Global empirical model for mapping zenith wet delays onto precipitable water

We can map zenith wet delays onto precipitable water with a conversion factor, but in order to calculate the exact conversion factor, we must precisely calculate its key variable $$T_\mathrm{m}$$. Yao et al. (J Geod 86:1125–1135, 2012. doi:10.1007/s00190-012-0568-1) established the first generation of global $$T_\mathrm{m}$$ model (GTm-I) with ground-based radiosonde data, but due to the lack of radiosonde data at sea, the model appears to be abnormal in some areas. Given that sea surface temperature varies less than that on land, and the GPT model and the Bevis $$T_\mathrm{m}$$–$$T_\mathrm{s}$$ relationship are accurate enough to describe the surface temperature and $$T_\mathrm{m}$$, this paper capitalizes on the GPT model and the Bevis $$T_\mathrm{m}$$–$$T_\mathrm{s}$$ relationship to provide simulated $$T_\mathrm{m}$$ at sea, as a compensation for the lack of data. Combined with the $$T_\mathrm{m}$$ from radiosonde data, we recalculated the GTm model coefficients. The results show that this method not only improves the accuracy of the GTm model significantly at sea but also improves that on land, making the GTm model more stable and practically applicable.