## 7. Tropsphere Monitoring
Considering the GNSS ground reference stations KUUJ and CPNM located in Hudson Bay and the Java Sea, respectively, at an elevation of 45 degrees, the monthly average tropospheric error range of the KUUJ station ranges from \(-6.8\) cm to \(5.4\) cm and that of the CPNM station ranges from \(-18.4\) cm to \(20\) cm. When the elevation angle drops to five degrees, the range of tropospheric errors significantly increases, with the KUUJ station ranging from \(-46.7\) cm to \(38.3\) cm and the CPNM station ranging from \(-143.3\) cm to \(146.3\) cm. [PERSON] et al. [124] studied the possibility of ZTD retrieval using airborne coherent GNSS reflection measurement data observed in coastal waters. By obtaining residual phase observations, a \(0.5\) Hz empirical Doppler extended threshold was established to extract coherent reflections. The results indicated that coherent observations respond well to very calm water bodies and low elevation angles, with a deviation of \(5\%\) in estimating ZTD for airborne coherent GNSS-R at low elevation angles (\(10^{\circ}\)). Although this study demonstrated, for the first time, the enormous potential of airborne GNSS-R coherent phase observations in ZTD retrieval, existing technologies face challenges in obtaining high-precision, high spatiotemporal resolution ZTD information for most coherent reflection areas such as oceans, polar sea ice, and inland water bodies. Furthermore, no method for retrieving ZWD and(or) IWV has been proposed. In order to achieve high-precision, high-spatial-resolution, and high-temporal-resolution TCWV monitoring, in 2023, [PERSON] [125; [PERSON] 125] explored a new method for retrieving tropospheric delay and TCWV from GNSS-R using Spire CubeSats data for the first time. The flowchart of this method is shown in Figure 8. The comparison between IWV retrieval using Spire GG-R and ERA5 products, as well as the measurement of IWV data using Sentinel-3 land and sea colorimeter (OLCI), showed a good performance. Among them, the GNSS-R tilted tropospheric delay is estimated to be as follows:  
\[\hat{T}_{R}(t)=\hat{\Phi}_{R}(t)-\hat{g}_{R}(t)-\hat{I}_{R}(t)+M(t)\lambda+ \epsilon(t) \tag{8}\]  
where \(\hat{\Phi}_{R}\) represents the phase range of the reflected signal, \(\hat{g}_{R}\) and \(\hat{I}_{R}\) represent the geometric and clock components respectively, \(M\) represents another unknown integer for phase ambiguity, and \(\epsilon\) represents the influence of various estimation errors and noise. The tilted tropospheric delay can be modeled as follows:  
\[T_{R}(t)=2\times\left(ZHD(t)\times m_{dry}(\theta(t))+ZWD(t)\times m_{wet}( \theta(t))\right) \tag{9}\]  
where \(m_{dry}\) and \(m_{wet}\) are the mapping functions of zenith rest delay (ZHD) and zenith wet delay (ZWD), respectively. The estimated ZWD value can be converted to TCWV using the following formula:  
\[TCWV=\Pi\times ZWD \tag{10}\]  
where \(\Pi\) is the conversion factor in units of \(kg\cdot m^{-2}\cdot mm^{-1}\). Due to the current lack of high-spatial-resolution ocean sky observation methods with short revisit periods, it is difficult to accurately capture the dynamic changes of IWVs. The use of low-cost cube satellites in low Earth orbits has great potential, providing near-high-resolution insights into IWV changes in tropical oceans and valuable data for climate monitoring and atmospheric research. In the latest study, [PERSON] and [PERSON] [126] used Spire Global grazing angle GNSS-R data collected in low-altitude areas ranging from 5\({}^{\circ}\) to 8\({}^{\circ}\) in 2022 to achieve accurate calculations of IWVs. However, so far, only a few studies have conducted preliminary studies on IWV retrieval using Spire satellite dual-frequency GPS-R Raw IF signal observation data.