# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 5 Conclusions  
As an advanced atmospheric sounding technique, RO effectively complements other conventional observations owing to its unique advantages. RO measurements have contributed significantly toNWP and TC forecasts. In this paper, we mainly reviewed the applications of RO observations in NWP and TC forecasts. RO has many advantages such as global coverage, high accuracy and precision, superior vertical resolution, and full-time and all-weather observation. In terms of the contribution to NWP, RO promotes the accuracy of NWP, particularly in the southern hemisphere and poles, where most of the conventional measurements are unavailable. Through studies on the sensitivity of RO data to the height, the core region where RO data make most contribute to the NWP assimilation system was found to be in the range of 10-20 km. While accounting for only 7% of the total observations made in 2014 in the assimilation system, RO has the fourth-largest impact on NWP. Moreover, the amount of current RO data is approximately 10,000 soundings per day, far from the upper limit, i.e., 128,000 soundings per day, which means that more RO data can benefit NWP. In the future, with the amount of assimilated RO data expected to reach up to 16,000 soundings per day, the error in NWP can be reduced by 25% compared with the error in 2013, which is a considerable improvement. In the tropical cyclone monitoring field, RO represents a solution that overcomes the long-lasting problem of false or missed predictions. With the assimilation of RO data, the intensity and track predictions of the TC can be improved significantly. RO data, from both below 6 km and the UTLS region, contribute to this improvement. RO data below 6 km are critical in the initial stage of TC genesis, and the data in the UTLS positively influence the forecast over three days owing to the high vertical resolution of RO. Moreover, through an analysis of the simulated vertical structure of TCs, the essential structures could be restored, particularly the eye, eyewall, and rain bands, because the RO technique provides more accurate measurements of the initial water vapor fields and helps adjust the wind fields. Because of the high horizontal gradient of the water vapor and temperature of TC, the assimilation of RO data in such regions remains challenging. New approaches, such as the non-local excess phase operator, can partly solve this problem and further improve the TC forecast. With denser RO data expected in the future, a more accurate TC forecast can be anticipated. Finally, to better serve NWP and TC forecasts, the potentials of RO should be exploited. New RO techniques and strategies have emerged to increase the frequency of RO soundings, such as the Airborne RO technique, multi-system compatible receiver, and Cube Satellite Constellation. Moreover, the superior behaviors of RO can yield original features, such as the polarimetric RO technique to measure heavy precipitation, and the multi-feature receiver, which helps integrated RO and GNSS-R. With more RO missions and satellite launches, a consistent improvement in NWP and TC forecast can be expected. Some problems in the application of RO in NWP and TC forecast remain unresolved. First, the horizontal resolution of RO data remains low [63], approximately 100 km. Additionally, the water vapor and temperature below a height of 10 km cannot be solved independently [61], resulting in further bias in the assimilation system. There are noises in the lower troposphere (0-5 km) due to the comprehensive effect of multipath effects, super-refractivity, and receiver errors [75]. To solve these issues and increase the total number of RO missions, more advanced algorithms are required. Further development in the application of RO in NWP and TC forecast can be realized using original methods such as deep learning, machine learning, and big data [90]. These methods may improve the quality of RO data below a height of 10 km, based on the reanalysis of RO data accumulated over the past several decades.