## 6 Conclusions and Future Prospects  
GNSS RO and GNSS-R technologies, with their unique advantages, effectively supplement the spatial and temporal limitations of traditional observation methods, particularly in the marine domain. Consequently, GNSS RS data have significantly improved the forecasting of typhoons. This article mainly reviews the assimilation applications of occultation and reflectometry observations in typhoon forecasting. The article briefly introduces the principles of RO and GNSS-R, existing satellite missions, and their advantages, such as global coverage, high precision, and all-weather capability. It discusses the benefits that GNSS remote sensing data bring to typhoon forecasting. In the realm of TC monitoring, RO is one of the solutions to the longstanding issue of forecast errors. Assimulating RO data has significantly enhanced the predictions of TC intensity and trajectory, with data from below 6 km and the UTLS region aiding this improvement. At the early stages of a TC, data below 6 km are crucial due to RO's high vertical resolution, and UTLS data impact forecasts over three days or more. Additionally, the analysis of simulated TC vertical structures has reconstructed critical structures, especially the eye, eyewall, and rainbands, because RO data provide more accurate initial fields of moisture and temperature. The assimilation of RO in regions with high gradients of moisture and temperature remains challenging. However, the new method of a non-local excess phase operator has somewhat resolved this issue, further improving TC predictions. With denser RO data, TCs can be predicted more accurately. GNSS-R in the field of TC detection mainly focuses on wind speed observations. Assimulating GNSS-R data has shown effectiveness in forecasting TC intensity and trajectory. The assimilation of CYGNSS wind data is more effective in improving path predictions, offering greater potential in representing vortex structures compared to conventional data assimilation and also aids in medium-range path forecasting. Ideally, the corresponding hurricane forecasts are improved within a 1-day timeframe, reducing errors in 0-48 h hurricane intensity predictions. The assimilation of VAM-CYGNSS vector winds can reduce MSW errors by 1-2.5 m/s and MSLP errors by 2-5 hPa. Statistical results of CYGNSS data assimilation are comparable to ASCAT, with reduced bias and root mean square errors. Currently, we hardly pay attention to the ocean surface wind speed error settings, but the error settings are important in the assimilation. In addition, there's nearly no study considering the correlation of ocean surface wind speed errors in data assimilation [14,95]. There is a potential that VAM is a good method to add directions for wind, but it has not been demonstrated in operational use. In this review article, most studies only evaluate the assimilation impact for one TC case, and researchers are encouraged to explore multiple TC scenarios for which results are more significant for operational use. Finally, to serve TC forecasts better, the potential of RO and GNSS-R has been explored. People can develop methods for assimilating RO data by incorporating not only atmospheric physical variables and refractivity but also bending angles, thereby reducing errors while exploring new assimilation techniques. Additionally, more advanced RO technologies, such as polarized occultation, could be developed, which would enhance precipitation forecasts and provide better observations of TC structures. Regarding GNSS-R, currently, most studies only assimilate the GNSS-R ocean surface wind. Efforts can be made to assimilate GNSS-R NBRCS or even DDM [96; 97] to capture more information from the lower-level observation. This method needs a more complex observation operator with its tangent linear operator, and we should supply the information, including satellite geometry, GNSS transmitting power, and receiver antenna parameters, which can usually be obtained from the GNSS-R Level 1 product.