimized EEMD-LSTM Model,” J. Energy Eng., vol. 149,
no. 1, 2023.
[21] C. Zoremsanga and J. Hussain, “Rainfall Prediction Using Particle Swarm Optimized Deep Learning Models,” in Proc. IEEE ICECC,
2022.
[22] X. Zhang et al., "StHCFormer: A Multivariate Ocean Weather Predicting Method Based on Spatiotemporal Hybrid Convolutional Attention
Networks," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 3610-3626, 2024.
[23] M. Biscarini, R. Nebuloni, L. Dossi, et al., “Using Short-Term NWP for Attenuation Series Synthesis in Q-Band,” IEEE Trans. Antennas
Propag., vol. 72, no. 5, pp. 7699–7708, May 2024.
[24] 24 A. Dolatabadi, H. Abdeltawab, and Y. A.-R. I. Mohamed, "Hybrid Deep Learning-Based Model for Wind Speed Forecasting Based on
DWPT and Bidirectional LSTM Network," IEEE Access, vol. 8, pp. 229219–229233, Dec. 2020, doi: 10.1109/ACCESS.2020.3047077.
[25] Y. Shrestha, Y. Zhang, G. M. McFarquhar, W. Blake, M. Starzec, and S. D. Harrah, "Development of Simulation Models Supporting Next-
Generation Airborne Weather Radar for High Ice Water Content Monitoring," IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, vol. 16, pp. 493–508, 2023, doi: 10.1109/JSTARS.2022.3227124.
[26] A. Bojesomo, H. AlMarzouqi, and P. Liatsis, “A Novel Transformer Network With Shifted Window Cross-Attention for Spatiotemporal
Weather Forecasting,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 17, pp. 45–56, 2024.
[27] J. Kim and S. Kim, “A Study on Estimating Theme Park Attendance Using the AdaBoost Algorithm Based on Weather Information,” J.
Web Eng., vol. 23, no. 6, pp. 869–884, 2024.
[28] N. Wedi et al., “Destination Earth: High-Performance Computing for Weather and Climate,” Comput. Sci. Eng., vol. 24, no. 6, pp. 29–37,
Nov.–Dec. 2022.
[29] Jinkook Kim and Soohyun Kim, "A Study on Estimating Theme Park Attendance Using the AdaBoost Algorithm Based on Weather
Information from the Korea Meteorological Administration Web," Journal of Web Engineering, Vol. 23_6, 869-884, 2024.
[30] Z. Tang, J. Liu, J. Ni, J. Zhang, P. Zeng, P. Ren, and T. Su, "Power Prediction of Wind Farm Considering the Wake Effect and its Boundary
Layer Compensation," IEEE Access, vol. 8, pp. 1234-1244, 2024.
[31] J. Montaña, C. Valle, S. Rosales, R. Schurch, and D. Pozo, "Predicting Algorithm of Thunderstorm Days in the Northern Region of Chile
Using Convolution Neural Network," IEEE Access, vol. 12, pp. 3445320, 2024.
[32] Y. Zhou, Y. Sun, S. Wang, R. J. Mahfoud, D. Hou, and J. Wang, "Very Short-term Probabilistic Prediction for Regional Wind Power
Generation Based on OPNPIS," CSEE Journal of Power and Energy Systems, doi: 10.17775/CSEEJPES.2022.02790.
[33] J. Lee, J. Kang, S. Son, and H. -M. Oh, "Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model
Approach," IEEE Access, vol. 13, pp. 3525659, 2025.
[34] J. Faraji, A. Ketabi, H. Hashemi-Dezaki, M. Shafie-Khah, and J. P. S. Catalão, "Optimal Day-Ahead Self-Scheduling and Operation of
Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting," IEEE Access, vol. 8, pp. 3019562, 2020.
