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In July 2021, the Civil Aircraft Flight Test Center of Commercial Aircraft Corporation of China (FTCC) organized and conducted a specialized flight test for a certain aircraft in rainy weather conditions at Dongying Airport, during the landing of typhoon In-Fa. This study investigates the improvements of using ensemble data assimilation and cycling forecasts based on the numerical weather prediction (NWP) relative to subjective forecasts. Utilizing the Weather Research and Forecasting Model (WRF v3.9), it is shown that suitable test flight windows are identified 3 days in advance. By assimilating observations every 6 h with the Ensemble Kalman Filter (EnKF), the forecast skills of rainfall intensity and crosswind speed are improved. Additionally, the ensemble forecasts provide probabilistic assessments of the rainfall intensity and crosswind speed. Objective success rate of flight tests can be then estimated, which is critical for operational decision-making. The potential of NWP to enhance safety and efficiency in aviation operations during adverse weather conditions is demonstrated.

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Research on Rainy Weather Test Flights for a Domestic Passenger Jet Based on Ensemble Data Assimilation and Cycling Forecasts

  • 会议论文

  • 发表日期：

    2025年

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  • Zheng Yang,
  • Yangjinxi Ge,
  • Lu Liu,
  • Sunyi Yuan,
  • Yukun Shi,
  • Fengxian Wang,
  • Junying Sun,
  • Donghua Zhang,
  • Yuxuan Ding

摘要

In July 2021, the Civil Aircraft Flight Test Center of Commercial Aircraft Corporation of China (FTCC) organized and conducted a specialized flight test for a certain aircraft in rainy weather conditions at Dongying Airport, during the landing of typhoon In-Fa. This study investigates the improvements of using ensemble data assimilation and cycling forecasts based on the numerical weather prediction (NWP) relative to subjective forecasts. Utilizing the Weather Research and Forecasting Model (WRF v3.9), it is shown that suitable test flight windows are identified 3 days in advance. By assimilating observations every 6 h with the Ensemble Kalman Filter (EnKF), the forecast skills of rainfall intensity and crosswind speed are improved. Additionally, the ensemble forecasts provide probabilistic assessments of the rainfall intensity and crosswind speed. Objective success rate of flight tests can be then estimated, which is critical for operational decision-making. The potential of NWP to enhance safety and efficiency in aviation operations during adverse weather conditions is demonstrated.