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Integration of Exponential Weighted Moving Average Chart in Ensemble of Precipitation of Multiple Global Climate Models (GCMs)

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  • Muhammad Shakeel

    (University of the Punjab)

  • Zulfiqar Ali

    (University of the Punjab)

Abstract

Global Climate Models (GCMs) are globally accepted simulations used for modeling and forecasting of precipitation and other parameters of climate. However, discrepancies among GCMs reduce the scope of individual models. In this regard, a multimodel ensemble provides a more accurate picture than individual models. However, the presence of extreme values and outliers in GCMs makes ensemble inappropriate for statistical analysis and reanalysis. In this article, we propose a novel weighting scheme that integrates the Exponentially Weighted Moving Average (EWMA) chart to obtain optimum weights for ensemble means of multiple GCMs – EWMA-based Ensemble (EWMABE). To investigate the validity of EWMABE, we used precipitation data of eighteen GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this study, the performance of EWMABE is compared with Multi-Model Simple Averaging Ensemble (MMSAE). The comparative statistics show that the EWMABE scheme has significantly improved the multimodel estimates. These results suggests the potential candidacy of EWMABE for the estimation of multimodel ensemble.

Suggested Citation

  • Muhammad Shakeel & Zulfiqar Ali, 2024. "Integration of Exponential Weighted Moving Average Chart in Ensemble of Precipitation of Multiple Global Climate Models (GCMs)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(3), pages 935-949, February.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:3:d:10.1007_s11269-023-03702-x
    DOI: 10.1007/s11269-023-03702-x
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    References listed on IDEAS

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    1. Norman McFarlane, 2011. "Parameterizations: representing key processes in climate models without resolving them," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 2(4), pages 482-497, July.
    2. Farman Ali & Bing-Zhao Li & Zulfiqar Ali, 2022. "A New Weighting Scheme for Diminishing the Effect of Extreme Values in Regional Drought Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4099-4114, September.
    3. Muhammad Riaz & Saddam Akber Abbasi & Muhammad Abid & Abdulhammed K. Hamzat, 2020. "A New HWMA Dispersion Control Chart with an Application to Wind Farm Data," Mathematics, MDPI, vol. 8(12), pages 1-14, December.
    4. Sandra García-Bustos & Francisco Aparisi & Eugenio K. Epprecht, 2015. "Optimal EWMA of linear combination of Poisson variables for multivariate statistical process control," International Journal of Production Research, Taylor & Francis Journals, vol. 53(14), pages 4141-4159, July.
    5. Hadush Meresa & Bernhard Tischbein & Tewodros Mekonnen, 2022. "Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(3), pages 2649-2679, April.
    6. Zulfiqar Ali & Sadia Qamar & Nasrulla Khan & Muhammad Faisal & Saad Sh. Sammen, 2023. "A New Regional Drought Index under X-bar Chart Based Weighting Scheme – The Quality Boosted Regional Drought Index (QBRDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 1895-1911, March.
    7. Muhammad Ahmad & Zulfiqar Ali & Maryam Ilyas & Muhammad Mohsin & Rizwan Niaz, 2023. "A Common Factor Analysis Based Data Mining Procedure for Effective Assessment of 21st Century Drought under Multiple Global Climate Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4787-4806, September.
    8. Young Hoon Song & Eun-Sung Chung & Mohammed Sanusi Shiru, 2020. "Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
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    Cited by:

    1. Mojtaba Poursaeid & Amir Hossein Poursaeed & Saeid Shabanlou, 2025. "Water Resources Quality Indicators Monitoring by Nonlinear Programming and Simulated Annealing Optimization with Ensemble Learning Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 1073-1087, February.

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    Keywords

    Global climate models; Simulations; Precipitation; CMIP6;
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