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<p>Accurate forecasting of heavy rainfall event is an important issue in weather forecasting. This study is conducted on the heavy rainfall event (maximum rainfall: 335&#xa0;mm) in the western part of the Democratic People’s Republic of Korea (DPRK) on July 27, 2024, using WRF−3DVar. The sensitivity of conventional and satellite radiance data assimilation to thinning distance is first investigated. The WRF−3DVar simulation results for four thinning distances (30&#xa0;km, 60&#xa0;km, 90&#xa0;km and 120&#xa0;km) are analyzed. As a result, the simulation with a thinning distance of 60&#xa0;km shows better performance among all experiments. To evaluate the impact of the assimilation of different observations, four numerical experiments are carried out such as CTRL (without data assimilation), DA (PREP) (with assimilation of conventional observations), DA (RAD) (with assimilation of satellite observations) and DA (All) (with assimilation of combined). The simulated rainfall field in the CTRL experiment is not well captured in terms of precipitation, intensity and spatial distribution compared to the observations. In the assimilation experiments conducted to improve quality of the initial conditions, rainfall field is simulated superior to CTRL. In particular, in DA (All), precipitation closely matches observations and the threat score is higher more than 30% compared to CTRL. There was a significant improvement in the initial field compared to CTRL in DA experiments, which further improve the prediction of the equivalent potential temperature, wind field at low-level atmosphere. Typically, the simulated amounts of hydrometeors (Qr, Qc, Qg + Qi + Qs) are found to be reasonably well captured compared to those in the CTRL experiment. These results from conventional and satellite radiance data assimilation show that the improvement of the initial conditions plays a positive impact on the simulation of heavy rainfall. The validity of these results is also demonstrated by simulation of two additional heavy rainfall events (5 August 2020, 15 August 2022). This study confirms that the accuracy of heavy rainfall forecasts is improved by using WRF−3DVar and highlights the importance of choice of parameters like thinning distance in operational forecast system using 3DVar.</p>

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Impact of assimilation of conventional and satellite radiance observations on simulation of the heavy rainfall event on 27 July 2024 in the Democratic People’s Republic of Korea using WRF-3DVar

  • 期刊论文

  • 发表日期：

    2025年7月26日

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  • Kyong-Ung Ri,
  • Hyok-Chol Kim,
  • Chang-Bok Rim,
  • Ju-Song Kim,
  • Sang-Il Pak,
  • Song-Hak Nam

摘要

Accurate forecasting of heavy rainfall event is an important issue in weather forecasting. This study is conducted on the heavy rainfall event (maximum rainfall: 335 mm) in the western part of the Democratic People’s Republic of Korea (DPRK) on July 27, 2024, using WRF−3DVar. The sensitivity of conventional and satellite radiance data assimilation to thinning distance is first investigated. The WRF−3DVar simulation results for four thinning distances (30 km, 60 km, 90 km and 120 km) are analyzed. As a result, the simulation with a thinning distance of 60 km shows better performance among all experiments. To evaluate the impact of the assimilation of different observations, four numerical experiments are carried out such as CTRL (without data assimilation), DA (PREP) (with assimilation of conventional observations), DA (RAD) (with assimilation of satellite observations) and DA (All) (with assimilation of combined). The simulated rainfall field in the CTRL experiment is not well captured in terms of precipitation, intensity and spatial distribution compared to the observations. In the assimilation experiments conducted to improve quality of the initial conditions, rainfall field is simulated superior to CTRL. In particular, in DA (All), precipitation closely matches observations and the threat score is higher more than 30% compared to CTRL. There was a significant improvement in the initial field compared to CTRL in DA experiments, which further improve the prediction of the equivalent potential temperature, wind field at low-level atmosphere. Typically, the simulated amounts of hydrometeors (Qr, Qc, Qg + Qi + Qs) are found to be reasonably well captured compared to those in the CTRL experiment. These results from conventional and satellite radiance data assimilation show that the improvement of the initial conditions plays a positive impact on the simulation of heavy rainfall. The validity of these results is also demonstrated by simulation of two additional heavy rainfall events (5 August 2020, 15 August 2022). This study confirms that the accuracy of heavy rainfall forecasts is improved by using WRF−3DVar and highlights the importance of choice of parameters like thinning distance in operational forecast system using 3DVar.