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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 |
| Shahrekord University |
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Abstract: (7 Views) |
The prediction of precipitation is of great importance in flood and water resource studies. In recent years, numerical models for weather prediction have been widely developed and are now utilized by famous meteorological centers in their weather forecasts. The TIGGE database has also leveraged these models for its weather forecasts. This study aimed to investigate the effect of seven bias correction methods on precipitation prediction models available in the TIGGE database for the Poldokhtar watershed. Additionally, the performance of ten precipitation prediction models in the TIGGE database was evaluated. The results showed that most prediction models had the lowest root mean square error (RMSE) at low altitudes and the highest value at high altitudes. Among the ten models examined in the TIGGE database, the ECMWF model had the lowest RMSE of about 5 millimeters per day in the Poldokhtar watershed. In contrast, precipitation data predicted by the NCMRWF center had the highest RMSE of about 8.8 millimeters per day. Moreover, the evaluation of corrected precipitation showed that the EZ, STB, and QM factors showed superiority at some of the stations in the examined models. On the other hand, the Delta method did not have a geographical region effect and was effective in all examined models. The LS and EQM factors could not correct the numerical models' precipitation prediction and did not affect the predictions. The corrections of the TVSV factor also showed significant differences in observed precipitation. Based on this study, post-processing of predicted precipitation in numerical models increases the efficiency of precipitation prediction models in flood warning systems.
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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: 2026/06/23 | Accepted: 2026/06/27 | ePublished ahead of print: 2026/07/31 | ePublished: 2026/07/31
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