# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 3 Research and Applications of RO to TC Forecast
### Effect of RO Data on TC Forecasts  
Theoretically, TCs form in the environment of reduced wind shear, a deep layer of warm sea surface (temperatures above 27 \({}^{\circ}\)C), enhanced vorticity, humidity, and deep convection. These conditions are based on the TC forecast model [51]. If these initial conditions in the TC forecast model, such as wind, temperature, and water vapor, are more accurate, the TC forecast will be improved. The RO data are conducive to improving the accuracy and precision of TC prediction via enhancing the accuracy of the intensity and track of the TCs. Most TCs are generated above oceans, and the inner structures of a TC are moist. The conventional measurements, such as radiosonde and some of the satellites, are either incapable of detecting the atmospheric systems above oceans or inaccurate in detecting the water vapor. As mentioned in Section 1.3, the RO signals can propagate through clouds and obtain water vapor condition above the oceans. Thus, this feature makes the RO measurements feasible for TC forecasts. Many studies show that RO measurements have a positive impact on the TC forecast [52; 53; 54; 55; 56; 57; 58; 59]. The most famous one is the NCAR (National Center for Atmospheric Research) 4-Day forecast of Ernesto in 2007. As shown in Figure 11, in the second and third column, the generation of hurricane Ernesto is successfully predicted in the group with the RO data, whereas the group without the RO data failed. Figure 10: Tropical cyclone (TC) forecast model performance of four institutes during 2007–2014. Probability of detection (POD) \(=\frac{hit}{hit+miss}\) is given along the \(y\)-axis; Success ratio (SR) \(=\frac{hit}{hit+false\ alarm}\) is given along the x-axis; the dashed line indicates the bias \(=\frac{hit+false\ alarm}{hit+miss}\); the solid line indicates the critical success index (CSI) \(=\frac{hit}{hit+false\ alarm+miss}\). The solid circle and triangle indicate the North Atlantic (NATL) and eastern North Pacific (EPAC) basins. Higher values of POD, SR, and CSI indicate a better forecast of the TC genesis. On the top right corner is the perfect forecast [49]. This case was further studied in [61]. Because of the strong correlation between \(T\), \(e\), and \(p\) as in Equation (1), while solving equations, it is difficult to know which parameter induces the error. Thus, it is difficult to accurately estimate the error RO data brings to the TC forecast. The authors of [61] solved this problem using an ensemble mean assimilation system. In the paper, one observation set (ObS) using the NCEP reanalysis was set, and three experiments were set: CTRL, RO, and RO6 km. The CTRL assimilated the conventional measurements, such as those obtained from radiosonde, aircraft, and satellite cloud drift wind; the RO assimilated all the RO refractivity profiles; and the RO6 km assimilated only the RO refractivity profiles above a height of 6 km. In Figure 12a, the central sea level pressure in the RO is approximately 15 hPa lower than those in CTRL and RO6 km, which makes the TC in RO stronger. This is because the assimilated RO data provided a more accurate pressure field and helped adjust the initial wind field through forecast multivariate correlations between the provided parameters RO and wind, which affects the cyclone's intensification. In Figure 12b, the track error of the RO is approximately in the range of 30-50 km, which is lower than that in other groups. From the image of the total column cloud liquid water shown in Figure 12c, the RO prediction is similar to the satellite infrared cloud image, which verified fact that RO data provide better measurements of the water vapor field. Therefore, the RO data have a positive impact on the TC prediction. Notably, the results of RO6 km are neutral, emphasizing the importance of RO data below 6 km. As mentioned in Section 2, the main benefits of RO for the NWP are in the height range of 10-20 km.