Advanced Weather Forecasting

Using Artificial Intelligence to revolutionize weather predictions across Africa through cutting-edge machine learning techniques and atmospheric modeling.

Our Research

We are a multidisciplinary team advancing atmospheric science through AI and machine learning, developing innovative solutions for weather prediction, climate modeling, and environmental monitoring.

Temperature Forecasting

Leveraging machine learning techniques with ERA5 reanalysis data to improve temperature predictions. Our spatio-temporal models provide 48-hour forecasts over Southwestern Nigeria with 99.26% accuracy, essential for heatwave detection and mitigation.

Work in Progress

Precipitation Modeling

Developing advanced precipitation forecasting models using similar machine learning approaches. Our research focuses on extreme rainfall event prediction with enhanced accuracy and lead time for flood management and agricultural planning.

Coming Soon

Drought Prediction

Building comprehensive drought prediction and monitoring systems using our successful temperature forecasting methodology. This research will provide crucial early warning systems for agricultural and water resource management.

Our Results

Step into our data laboratory! These living visualizations capture our models as they learn, adapt, and predict with real-time findings from our collaborative research hub.

Air Temperature Forecast over SW Nigeria

Our spatio-temporal model provides 48-hour forecasts over Southwestern Nigeria, useful for detecting and mitigating heatwave impacts. Compare our ML model with CAMS predictions over West Africa.

48

48HR Ahead Prediction

48-hour lead-time prediction for disaster planning and early warning systems.

MAP

Spatial Coverage

Complete coverage of Southwestern Nigeria region with high spatial resolution.

99%

High Accuracy

99.26% temperature prediction accuracy validated against observations.

Our ML Model Forecast

CAMS Forecast (West Africa)

CMIP6 Weather Prediction

Advanced climate modeling using CMIP6 data for enhanced weather prediction capabilities and long-term climate projections across multiple variables and timeframes.

CMIP6 Precipitation Analysis

CMIP6 Precipitation

CMIP6 Temperature Analysis

CMIP6 Temperature

Our Team

Meet the brilliant minds driving innovation in climate science and machine learning research.

Dr. S. T. Ogunjo

Lead Researcher & Senior Lecturer
Federal University of Technology Akure

Ph.D. in Condensed Matter Physics (2019). Specializes in theoretical physics with applications to climate change, space physics, and secure communication. Over 80 published articles indexed in Scopus with expertise in machine learning applications to atmospheric sciences.

Google Scholar Profile

Dr. Ojo Samuel

Atmospheric Physics Researcher
Federal University of Technology Akure

Ph.D. in Atmospheric Physics. Specializes in climate dynamics, atmospheric radiation, climate change adaptation, and mitigation. Active member of Nigerian Institute of Physics, URSI-NG, and serves as Secretary of the Atmospheric Research Group.

Research Profile

Mrs. Ijila Itunu

Data Scientist & Research Analyst
University of Hull

M.Sc. in Data Science and Analytics. Specializes in climate data analysis, statistical modeling, and machine learning applications in environmental sciences. Expert in Python, R, SQL, and advanced statistical analysis for atmospheric research.

GitHub Profile

Publications

Our peer-reviewed articles, conference proceedings, and cutting-edge research contributions to the scientific community.

Recent Journal Articles

Predicting River Discharge in the Niger River Basin: A Deep Learning Approach

Ogunjo S., Olusola A., Olusegun C. (2024)
Applied Sciences (Switzerland), Vol. 14, Issue 1, art. no. 12
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Machine learning models for prediction of rainfall over Nigeria

Ojo O.S., Ogunjo S.T. (2022)
Scientific African, Vol. 16, art. no. e01246
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Clustered adaptive neuro-fuzzy inference system models of net radiation flux using temperature series

Ojo O.S., Oladele S.O. (2022)
Journal of Applied Remote Sensing, Vol. 16, Issue 1, art. no. 014510
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Artificial neural network models for prediction of net radiation over a tropical region

Ojo O.S., Adeyemi B., Oluleye D.O. (2021)
Neural Computing and Applications, Vol. 33, Issue 12, pp. 6865 - 6877
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Conference Papers & Presentations

Forecasting tropospheric wet delay using LSTM neural network

Ogunjo S.T., Dada J.B., Ajayi O.J. (2022)
IOP Conference Series: Earth and Environmental Science, 993 (1), art. no. 012024
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Machine learning prediction of solar energy potential in Nigeria

Ogunjo S., Aderonke O., Rabiu B. (2022)
AIP Conference Proceedings, 2570, art. no. 030002
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Predicting Rainfall Onset and Cessation Within the West African Sahel Region Using Echo State Network

Olusola A., Ogunjo S., Olusegun C. (2024)
Advances in Science, Technology and Innovation, pp. 263 - 265
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Land Surface Temperature Modelling over Geoclimatic Regions of Nigeria using Soft-computing Intelligence Techniques

Ojo O.S., Adeyemi B. (2020)
2020 International Conference on Decision Aid Sciences and Application, DASA 2020, art. no. 9317199, pp. 726 - 731
View Paper →

Contact Us

Ready to collaborate? Have questions about our research? We'd love to hear from you and explore potential partnerships in climate science and AI.

Get in Touch

@

Email

info.climanovaai@gmail.com
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Location

Department of Physics
Federal University of Technology Akure
Akure, Ondo State, Nigeria

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Collaborations

Open to research partnerships and joint projects in atmospheric physics and climate modeling.

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