GNSS天顶对流层延迟的短时天气预报分析

Analysis of short-time weather forecast based on GNSS zenith troposphere delay

针对天顶对流层延迟(ZTD)与大气可降水量(PWV)相关性特征及变化规律,该文提出基于无气象元素ZTD短时天气监测及预报的方法。该文以北京市2013—2015年BJFS站日均数据为例,采用GZTD和Saastamoinen+GPT2模型拟合,得到传统的无气象元素的PWV模型。并利用2016年BJFS站ZTD和气象数据,计算出PWV1(含气象元素)和PWV2(无气象元素)数据,通过定量数据分析得出:PWV1与PWV2、ZTD相关系数分别达到0.6332,0.9676;PWV2与ZTD相关性为0.6498。从短期数据,BJFS站6—9月的ZTD与PWV1相关性系数为0.9696。这证明了ZTD能够真实反映PWV1的真实变化趋势,同时也说明ZTD应用于短时天气预报的可行性。最后针对北京地区2016年的降雨、降雪和雾霾等极端天气事件,采用ZTD方法对其进行了分析研究,并取得了较好的效果。

According to the characteristics and variation rules of the correlation between zenith tropospheric delay(ZTD) and precipitable water vapor(PWV),it is possible to monitor and predict the change of short-time weather based on ZTD without meteorological elements. In this paper, taking Beijing as an example, the PWV model without meteorological elements was got with the daily average data of BJFS station from 2013 to 2015 fitting GZTD and Saastamoinen+ GPT2 model.And then,using ZTD and meteorological data of BJFS station in2016 with a data sampling rate of one hour,PWV1 with meteorological elements and PWV2 without meteorological elements were calculated respectively.Through the data analysis,the correlation coefficients of PWV1,PWV2 and ZTD were 0.633 2,0.967 6 respectively,and the correlation coefficients of PWV2 and ZTD were0.649 8.From the short-term data,the correlation coefficient between ZTD and PWV1 based on BJFS station from June to September was 0.969 6.This proved that ZTD could truly reflect the change trend of PWV1,and also showed the feasibility of ZTD in short-term weather forecast.At last,the extreme weather(precipitation,snowfall,haze)in Beijing in 2016 was analyzed with ZTD products,and good results were obtained.