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This study addresses the meteorological support requirements for commercial aircraft’s extreme cold weather flight testing in China. We conducted a targeted statistical analysis of low-temperature conditions at domestic civil airports, identifying the top eight airports with the most severe cold conditions: Arxan, Greater Khingan Range, Yichun, Hailar, Mohe, Manzhouli, Changbai Mountain, Wudalianchi airports. We then evaluated the winter temperature forecast performance of the ECMWF (EC), Global Forecast System (GFS), and CAAC regional models. The results show that the initial field assimilation of EC model closely matches actual temperatures, with smaller average errors in the 12–36 h forecast before errors increase. GFS model exhibits outstanding stability across forecast lead times, while the CAAC regional model outperforms better than EC model beyond 48 h. Finally, by integrating SMART-SF sub-seasonal temperature forecast, we established a progressive low-temperature support framework. This approach enhances the standardization and reliability of extreme cold weather flight testing and can be extended to meteorological support for civil aviation operations in winter.

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Statistical Analysis and Forecast Comparison of Meteorological Conditions for Commercial Aircraft’s Extreme Cold Weather Flight Testing in China

  • 会议论文

  • 发表日期：

    2026年

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  • Wang Ziyi,
  • Ge Yangjinxi,
  • Cao Zhenyu,
  • Dong Junhui,
  • Zhang Wenting,
  • Ji Mo

摘要

This study addresses the meteorological support requirements for commercial aircraft’s extreme cold weather flight testing in China. We conducted a targeted statistical analysis of low-temperature conditions at domestic civil airports, identifying the top eight airports with the most severe cold conditions: Arxan, Greater Khingan Range, Yichun, Hailar, Mohe, Manzhouli, Changbai Mountain, Wudalianchi airports. We then evaluated the winter temperature forecast performance of the ECMWF (EC), Global Forecast System (GFS), and CAAC regional models. The results show that the initial field assimilation of EC model closely matches actual temperatures, with smaller average errors in the 12–36 h forecast before errors increase. GFS model exhibits outstanding stability across forecast lead times, while the CAAC regional model outperforms better than EC model beyond 48 h. Finally, by integrating SMART-SF sub-seasonal temperature forecast, we established a progressive low-temperature support framework. This approach enhances the standardization and reliability of extreme cold weather flight testing and can be extended to meteorological support for civil aviation operations in winter.