湖北农业科学 ›› 2026, Vol. 65 ›› Issue (7): 178-183.doi: 10.14088/j.cnki.issn0439-8114.2026.07.028

• 农业工程 • 上一篇    下一篇

地面气象观测数据质量控制技术在东营市萝卜生产中的运用

梁海霞1, 梁倩2, 张立清1   

  1. 1.山东省东营市气象局,山东 东营 257091;
    2.山东省东营市河口区气象局,山东 东营 257200
  • 收稿日期:2026-04-21 出版日期:2026-07-25 发布日期:2026-07-23
  • 作者简介:梁海霞(1979-),女,山东东营人,助理工程师,主要从事气象服务与应用气象研究工作,(电子信箱)lhx2026410@163.com;张立清(1974-),女,山东利津人,高级工程师,硕士,主要从事大气探测研究工作,(电子信箱)dyqxjzlq@163.com。
  • 基金资助:
    东营市气象局气象科学技术研究项目(2025dyqx05)

Application of quality control technology for surface meteorological observation data in radish production in Dongying

LIANG Hai-xia1, LIANG Qian2, ZHANG Li-qing1   

  1. 1. Dongying Meteorological Bureau of Shandong Province, Dongying 257091, Shandong, China;
    2. Hekou Meteorological Bureau of Shandong Province, Dongying, Dongying 257200, Shandong, China
  • Received:2026-04-21 Published:2026-07-25 Online:2026-07-23

摘要: 为提升地面气象观测数据在东营市萝卜生产中的应用价值,围绕东营青萝卜生育期的气象敏感指标,构建适配萝卜生产需求的气象数据质量控制技术体系和三级质控流程,并提出INLM-PSO缺失值修复方法。结果表明,RF异常识别法在各生育期的误剔率为1.37%~1.62%。INLM-PSO缺失值修复法在日、周、旬尺度下的修复后完整率均高于98%。在萝卜生产应用评价中,播种出苗期出苗率保持在86%以上,烂种率控制在3%~8%,出苗整齐度为80%~95%,综合表现均优于KNN邻域判别法和改进3σ阈值法。该气象数据质量控制技术体系可降低异常数据误剔率和漏检率,提高缺失数据修复后完整率,为东营市萝卜生产与田间管理决策提供数据支撑。

关键词: 地面气象观测, 数据质量控制, 随机森林, 萝卜生产, 东营市

Abstract: To enhance the application value of ground meteorological observation data in radish production in Dongying, this study focused on the meteorological sensitive indicators during the growth period of Dongying green radish. A meteorological data quality control technology system and a three-level quality control process adapted to radish production requirements were constructed, and an INLM-PSO missing value repair method was proposed. The results showed that the error rejection rate of the RF anomaly identification method ranged from 1.37% to 1.62% across different growth periods. The INLM-PSO missing value repair method achieved repair completeness rates over 98% at daily, weekly, and ten-day-period scales. In the application evaluation of radish production, the emergence rate during the sowing and emergence period remained above 86%, the rotten seed rate was controlled within 3% to 8%, and the emergence uniformity ranged from 80% to 95%, indicating better overall performance than the KNN neighborhood discrimination method and the improved 3σ threshold method. This meteorological data quality control system could reduce the false rejection and missed detection rates of anomalous data, improve the repair completeness rates of missing data, and provide reliable data support for radish production and field management decisions in Dongying.

Key words: ground meteorological observation, data quality control, random forest, radish production, Dongying

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