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提出一种高精度的ZWD模型（tianjin＿zwd,TZ）。TZ基于2016-2018年逐小时气压分层的ERA5，欧洲中尺度气象预报中心第五代再分析产品数据，采用BP神经网络建立。然后，根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明：相比GPT3模型，TZ模型可提供更贴近真值的ZWD估值；并且，其RMSE由5.0 cm (GPT3)降至4.5 cm，表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能，该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。
Abstract：We proposed a high-precision zenith wet delay model for Tianjin region(TZ). Using the hourly pressure-level European center for medium-range weather forecasts re-analysis 5(ERA5) products from 2016 to 2018, we constructed the TZ model by the back propagation neural network(BPNN). Then, we used the ERA5-derived ZWD in 2019 to validate the TZ model. Test results show that that the TZ model can provide the ZWD estimation that is closer to the true value than the global pressure and temperature 3(GPT3) model. Moreover, its RMSE is reduced from5.0 cm(GPT3) to 4.5 cm, indicating a 10% accuracy improvement. The above results indicate that the TZ model gets better prediction performance, and its construction strategy can provide a reference for ZWD modeling in other regions.
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基本信息:
中图分类号:TP183;P228.4
引用信息:
[1]刘杰,刘亮.采用ERA5数据构建基于人工神经网络的天津ZWD模型[J].地理空间信息,2024,22(02):106-108.
基金信息:
天津市科技计划项目（23YDPYCG00010）; 天津市交通运输科技项目（2024-B07）; 中央级公益性科研院所基本科研业务费(TKS20230202); 企业科研创新基金（SJY202101）