University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 55 11 2025 01 20 Evaluation of the WRF local and regional IFS numerical model in precipitation estimation Evaluation of the WRF local and regional IFS numerical model in precipitation estimation 2017 2033 100308 10.22059/ijswr.2024.376017.669704 FA Sakine Koohi Water Engineering Dept./ Imam Khomeini International University, Qazvin, Iran Asghar Azizian Assistant Professor in Water Engineering Department/ Imam Khomeini International University Mohammad Saeid Najafi Water Resources Research (WRR) Department, Ministry of Energy, Water Research Institute (WRI), Tehran, Iran Journal Article 2024 05 02 In recent years, numerous numerical models have been developed to simulate atmospheric variables such as precipitation. This study aims to assess the efficacy of the Weather Research and Forecasting (WRF) model and the Integrated Forecast System (IFS) numerical system in simulating precipitation within the Poldokhtar Basin. The findings revealed that the WRF model exhibited a stronger correlation with observed precipitation values in the 6-hour time step (The average CC of WRF for the events of 2016 and 2018 is equal to 0.49 and for the IFS system in 2016, 0.43, in 2018, 0.15), whereas the IFS system demonstrated a higher correlation with observational data over longer time steps (The average CC in the 24-hour time step in 2016 and 2018 for the WRF model is 0.72 and 0.60, respectively, and for the IFS system, it is 0.75 and 0.70, respectively). Based on the NRMSE error-index, the average NRMSE in time steps of 6, 12, and 24 hours for the WRF model is 0.98, 0.86, and 0.67 mm (2016), 0.97, 0.72, and 0.75 mm (2018), respectively and for IFS numerical system is 1.01, 0.80 and 0.66 mm (2016) and 1.20, 0.76 and 0.79 mm (2018) respectively. Additionally, in the 24-hour time step, the results from the IFS numerical system closely resembled those obtained from the WRF model. Thus, the model's daily predictions can be utilized with higher confidence levels. It is imperative to note that the implementation of bias correction techniques is essential for mitigating the output errors in numerical weather forecasting models. In recent years, numerous numerical models have been developed to simulate atmospheric variables such as precipitation. This study aims to assess the efficacy of the Weather Research and Forecasting (WRF) model and the Integrated Forecast System (IFS) numerical system in simulating precipitation within the Poldokhtar Basin. The findings revealed that the WRF model exhibited a stronger correlation with observed precipitation values in the 6-hour time step (The average CC of WRF for the events of 2016 and 2018 is equal to 0.49 and for the IFS system in 2016, 0.43, in 2018, 0.15), whereas the IFS system demonstrated a higher correlation with observational data over longer time steps (The average CC in the 24-hour time step in 2016 and 2018 for the WRF model is 0.72 and 0.60, respectively, and for the IFS system, it is 0.75 and 0.70, respectively). Based on the NRMSE error-index, the average NRMSE in time steps of 6, 12, and 24 hours for the WRF model is 0.98, 0.86, and 0.67 mm (2016), 0.97, 0.72, and 0.75 mm (2018), respectively and for IFS numerical system is 1.01, 0.80 and 0.66 mm (2016) and 1.20, 0.76 and 0.79 mm (2018) respectively. Additionally, in the 24-hour time step, the results from the IFS numerical system closely resembled those obtained from the WRF model. Thus, the model's daily predictions can be utilized with higher confidence levels. It is imperative to note that the implementation of bias correction techniques is essential for mitigating the output errors in numerical weather forecasting models. https://ijswr.ut.ac.ir/article_100308_718e973086d1e8d11251b588c2116ace.pdf