| 杨加春,王彦明,李庆军,贾克斌,刘鹏宇.探空温度传感器误差预测技术研究[J].电子测量与仪器学报,2021,35(12):24-36 |
| 探空温度传感器误差预测技术研究 |
| Research on error prediction technology of radiosonde temperature sensor |
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| DOI： |
| 中文关键词: 气象探测 温度传感器 误差预测 特征工程 模型融合 |
| 英文关键词:aerological sounding temperature sensor error prediction feature engineering model fusion |
| 基金项目:国家重点研发计划(2018YFC1506202)项目资助 |
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| Author | Institution |
| Yang Jiachun | 1. Tianjin Huayuntianyi Special Meteorological Detection Technology Co. , Ltd |
| Wang Yanming | 2. Shijiazhuang Posts and Telecommunications Technical College,3. Faculty of Information Technology, Beijing University of Technology,4. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology |
| Li Qingjun | 1. Tianjin Huayuntianyi Special Meteorological Detection Technology Co. , Ltd |
| Jia Kebin | 3. Faculty of Information Technology, Beijing University of Technology,4. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology |
| Liu Pengyu | 3. Faculty of Information Technology, Beijing University of Technology,4. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology |
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| 中文摘要: |
| 随着气候诊断、气候变化、天气预报等学科的深入开展,对探空温度传感器的测量精度提升到了 0. 1 ℃ 的量级要求,而
由于太阳辐射、升空速度、入云出云等因素的干扰,引起的测量误差可达 3℃甚至更高,已成为制约气象探测精度提升的主要障
碍。 针对此问题,首先通过三维建模及流体力学分析,得到了温度传感器最优的设计方案,从传感器形态设计上实现了测量误
差最小化。 然后对历史气象探测数据进行分析和汇总,构造出国内首个基于真实环境的、包含 900 000 条探测记录的高空气象
探测数据集,以解决仿真环境与真实环境存在偏差的问题。 最后,将 Morlet 小波作为深度神经网络的激活函数,并将支持向量
机、XGBoost、深度神经网络、线性回归相融合,构造出一个针对探空温度传感器测量误差的预测模型。 经过本文所提出的误差
预测模型,平均误差从 0. 817 降低到了 0. 008,均方误差从 0. 878 降低到了 0. 068,标准差从 0. 458 降低到了 0. 204,拟合系数 R
2
为 0. 93,使温度传感器的测量精度得到显著提升,更有利于气象学科相关内容的展开。 |
| 英文摘要: |
| With the development of disciplines such as climate diagnosis, climate change and weather forecasting, the measurement
accuracy of the sounding temperature sensor has increased to the order of 0. 1℃ . Due to the interference of factors such as solar
radiation, lift-off speed, cloud in and out of the cloud, the sensor measurement error can reach 3℃ or even higher, which has become
the main obstacle restricting the improvement of meteorological detection accuracy. Aiming at this problem, firstly, the optimal design
scheme of the temperature sensor is obtained through three-dimensional modeling and fluid mechanics analysis. The measurement error is
minimized from the sensor morphology design. Then, by analyzing and summarizing historical meteorological observation data, the first
domestic high-altitude observation dataset containing 900 000 detection records based on the real environment was constructed to solve
the problem of deviation between the simulated environment and the real environment. Finally, Morlet wavelet is used as the activation
function of the deep neural network. The support vector machine, XGBoost, deep neural network, and linear regression are combined to
construct a prediction model for the measurement error of the sounding temperature sensor. After the error prediction model proposed in
this paper, the average error is reduced from 0. 817 to 0. 008, the root mean square error is reduced from 0. 878 to 0. 068, the standard
deviation is reduced from 0. 458 to 0. 204, and the fitting coefficient R
2
is 0. 93. The measurement accuracy of the temperature sensor
has been significantly improved, which is more conducive to the development of relevant content of the meteorological discipline. |
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