[35] N. Uthayansuthi and P. Vateekul, "Optimization of Peer-to-Peer Energy Trading With a Model-Based Deep Reinforcement Learning in a
Non-Sharing Information Scenario," IEEE Access, vol. 12, pp. 3442445, 2024.
[36] S. Choi and E. -S. Jung, "Optimizing Numerical Weather Prediction Model Performance Using Machine Learning Techniques," IEEE
Access, vol. 11, pp. 3297200, 2023.
[37] C. Zoremsanga and J. Hussain, "Particle Swarm Optimized Deep Learning Models for Rainfall Prediction: A Case Study in Aizawl,
Mizoram," IEEE Access, vol. 12, pp. 3390781, 2024.
[38] C. Senogz, S. Ramanna, S. Kehler, R. Goomer, and P. Pries, "Machine Learning Approaches to Improve North American Precipitation
Forecasts," IEEE Access, vol. 11, pp. 3309054, 2023.
[39] M. M. Hassan, M. A. T. Rony, M. A. R. Khan, M. M. Hassan, F. Yasmin, A. Nag, T. H. Zarin, A. K. Bairagi, S. Alshathri, and W. El-Shafai,
"Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness," IEEE Access, vol. 11, pp.
3333876, 2023.
[40] M. Zhao and X. Zhou, "Multi-Step Short-Term Wind Power Prediction Model Based on CEEMD and Improved Snake Optimization
Algorithm," IEEE Access, vol. 12, pp. 3385643, 2024.
[41] S. Surendran, M. V. Ramesh, A. Montresor, and M. J. Montag, "Link Characterization and Edge-Centric Predictive Modeling in an Ocean
Network," IEEE Access, vol. 11, pp. 3235387, 2023.
[42] M. M. Hassan, M. A. T. Rony, M. A. R. Khan, M. M. Hassan, F. Yasmin, A. Nag, T. H. Zarin, A. K. Bairagi, S. Alshathri, and W. El-Shafai,
"Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness," IEEE Access, vol. 11,
pp. 3333876, 2023.
[43] CHENG LYU, (Graduate Student Member, IEEE), AND SARA EFTEKHARNEJAD, (Senior Member, IEEE), "Probabilistic Solar
Generation Forecasting for Rapidly Changing Weather Conditions," IEEE Access, vol., pp., 2024.
[44] CHRISTIAN GIANOGLIO 1, (Member, IEEE), SARA ZANI 2, MATTEO COLLI 2, AND DANIELE D. CAVIGLIA 1, (Life Member, IEEE),
"Rainfall Classification in Genoa: Machine Learning Versus Adaptive Statistical Models Using Satellite Microwave Links," IEEE Access,
vol. 12, pp. 132743 - 132752, 2024.
[45] NANA KOFI AHOI APPIAH-BADU1.2, YAW MARFO MISSAH¹, LEONARD K. AMEKUDZI³, NAJIM USSIPH¹, TWUM FRIMPONG¹,
AND EMMANUEL AHENE1, "Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana," IEEE
Access, vol. 10, pp. 5082 - 5095, 2022.
[46] Yihe Zhang, Bryce Turney, Purushottam Sigdel, Xu Yuan, Eric Rappin, Adrian L. Lago, Sytske Kimball, Li Chen, Paul Darby, Lu Peng,
Sercan Aygun, Yazhou Tu, M. Hassan Najafi, and Nian-Feng Tzeng, "Regional Weather Variable Predictions by Machine Learning With
Near-Surface Observational and Atmospheric Numerical Data," IEEE Transactions on Geoscience and Remote Sensing, vol. 63, 2025, pp.
1-16.
[47] M. M. Asiri, G. Aldehim, F. A. Alotaibi, M. M. Alnfiai, M. Assiri, and A. Mahmud, “Short-Term Load Forecasting in Smart Grids Using
Hybrid Deep Learning,” IEEE Access, vol. 12, pp. 23504–23512, Jan. 2024, doi: 10.1109/ACCESS.2024.3358182.
