利用多源数据构建PWV混合模型

Establishment of PWV Fusion Model Using Multi-source Data

    摘要
  • 摘要: 高精度和高时空分辨率的大气可降水量(precipitable water vapor, PWV)信息对于极端天气研究具有重要作用。传统的单一水汽探测技术获取的PWV因其系统设计的局限性存在精度差、时空分辨率低等缺陷。针对该问题，提出了一种基于多源数据的混合模型——全球温度气压湿度(global pressure and temperature 2 wet, GPT2w) +球谐函数（spherical harmonic function，SHF）+多项式拟合（polynomial fitting，PF)，简称GSP模型。该模型通过GPT2w计算PWV的初始值，利用SHF拟合PWV的偏差序列，利用PF对模型偏差进行校正，并引入Bartlett检验确定GSP模型中多源数据的最优权值。选取2014年中国云南省26个全球导航卫星系统(global navigation satellite system, GNSS)测站和37个欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasting, ECMWF)气候再分析数据集(ECMWF reanalysis-interim, ERA-Interim)格网点(1°×1°)的数据为例，建立GSP模型并进行验证，发现GSP模型较传统PF模型的精度提升率为15%~18%。以ECMWF第5代气候再分析数据集(ECMWF reanalysis v5, ERA5)提供的PWV格网数据(0.25°×0.25°)为参考，GSP模型的平均均方根误差和偏差分别为1.64 mm、－0.25 mm。上述结果表明GSP模型具有较高的精度，对于极端天气预警具有重要作用。

     

    Abstract:
      Objectives  Precipitable water vapor (PWV) information with high precision and high spatial-temporal resolution plays an important role in the study of extreme weather. The PWV obtained by traditional single water vapor detection technology has the defects of poor precision and low spatial-temporal resolution due to the limitations of its system design.
      Methods  To solve this problem, we propose a PWV hybrid model based on multi-source data, called the GSP(GPT2w+spherical harmonical function+polynomial fitting) model.In this model, the initial value of PWV is calculated by the GPT2w model, the residual sequence of PWV is fitted by a spherical harmonic function, and after that, deviation correction is performed for the residual PWV based on the polynomial fitting, and the Bartlett test is introduced to deter mine the optimal weights of multi-source data in the GSP model.
      Results  The data of 26 GNSS stations and 37 ERA-Interim grid points (1°×1°) in Yunnan Province, China has been selected to validate the GSP model, and the numerical results show that the accuracy improvement rate of the GSP model is 15%— 18% compared with the traditional polynomial fitting model. Compared with the ERA5 (0.25°×0.25°) data, the mean root mean square and Bias of GSP model are 1.64 mm and -0.25 mm, respectively.
      Conclusions  The above results show that the proposed GSP model has high accuracy and plays an important role in extreme weather warnings.

     

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