Evaluation and Bias Correction of ECMWF Extended-Range Precipitation Forecasts over the Confluence of Asian Monsoons and Westerlies Using the Linear Scaling Method

This study evaluates and corrects ECMWF precipitation forecasts (Set VI-ENS extended) over the confluence of Asian monsoons and westerlies, deriving a time series of correction factors for medium- and long-term hydrological forecasting. Based on a 15-year dataset (2008–2023), a dominant spatial and temporal bias pattern was identified: ~50% of the study area—in particular, the entire Tibetan Plateau—experienced overestimated precipitation, with larger relative errors in dry seasons than in wet seasons. Daily correction factors were derived using the linear scaling method and applied to distributed hydrological models for the Mekong, Salween, and Brahmaputra river basins. The results demonstrated substantial efficacy in correcting streamflow forecasts, particularly in the Brahmaputra basin at the Nuxia station, where the relative error in the total water volume over a 32-day period was reduced from 25% to 10% during the calibration period (2008–2020) and from 20% to 9% in the validation period (2021–2023). Furthermore, over 90% (calibration) and 85% (validation) of hydrological forecast events were successfully corrected at Nuxia. Comparable improvements were observed in key stations across the Salween and Mekong basins, with the combined success rates exceeding 70% and 65%, demonstrating the method’s regional robustness. Challenges remain in areas with weak linear relationships between forecasted and observed data, highlighting the need for further investigation.

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Publication Details

Journal
Hydrology
Published
2025-08-18
DOI
https://doi.org/10.3390/hydrology12080218
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
0.00

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article

Evaluation and Bias Correction of ECMWF Extended-Range Precipitation Forecasts over the Confluence of Asian Monsoons and Westerlies Using the Linear Scaling Method

Keer Zhang, Haoyang Lyu, Fuqiang Tian, Mahmut Tudaji
Hydrology
Meteorological Phenomena and Simulations
article

Evaluation and Bias Correction of ECMWF Extended-Range Precipitation Forecasts over the Confluence of Asian Monsoons and Westerlies Using the Linear Scaling Method

Keer Zhang, Haoyang Lyu, Fuqiang Tian, Mahmut Tudaji
article en

Abstract

This study evaluates and corrects ECMWF precipitation forecasts (Set VI-ENS extended) over the confluence of Asian monsoons and westerlies, deriving a time series of correction factors for medium- and long-term hydrological forecasting. Based on a 15-year dataset (2008–2023), a dominant spatial and temporal bias pattern was identified: ~50% of the study area—in particular, the entire Tibetan Plateau—experienced overestimated precipitation, with larger relative errors in dry seasons than in wet seasons. Daily correction factors were derived using the linear scaling method and applied to distributed hydrological models for the Mekong, Salween, and Brahmaputra river basins. The results demonstrated substantial efficacy in correcting streamflow forecasts, particularly in the Brahmaputra basin at the Nuxia station, where the relative error in the total water volume over a 32-day period was reduced from 25% to 10% during the calibration period (2008–2020) and from 20% to 9% in the validation period (2021–2023). Furthermore, over 90% (calibration) and 85% (validation) of hydrological forecast events were successfully corrected at Nuxia. Comparable improvements were observed in key stations across the Salween and Mekong basins, with the combined success rates exceeding 70% and 65%, demonstrating the method’s regional robustness. Challenges remain in areas with weak linear relationships between forecasted and observed data, highlighting the need for further investigation.

HydrologyVol. 12(8)
Nanjing Forestry University (CN), State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (CN), Tsinghua University (CN)
National Oceanic and Atmospheric Administration, National Key Research and Development Program of China
Openalex Percentile: Top 74%
Meteorological Phenomena and Simulations
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