ERA5–NASA Ensembles for Daily Rain Prediction Supporting Irrigation in Konya, Türkiye

Year 2025, Volume: 9 Issue: Special, 153 - 161, 28.12.2025

Ali Çetinkaya

https://doi.org/10.31015/2025.si.23

Abstract

Reliable daily precipitation prediction is pivotal for agricultural water management in semi-arid regions, where water scarcity and climate variability raise operational risk. This study develops and evaluates machine-learning approaches for precipitation occurrence in Konya, Türkiye - one of the country’s key agricultural basins - using 44 years (1980–2024) of meteorological data. We examine three data strategies: (i) European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) reanalysis features alone, (ii) National Aeronautics and Space Administration (NASA) Prediction Of Worldwide Energy Resources (POWER) observations alone, and (iii) a combined strategy that merges ERA5 predictors with the NASA rain/no-rain target. Five model families are compared— Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and a three-layer Long Short-Term Memory (LSTM) on analysis-ready tabular datasets. All approaches use advanced feature engineering (cyclical seasonality, lags, rolling windows) and a deployment-minded post-processing step that scans decision thresholds from 0.10 to 0.90 to maximize F1‑score. Performance is assessed with a chronological train/test split; F1‑score is the primary metric, complemented by Area Under the Receiver Operating Characteristic Curve (AUC-ROC), precision, recall, confusion matrices, and calibration. Results show that the combined strategy with CatBoost delivers the highest skill (F1‑score: 84.57%; AUC-ROC: 94.37%), confirming that pairing rich ERA5 features with the quality-controlled NASA target improves performance relative to single-source approaches. Ensemble tree-based methods consistently outperform the LSTM baseline on this daily, tabular classification task. Threshold optimization raises F1‑score by about 1–5% across models, and calibration indicates that predicted probabilities closely match observed frequencies. For semi-arid farming in Konya, calibrated daily probabilities of precipitation can be converted into simple decision rules (e.g., skip irrigation when the predicted probability exceeds a locally selected threshold), supporting irrigation scheduling, fertilizer timing, and harvest planning. The workflow is computationally efficient and reproducible, built on globally available ERA5 and NASA POWER resources, and readily transferable to other semi-arid basins with minor re-tuning. Findings highlight a practical path to trustworthy, probability-based rainfall guidance for agricultural water management. Feature importance analysis highlights seasonality, humidity, temperature ranges, pressure tendencies, and wind extremes as leading signals.

Keywords

Agricultural water management , ERA5 , Machine learning , Model calibration , NASA POWER , Precipitation prediction

