中国地区地基GPS加权平均温度T_m统计分析

Weighted Mean Temperature T_m Statistical Analysis in Ground-based GPS in China

  • 摘要: 利用香港Kings Park探空站(站号45004)2003~2009年探空资料回归出了大气加权平均温度Tm与地面温度Ts、气压es和水汽压Ps的线性公式。通过比较分析发现,Tm-Ts单因素回归结果和Tm-Ts、es、Ps多因素回归结果并没有显著差异,但基于本地化探空数据回归公式精度比Bevis公式高;通过逐年增加样本数回归分析并不能显著提高公式精度,采用最近一年探空数据回归公式即可很好地由Ts预测下年Tm,预测均方根误差为1.913K;对样本数据按季节分类,Tm-Ts模型系数a、b值春秋季节变化不大,而冬夏季节变化剧烈,但利用无季节区分数据回归公式去拟合分季节数据,精度相当,分季节回归Tm-Ts经验公式没有必要。分气候区回归分析发现,Tm-Ts模型a、b系数与气候相关关系明显,同一气候区内探空站数据拟合出的a、b系数具有集群性。

     

    Abstract: We made linear regression to get the emprical formula of Tm-Ts and Tm-Ts、es、Ps using Hong Kong Kings Park sounding station(number 45004) data from 2003 to 2009,in which Tm means atmospheric weighting mean temperature,Ts means surface temperature,es means surface water vapour pressure and Ps means surface atmospheric pressure.It concludes that there is no significant difference between one-factor and multi-factor results,but the precision of regression formula based on local sounding data is higher than Bevis formula;The precision of regression formula can't be significantly increased by annually adding sample number,so it is sufficient to forecast Tm by Ts using sounding data of last year and the root mean square of estimation is 4.913 K;By classifying sounding sample data according seasons,Tm-Ts model coefficients a and b value vary little in spring and autumn,whereas they vary obviously in summer and winter;Comparing the accuracy of regression formula using data by season and no season classification,it is almost quite the same,so there is no need to regress the Tm-Ts model using different season data.It can be found that a and b value relatively uniform in one same climatic region and are correlative with climatic type

     

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