The global navigation satellite system (GNSS) radio occultation (RO) technique is an atmospheric sounding technique that originated in the 1990s. The data provided by this approach are playing a consistently significant role in atmospheric research and related applications. This paper mainly summarizes the applications of RO to numerical weather prediction (NWP) generally and specifically for tropical cyclone (TC) forecast and outlines the prospects of the RO technique. With advantages such as high precision and accuracy, high vertical resolution, full-time and all-weather, and global coverage, RO data have made a remarkable contribution to NWP and TC forecasts. While accounting for only 7% of the total observations in European Centre for Medium-Range Weather Forecasts’ (ECMWF’s) assimilation system, RO has the fourth-largest impact on NWP. The greater the amount of RO data, the better the forecast of NWP. In cases of TC forecasts, assimilating RO data from heights below 6 km and from the upper troposphere and lower stratosphere (UTLS) region contributes to the forecasting accuracy of the track and intensity of TCs in different stages. A statistical analysis showed that assimilating RO data can help restore the critical characteristics of TCs, such as the location and intensity of the eye, eyewall, and rain bands. Moreover, a non-local excess phase assimilation operator can be employed to optimize the assimilation results. With denser RO profiles expected in the future, the accuracy of TC forecast can be further improved. Finally, future trends in RO are discussed, including advanced features, such as polarimetric RO, and RO strategies to increase the number of soundings, such as the use of a cube satellite constellation.

The transition of the freeze–thaw state of the land surface soil occurs every year with the season and is closely related to the human living environment. The freezing and thawing changes of the ground surface have important effects on hydrological activities, meteorological conditions, and ecological gas dynamics. Traditional monitoring methods have their limitations. In the past two decades, the emerging GNSS-R/IR (Global Navigation Satellite System-Reflectometry/Interference Reflectometry) technology has provided a new method for monitoring the surface f state; however, fewer works have paid attention to the scattering mechanism models in the current study. In this paper, a forward GNSS multipath model suitable for a complex cold surface is developed. The dielectric constant model with different surface parameters is added. The calculation of snow layer attenuation is employed to take the snow cover into consideration. Based on the first-order radiation transfer equation model, a polarization synthesis method is used to obtain the circularly and linearly polarized vegetation specular scattering characteristics. The surface characteristics and antenna model are coupled. A more detailed forward GNSS multipath model of frozen and thawed soil under complex surface conditions is established. The model is used to simulate and analyze the forward GNSS multipath (Signal to Noise Ratio (SNR), phase and pseudorange) responses of frozen and thawed soil under complex surface conditions (soil salinity, snow and vegetation coverage). Studies have shown that when the soil changes from freezing to thawing due to the change in the phase of the water in the soil, the dielectric constant and BRCS (bi-static radar cross-section) increase, causing the increase in the amplitude of the multipath observation. The higher the salinity content, the larger the amplitude of the multipath observation. The attenuation of the snow cover and the vegetation layer will lead to the reduction of the multipath observation amplitude. For the first time, the model developed by this paper reveals the GNSS multipath observation response of frozen and thawed soil under complex surface conditions in detail, which can provide some theoretical support for subsequent experimental design and data analysis.

In radio occultation (RO) data processing and data assimilation, the forward model (FM) is used to calculate bending angle (BA) from refractivity (N). The accuracy and precision of forward modeled BA are affected by refractivity profiles and FM methods, including Abel integral algorithms (direct, exp, exp_T, linear) and methods of interpolating refractivity during integral (log-cubic spline and log-linear). Experiment 1 compares these forward model methods by comparing the difference and relative difference (RD) of the experimental value (forward modeled ECMWF analysis) and the true value (BA of FY3D RO data). Results suggested that the exp with log-cubic spline (log-cubic) interpolation is the most accurate FM because it has better integral accuracy (less than 2%) to inputs, especially when the input is lower than an order of magnitude of 1 × 10⁻² (that is, above 60 km). By contrast, the direct induced a 10% error, and the improvement of exp T to exp is limited. Experiment 2 simulated the exact errors of an FM (exp) based on inputs on different vertical resolutions. The inputs are refractivity profiles on model levels of three widely used analyses, including ECMWF 4Dvar analysis, final operational global analysis data (FNL), and ERA5. Results demonstrated that based on exp and log-cubic interpolation, BA on model level of ECMWF 4Dvar has the highest accuracy, whose RD is 0.5% between 0–35 km, 4% between 35–58 km, and 1.8% between 58–80 km. By contrast, the other two analyses have low accuracy. This paper paves the way to better understanding the FM, and simulation errors on model levels of three analyses can be a helpful FM error reference.