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Bias correction of numerical precipitation forecasting model outputs from the TIGGE database
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Soudabeh Behiyan Motlagh *    , Afshin Honarbakhsh |
| Department of Natural Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord |
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Abstract: (171 Views) |
| Accurate precipitation forecasting is essential for flood forecasting and water resources management. In recent years, numerical weather prediction (NWP) models have been extensively developed and are widely used by major meteorological centers for operational weather forecasting. The TIGGE database integrates forecasts from these models, providing a valuable resource for evaluating precipitation prediction performance. This study investigated the effectiveness of seven bias-correction methods applied to precipitation forecasts generated by ten NWP models available in the TIGGE database for the Poldokhtar watershed. The results showed that most forecasting models exhibited the lowest root mean square error (RMSE) at low elevations and the highest RMSE at high elevations. Among the ten models evaluated, the ECMWF model achieved the lowest RMSE (approximately 5 mm day⁻¹), whereas the NCMRWF model produced the highest RMSE (approximately 8.8 mm day⁻¹). Evaluation of the bias-corrected precipitation forecasts indicated that the EZ, STB, and QM methods outperformed the other correction methods at several stations across the study area. In contrast, the Delta method consistently improved forecast performance across all evaluated models, regardless of geographical location. The LS and EQM methods showed little to no improvement in precipitation forecasts, while the TVSV method produced corrected precipitation values that differed considerably from the observed precipitation. Overall, the results demonstrate that post-processing of precipitation forecasts can substantially improve the performance of numerical weather prediction models, thereby enhancing their applicability for flood forecasting and early warning systems. |
Article number: 1 |
| Keywords: Error correction, Numerical weather prediction model, Post-processing, Delta bias correction factor, QM bias correction factor |
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Type of Study: Research |
Subject:
Special
Received: 2026/04/23 | Revised: 2026/08/17 | Accepted: 2026/06/27 | ePublished ahead of print: 2026/07/31 | ePublished: 2026/07/31
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