Journal of Arid Meteorology ›› 2023, Vol. 41 ›› Issue (1): 164-172.DOI: 10.11755/j.issn.1006-7639(2023)-01-0164

• Technical Reports • Previous Articles     Next Articles

Study of 2 m temperature variation correction during transitional processes of temperature in Sichuan

FENG Liangmin1,2(), ZHOU Qiuxue1,2, CAO Pingping1,2, WANG Jiajin1,2   

  1. 1. Sichuan Meteorological Observatory, Chengdu 610072, China
    2. Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
  • Received:2022-05-16 Revised:2022-10-25 Online:2023-02-28 Published:2023-02-28

四川地区气温转折过程2 m温度变化订正研究

冯良敏1,2(), 周秋雪1,2, 曹萍萍1,2, 王佳津1,2   

  1. 1.四川省气象台,四川 成都 610072
    2.高原与盆地暴雨旱涝灾害四川省重点实验室,四川 成都 610072
  • 作者简介:冯良敏(1987—),女,硕士,高级工程师,主要从事数值模式释用与现代天气预报技术研究。E-mail:
  • 基金资助:


Based on the daily 2 m maximum and minimum temperature data from 1990 to 2019 in Sichuan Province, the temperature transitional weather processes have been analyzed statistically. Then a correction model of temperature change during transitional processes of temperature has been performed by using of NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research) daily reanalysis data and the LightGBM (Light Gradient Boosting Machine) algorithm.The results show that the area with the most temperature transitional processes is the slope transition zone between the plateau and the basin, while the least is in the basin. The number of temperature transitional processes in each region has an obviously seasonal differences with the most in spring and the least in winter, and the temperature transitional processes in spring is significantly more than those in the other three seasons. For the training set from 1990 to 2019,the LightGBM model has good performances with an overall accuracy of 78.64% and a mean absolute error of 1.35 ℃. For the independent testing set in 2020,the LightGBM model has an overall accuracy of 53.60% and a mean absolute error of 2.19 ℃, which are better than those of ECMWF (European Centre for Medium-Range Weather Forecasting), SCMOC and SPCO models.

Key words: transitional process of temperature, LightGBM algorithm, machine learning


基于四川地区1990—2019年的逐日2 m最高、最低温度站点实况数据,对气温转折天气过程进行统计和分析,在此基础上,应用LightGBM(Light Gradient Boosting Machine)算法及NCEP/NCAR(National Center for Environmental Prediction/National Center for Atmospheric Research)逐日再分析资料,构建气温转折天气过程变温订正模型。结果表明:(1)出现气温转折过程最多的区域是高原与盆地的边坡过渡区,最少的是盆地;(2)各区域的气温转折过程具有明显的季节差异,均表现为春季最多、冬季最少,且春季的气温转折过程明显多于其他3季;(3)在1990—2019年验证集中,LightGBM订正模型表现较好,准确率为78.64%,平均绝对误差为1.35 ℃。(4)在2020年的独立样本测试中,LightGBM订正模型的准确率为53.60%,平均绝对误差为2.19 ℃,整体订正效果优于ECMWF模式(European Centre for Medium-Range Weather Forecasting)、中央台城镇预报指导报(SCMOC)及四川省气象台数值预报客观释用城镇预报指导报(SPCO)的预报。

关键词: 气温转折过程, LightGBM算法, 机器学习

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