赵庆志, 姚宜斌, 辛林洋. 融合ECMWF格网数据的水汽层析精化方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(8): 1131-1138. DOI: 10.13203/j.whugis20190323
引用本文: 赵庆志, 姚宜斌, 辛林洋. 融合ECMWF格网数据的水汽层析精化方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(8): 1131-1138. DOI: 10.13203/j.whugis20190323
ZHAO Qingzhi, YAO Yibin, XIN Linyang. A Method to Sophisticate the Water Vapor Tomography Model by Combining the ECMWF Grid Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1131-1138. DOI: 10.13203/j.whugis20190323
Citation: ZHAO Qingzhi, YAO Yibin, XIN Linyang. A Method to Sophisticate the Water Vapor Tomography Model by Combining the ECMWF Grid Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1131-1138. DOI: 10.13203/j.whugis20190323

融合ECMWF格网数据的水汽层析精化方法

A Method to Sophisticate the Water Vapor Tomography Model by Combining the ECMWF Grid Data

  • 摘要: 全球导航卫星系统(global navigation satellite system, GNSS)层析技术是获取对流层三维水汽信息的重要途径之一。然而,传统水汽层析方法在构建层析模型时缺少足够的初始先验信息,导致层析模型设计矩阵结构不稳定,层析解算结果精度较差。针对上述情况,提出了一种融合欧洲中尺度天气预报中心(European Center for Medium-Range Weather Forecasting, ECMWF)格网数据精化层析模型反演水汽的方法。该方法通过ECMWF ERA-Interim再分析资料数据集提供的格网数据计算得到层析区域各网格内的水汽密度初值,将其作为先验初始信息附加到传统层析模型中对模型精化。在层析模型解算时,顾及层析模型先验信息权比对层析结果的影响。为了验证提出方法的有效性,以中国香港卫星定位参考站网(satellite positioning reference station network, SatRef)实测GNSS和气象数据为例进行实验,并以实验区域的无线电探空数据为基准验证该方法的可行性及精度。实验结果表明,提出的方法能够明显提高层析结果的精度,反演水汽的均方根误差(root mean squared error, RMSE)由原来的1.82 g/m3减小到了1.07 g/m3,改善率为41.2%。此外,所提方法在平均绝对偏差(mean absolute error, MAE)、偏差(Bias)和标准差(standard deviation,STD)等方面也均优于传统层析方法。

     

    Abstract:
      Objectives  GNSS tomography technique is one of the most important methods to obtain the three-dimensional water vapor information. However, due to lack of enough initial prior information in the process of building the tomography model, the design matrix of tomography model is unstable and the tomographic result is poor, which has become an urgent problem to be resolved.
      Methods  First of all, the grid data derived from European Center for Medium-Range Weather Forecasting (ECMWF) is used to calculate the initial value of water vapor density in every voxel of interest area. And then, sophisticating the traditional tomography modeling using the calculated initial water vapor values. Finally, the influence of weightings of different equations in tomography model on tomographic result is also considered.
      Results  Comparing to the traditional methods, the proposed approach can enhance the accuracy of tomographic result by 41.2%, and its root mean squared error (RMSE) decreases from 1.82 g/m3 to 1.07 g/m3. Additionally, the mean absolute error (MAE), Bias and standard deviation (STD) also show a better performance than those of the traditional methods.
      Conclusions  The purpose is to improve the accuracy of tomographic water vapor profiles and used to sophisticate the established tomography model.

     

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