[48] H. Kim, S. Park, H.-J. Park, H.-G. Son, and S. Kim, “Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model,” IEEE
Access, vol. 11, pp. 13492–13498, Feb. 2023, doi: 10.1109/ACCESS.2023.3243252.
[49] S. Wang, Y. Li, B. Yang, and R. Duan, "Short-Term Forecasting of Convective Weather Affecting Civil Aviation Operations Using Deep
Learning," IEEE Access, vol. 12, pp. 116603-116614, 2024.[50] S. Tsegaye, S. Padmanaban, L. B. Tjernberg, and K. A. Fante, "Short-Term Load Forecasting for Electrical Power Distribution Systems
Using Enhanced Deep Neural Networks," IEEE Access, vol. 12, pp. 186871-186884, 2024.
[51] M. Yang, K. Wang, X. Su, M. Ma, G. Wu, and D. Huang, "Short-Term Photovoltaic Output Probability Prediction Method Considering the
Spatio-Temporal-Conditional Dependence of Prediction Error," DOI: 10.17775/CSEEJPES.2022.02360.
[52] T. Lin and R. Lin, "Smart City Traffic Flow and Signal Optimization Using STGCN-LSTM and PPO Algorithms," IEEE Access, vol. 13,
pp. 15078-15091, 2025.
[53] A. Guo, Y. Liu, S. Shao, X. Shi, and Z. Feng, "Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather
Forecasting," IEEE Access, vol. 13, pp. 15824-15833, 2025.
[54] X. Zhang et al., "StHCFormer: A Multivariate Ocean Weather Predicting Method Based on Spatiotemporal Hybrid Convolutional Attention
Networks," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 3610-3626, 2024.
[55] Ş. Özdemir, Y. Demir, and Ö. Yıldırım, "The Effect of Input Length on Prediction Accuracy in Short-Term Multi-Step Electricity Load
Forecasting: A CNN-LSTM Approach," IEEE Access, vol. 13, pp. 28432-28443, 2025.
[56] T. A. Gahwera, O. S. Eyobu, M. Isaac, S. Kakuba, and D. S. Han, "Transfer Learning-Based Ensemble Approach for Rainfall Class Amount
Prediction," IEEE Access, vol. 13, pp. 48334-48344, 2025.
[57] S. Wang and O. Xu, "Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning," IEEE Access, vol. 12, pp.
3384066-3384077, 2024.
[58] M. K. Saravana, M. S. Roopa, J. S. Arunalatha, and K. R. Venugopal, “Temporal Harmony: Bridging Gaps in Multivariate Time Series
Data with GAN-Transformer Integration,” in Proc of the 2024 IEEE Int. Conf. Interdisciplinary Approaches in Technology and
Management for Social Innovation (IATMSI), vol. 2, pp. 1–6, 2024. IEEE
[59] M. K. Saravana, M. S. Roopa, J. S. Arunalatha, and K. R. Venugopal, “Navigating Data Scarcity in Multivariate Time Series Forecasting:
A Hybrid Model Perspective,” in Proceedings of the 2024 IEEE Region 10 Symposium (TENSYMP), pp. 1–7, 2024. IEEE
[60] M. K. Saravana, M. S. Roopa, J. S. Arunalatha, and K. R. Venugopal, “Graph Laplacian Eigenvalues Empowered VAEs: A Novel Approach
to Adaptive Latent Dimension Choice,” IEEE Access, vol. 12, pp. 135265–135282, 2024, doi: 10.1109/ACCESS.2024.3460971.
[61] M. K. Saravana, M. S. Roopa, J. S. Arunalatha, and K. R. Venugopal, “Unsupervised MTS Anomaly Detection with Variational
Autoencoders,” in Lecture Notes in Networks and Systems, in Proceedings of the 4th Int. Conf. Front. Comput. Syst., Singapore, 2024, vol.
974, pp. 219–236, Springer Nature Singapore.

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