| 基于MLP神经网络的大气加权平均温度模型 |
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| 引用本文： | 谢劭峰,曾 印,张继洪,张亚博,熊 思.基于MLP神经网络的大气加权平均温度模型[J].大地测量与地球动力学,2022,42(11):1105-1110. |
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| 作者姓名： | 谢劭峰 曾 印 张继洪 张亚博 熊 思 |
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| 摘 要： | 以中国西南地区2015~2017年探空数据为实验数据，使用多层感知器（MLP）神经网络回归方法建立西南地区的加权平均温度(Tm)模型。将气象参数（地表温度、水汽压）和非气象参数（高程、纬度和年积日）作为模型输入因子，由数值积分法计算得到的Tm作为学习目标，通过神经网络模型进行迭代训练从而得到中国西南地区的Tm。以2018年探空站Tm数据为参考值，对MLP模型精度进行验证，并与Bevis模型和GPT3模型进行对比分析。结果表明，MLP模型的年均RMSE和年均bias分别为1.99 K和0.15 K，比Bevis模型、GPT3模型年均RMSE分别降低1.36 K(40.6%)和1.51 K(43.1%)，年均bias分别下降0.70 K（82.4%）和1.04 K（87.4%），且该模型在中国西南区域不同高程、纬度和季节的精度与稳定性优于Bevis模型和GPT3模型。
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| 关 键 词： | 大气加权平均温度 多层感知器 精度检验：西南地区 |
Atmospheric Weighted Mean Temperature Model Based on MLP Neural Network |
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| XIE Shaofeng,ZENG Yin,ZHANG Jihong,ZHANG Yabo,XIONG Si.Atmospheric Weighted Mean Temperature Model Based on MLP Neural Network[J].Journal of Geodesy and Geodynamics,2022,42(11):1105-1110. |
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| Authors: | XIE Shaofeng ZENG Yin ZHANG Jihong ZHANG Yabo XIONG Si |
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| Abstract: | Using multi-layer perceptron(MLP) on sounding data from 2015 to 2017 in southwest China as the experimental data, we establish the weighted mean temperature (Tm) model for the area. We use meteorological parameters (surface temperature, water vapor pressure) and non-meteorological parameters (elevation, latitude, and day of year) as model input factors, and use theTm calculated by the numerical integration method as the learning target. We apply the neural network model for iterative training to obtain theTm in southwest China. Using theTm data of the sounding stations in 2018 as reference values, we verify the accuracy of the MLP model and compare it with the Bevis and GPT3 models. The results show that the mean annual RMSE and annual bias of the MLP model are 1.99 K and 0.15 K, respectively. Compared with the Bevis model and the GPT3 model, the mean annual RMSE is reduced by 1.36 K (40.6%) and 1.51 K (43.1%), respectively. The bias drops by 0.70 K(82.4%) and 1.04 K(87.4%), respectively, and the accuracy and stability of the model in different elevations, latitudes and seasons in southwest China are better than the Bevis and GPT3 models. |
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| Keywords: | atmosphere weighted mean temperature multi-layer perceptron(MLP) accuracy test southwest China |
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