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<p>This study investigates the effectiveness of ensemble combining techniques in improving the accuracy of satellite-based gridded precipitation estimations in the Sirjan watershed, Iran. For this purpose, four satellite-based products (CHIRPS, MSWEP, PERSIANN-CDR, and PERSIANN-CCS-CDR) were analyzed over 25&#xa0;years (1996–2020). Various ensemble combining techniques, including Simple Model Averaging (SMA), Weighted Averaging Model (WAM), Multi-Model Super Ensemble (MMSE), and Modified MMSE (M3SE), were implemented to generate new estimations of precipitation based on satellite-based products. The performance of the ensemble combining techniques was evaluated through a series of comparative tests. The findings indicate that the MMSE and M3SE significantly improve the accuracy of precipitation estimates compared to individual products, increasing the accuracy rate up to 48% at the annual scale. The results demonstrate that the ensemble combining techniques provide a more reliable classification of wet and dry days, resulting in a 1.1–2.6% increase in true detection. This study offers a robust framework for generating more reliable precipitation estimates in non-gauge regions.</p>

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Ensemble combining techniques to improve gridded satellite-based products in precipitation estimation

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Ameneh Mianabadi,
  • Ahmad Jafarzadeh,
  • Mohsen Pourreza Bilondi,
  • Sedigheh Anvari

摘要

This study investigates the effectiveness of ensemble combining techniques in improving the accuracy of satellite-based gridded precipitation estimations in the Sirjan watershed, Iran. For this purpose, four satellite-based products (CHIRPS, MSWEP, PERSIANN-CDR, and PERSIANN-CCS-CDR) were analyzed over 25 years (1996–2020). Various ensemble combining techniques, including Simple Model Averaging (SMA), Weighted Averaging Model (WAM), Multi-Model Super Ensemble (MMSE), and Modified MMSE (M3SE), were implemented to generate new estimations of precipitation based on satellite-based products. The performance of the ensemble combining techniques was evaluated through a series of comparative tests. The findings indicate that the MMSE and M3SE significantly improve the accuracy of precipitation estimates compared to individual products, increasing the accuracy rate up to 48% at the annual scale. The results demonstrate that the ensemble combining techniques provide a more reliable classification of wet and dry days, resulting in a 1.1–2.6% increase in true detection. This study offers a robust framework for generating more reliable precipitation estimates in non-gauge regions.