References

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
• Bzdok, D., Altman, N., & Krzywinski, M. (2018). Statistics versus machine learning. Nature Methods, 15(4), 233-234. https://doi.org/10.1038/nmeth.4642
• C3S (2017). ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Data Store. https://cds.climate.copernicus.eu
• Caló, F., Notti, D., Galve, J. P., Abdikan, S., Görüm, T., Pepe, A., & Balik Şanli, F. (2017). DInSAR-Based Detection of Land Subsidence and Correlation with Groundwater Depletion in Konya Plain, Turkey. Remote Sensing, 9(1), 83. https://doi.org/10.3390/rs9010083
• Chen, G., & Wang, W.-C. (2021). Short-term precipitation prediction using deep learning. arXiv preprint arXiv:2110.01843. https://doi.org/10.48550/arXiv.2110.01843
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD Conference, 785-794. https://doi.org/10.1145/2939672.2939785
• Choi, C., Kim, J., Kim, J., Kim, D., Bae, Y., & Kim, H. S. (2018). Development of heavy rain damage prediction model using machine learning based on big data. Advances in Meteorology. https://doi.org/10.1155/2018/3857645
• Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv:1810.11363.
• Dutra, E., Muñoz-Sabater, J., Boussetta, S., Komori, T., Hirahara, S., & Balsamo, G. (2020). Environmental lapse rate for high-resolution land surface downscaling: An application to ERA5. Earth and Space Science, 7(5), e2019EA000984. https://doi.org/10.1029/2019EA000984
• ECMWF (Haiden, T., et al.) (2023). Evaluation of ECMWF forecasts, including the 2023 upgrade. ECMWF Technical Memorandum No. 911.
• Fente, D. N., & Singh, D. K. (2018). Weather forecasting using artificial neural network. In Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (pp. 1757-1761).
• García-Feal, O., et al. (2022). A machine learning approach for reservoir outflow forecasting-A comparison of data-driven techniques. Natural Hazards and Earth System Sciences, 22, 2247-2266. https://doi.org/10.5194/nhess-22-2247-2022
• Gavahi, K., et al. (2023). A deep learning-based framework for multi-source precipitation fusion. Remote Sensing of Environment, 289, 113573. https://doi.org/10.1016/j.rse.2023.113573
• Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477), 359-378. https://doi.org/10.1198/016214506000001437
• Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? NeurIPS Datasets & Benchmarks. https://doi.org/10.5555/3600270.3600307
• Hersbach, H., Bell, B., Berrisford, P., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. https://doi.org/10.1002/qj.3803
• Hung, N. Q., Babel, M. S., Weesakul, S., & Tripathi, N. K. (2009). An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences, 13, 1413-1425.
• Izadi, N., Habibi, M., Farjad, B., & Hashemi-Monfared, S. A. (2021). Evaluation of ERA5 precipitation accuracy based on meteorological observations in different precipitation zones of Iran. Water, 13, 3188. https://doi.org/10.3390/w13223188
• Karevan, Z., & Suykens, J. A. K. (2018). Spatio-temporal stacked LSTM for temperature prediction in weather forecasting.
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. NeurIPS 30, 3146-3154.
• Khalilzadeh, Z., & Wang, L. (2021). An MILP model for corn planting and harvest scheduling considering storage capacity and growing degree units. bioRxiv.
• Lee, J., Hong, S., & Lee, J. H. (2014). An efficient prediction for heavy rain from big weather data using genetic algorithm. In IMCOM (ICUIMC) 2014 (pp. 1-7).
• Lelieveld, J., Hadjinicolaou, P., Kostopoulou, E., Chenoweth, J., Maayar, M., Giannakopoulos, C., ... Xoplaki, E. (2012). Climate change and impacts in the Eastern Mediterranean and the Middle East. Climatic Change, 114(3-4), 667-687. https://doi.org/10.1007/s10584-012-0418-4
• Lin, X., Negenborn, R. R., & Lodewijks, G. (2018). Predictive quality-aware control for scheduling of potato starch production. Computers and Electronics in Agriculture, 266-278.
• Luk, K. C., Ball, J. E., & Sharma, A. (2001). An application of artificial neural networks for rainfall forecasting. Mathematical and Computer Modelling, 33, 683-693.
• McGovern, A., Ebert-Uphoff, I., Gagne, D. J., & Bostrom, A. (2022). Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science. Environmental Data Science, 1, e6. https://doi.org/10.1017/eds.2022.5
• Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., et al. (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349-4383. https://doi.org/10.5194/essd-13-4349-2021
• NASA LRC (2025). NASA Prediction of Worldwide Energy Resources (POWER) Project. https://power.larc.nasa.gov
• Powers, D. M. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37-63.
• Rasp, S., Pritchard, M. S., & Gentine, P. (2018). Deep learning to represent subgrid processes in climate models. Proceedings of the National Academy of Sciences, 115(39), 9684-9689. https://doi.org/10.1073/pnas.1810286115
• Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10(3), e0118432.
• Samasti, M., & Kucukdeniz, T. (2023). Precipitation forecast with logistic regression methods for harvest optimization. International Journal of Agriculture, Environment and Food Sciences, 7(1), 213-222. https://doi.org/10.31015/jaefs.2023.1.26
• Schultz, M. G., Betancourt, C., Gong, B., et al. (2021). Can deep learning beat numerical weather prediction? Philosophical Transactions of the Royal Society A, 379(2194), 20200097. https://doi.org/10.1098/rsta.2020.0097
• Sparks, A. H. (2018). nasapower: A NASA POWER global meteorology, surface solar energy and climatology data client for R. Journal of Open Source Software, 3(30), 1035. https://doi.org/10.21105/joss.01035
• White, J. W., Hoogenboom, G., Stackhouse Jr, P. W., & Hoell, J. M. (2008). Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US. Agricultural and Forest Meteorology, 148(10), 1574-1584. https://doi.org/10.1016/j.agrformet.2008.05.017
• White, J. W., Hoogenboom, G., Wilkens, P. W., Stackhouse Jr, P. W., & Hoell, J. M. (2011). Evaluation of satellite-based, model-derived daily solar radiation data for the continental United States. Agronomy Journal, 103(4), 1242-1251. https://doi.org/10.2134/agronj2011.0038