Abstract
The conversion factor is a key parameter for converting zenith wet delays (ZWD) into precipitable water vapour (PWV) with a mean value of 0.15, and the traditional method of calculating it is to model the weighted average temperature in the process of conversion factor calculation. Here, we overcome the dependence on high-precision atmospheric weighted average temperature for mapping ZWD onto PWV and build a global non-meteorological parametric model for conversion factor GΠ model by using the gridded data of global conversion factor time series from 2006 to 2013 provided by the Global Geodetic Observing System (GGOS) Atmosphere. Internal and external accuracy tests were performed using data from four times (UTC 00:00, 06:00, 12:00, and 18:00) per day throughout 2012 and 2014 as provided by the GGOS Atmosphere, and the statistical average root-mean-square (RMS) and mean absolute errors (MAE) on a global scale are 0.0031/0.0026 and 0.0030/0.0026, respectively, which only account for 1.5–2% of the conversion factor value. In addition, the observed GPS data are also used to validate the established GΠ model, and the RMS of the PWV differences between the established model and the observed meteorological data was less than 3.2 mm. The results show that the established GΠ model has a high accuracy, which can be used to calculate the PWV value where no observed meteorological parameters are available.
Highlights
It is important to understand the spatiotemporal variability of water vapour so as to study global climate change and for rainfall forecasting [1]
A global conversion factor model of non-meteorological parameters (GΠ model) was established by using gridded data provided by the Global Geodetic Observing System (GGOS) Atmosphere from 2006 to
The internal accuracy of the established model was tested using the data from 2012 provided by the GGOS Atmosphere, and the result shows that the average values of the mean absolute errors (MAE) and RMS of the GΠ model were 0.0026 and 0.0031 respectively
Summary
It is important to understand the spatiotemporal variability of water vapour so as to study global climate change and for rainfall forecasting [1]. The traditional method of monitoring atmospheric water vapour mainly uses sounding balloons, water vapour radiometers, etc., but the data from the traditional method (such as the sounding balloons) have low temporal resolution, e.g., two or four times daily, and larger spatial resolution, e.g., 200 to 300 km [2,3]. Water vapour radiometers are mainly affected by the weather and cannot work under rainfall or foggy weather conditions, and the instruments are unwieldy and expensive [4,5,6]. With the development of the global navigation satellite system (GNSS) technology, the monitoring of atmospheric water vapour by ground-based GNSS technology has become a new research hotspot. Askne and Nordius [4]
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