利用广义回归神经网络融合天顶对流层湿延迟

Zenith Wet Delay Fusion Based on Generalized Regression Neural Network

    摘要
  • 摘要: 精确模型化天顶对流层湿延迟(zenith wet delay, ZWD)有利于提高全球导航卫星系统(global navigation satellite system, GNSS)导航定位和气象应用的精度。用于ZWD模型化的数据源有多种，各有独特的优势，但它们之间存在多源异构、精度不等甚至严重系统偏差等问题，难以对多源ZWD数据进行综合利用。针对这个问题，首先，利用广义回归神经网络的非线性逼近能力和高质量探空ZWD (radiosonde ZWD, RS ZWD)，优化质量相对较低的中国气象局天气预报系统发布的全球/区域同化预报增强系统(global/regional assimilation and prediction enhanced system-mesoscale, GRAPES) ZWD以及欧洲中期天气预报中心（European centre for medium-range weather forecasts，ECMWF）发布的第五代气候再分析资料(the fifth generation ECMWF reanalysis, ERA5) ZWD；然后，将3种数据源的ZWD合并，实现ZWD的无偏融合，获得1 032张ZWD融合图。结果表明，以RS ZWD为参考，2016—2017年的GRAPES ZWD、ERA5 ZWD与RS ZWD间存在不同程度的系统偏差；优化后GRAPES ZWD和ERA5 ZWD的总体偏差均趋近于0，均方根(root mean square, RMS)分别改善了22.6%和16.0%；且优化后GRAPES ZWD和ERA5 ZWD的偏差、标准差和RMS的时空变化得到较大程度的削弱。融合后的ZWD兼具较高的精度和稳定性，能为GNSS应用提供可靠的ZWD数据源。

     

    Abstract:
    Objectives Accurate modeling of zenith wet delay (ZWD) is crucial for enhancing the precision of global navigation satellite system (GNSS) positioning and meteorological applications. While multiple data sources exist for ZWD modeling, their comprehensive utilization is challenged by multi-source heterogeneity, uneven precision, and significant systematic biases.
    Methods This paper employs a generalized regression neural network which is known for its strong nonlinear approximation capability, to correct the relatively lower-quality ZWD products from global/regional assimilation and prediction enhanced system-mesoscale (GRAPES) model and the the fifth generation ECMWF reanalysis (ERA5). The correction uses high-quality radiosonde (RS) ZWD as a reference. The modified GRAPES ZWD and ERA5 ZWD are then fused with RS ZWD to achieve a bias-free multi-source ZWD product, resulting in 1 032 fused ZWD grid sets.
    Results Validation against RS ZWD in 2016—2017 reveal systematic biases in the original GRAPES ZWD and ERA5 ZWD. After correction, the overall bias of both datasets approaches zero, and root mean square (RMS) is reduced by 22.6% and 16.0%,respectively. The spatiotemporal variations of bias, standard deviation, and RMS are also significantly attenuated.
    Conclusions The fused ZWD product demonstrates high accuracy and stability, offering a reliable data source for advanced GNSS applications.

     

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