# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 2 Research and Applications of RO in NWP
### Progress in the Application of RO to NWP  
Given the advantages described in Section 1.3, RO measurements can be applied to operational NWP and climate monitoring. Initially, RO data were operationally assimilated into the NWP systems of the ECMWF, NCEP, and Met Office. The results showed that the RO measurements have a significant impact on the NWP. On 12 December 2006, the ECMWF started to assimilate RO data obtained from COSMIC operationally. As shown in Figure 2, there is a significant reduction in the mean biases for short-range forecast and analysis in the southern hemisphere [37]. According to [38], after the NCEP incorporated the COSMIC RO data into their assimilation system, the anomaly correlation scores at 500 hPa for the southern hemisphere improved by 4.5 h in a seven-day prediction period. A similar result was obtained in [39] when Met Office assimilated the refractivity of GHAMP and GRACE into their operational forecast system. The forecast was improved significantly for the upper troposphere and lower stratosphere (UTLS) region in the southern hemisphere. Subsequently, the quality of the RO data was tested gradually. To test the sensitivity of RO data quality to the geopotential height and hemisphere, the authors of [40] studied the effect of COSMIC RO data at different geopotential heights on the ECMWF forecast via comparison experiments with and without RO data. The group without the RO data was called CTRL, whereas the group with the COSMIC RO data was called COS1. The fractional reduction in the root mean square (RMS) means the impact of the RO measurement on the NWP, which is defined as \((\text{RMS}_{\text{CTL}}-\text{RMS}_{\text{COS1}})/\text{RMS}_{\text{CTL}}\), where \(\text{RMS}_{\text{CTL}}\) and \(\text{RMS}_{\text{COS1}}\) denote the RMS error for the CTL and COS1, respectively. As shown in Figures 3 and 4, the RO measurement improves the forecast accuracy at heights of 100 hPa and 200 hPa in both the northern and southern hemispheres, which can be attributed to the high vertical resolution of RO in the UTLS region. However, the results for heights of 1000 hPa and 500 hPa are neutral. This is because, in the troposphere, there are already many conventional observations, such as radiosonde and radar, and the signal-to-noise ratio (SNR) of RO data toward the troposphere is relatively low. A comparison of Figures 3 and 4 shows that the RO measurement performs better in the southern hemisphere than in the northern hemisphere. This is because the emergence of RO data increased the total number of observations in the southern hemisphere, where oceans dominate and where conventional observations, such as radiosonde and ground-based observations, are unavailable. Moreover, the RO signal is minimally contaminated by clouds and precipitation, making it suitable for detecting atmospheric profiles above oceans. Thus, RO measurements provide more accurate atmospheric parameters than other observations in the southern hemisphere, such as meteorological satellites based on the detection in the visible and infrared ranges, where the clouds and precipitation could contaminate signals in this band. Many studies also support the superior performance of the RO measurement for NWP [36; 41; 42; 43; 44]. The degradation over day 6 in Figure 3a or Figure 4a may be a general phenomenon for many observations associated with synoptic systems in the troposphere, as these synoptic systems disappear within six days. Figure 2: Time series of the mean and standard deviation of short-range forecast and analysis fitted to the radiosonde temperature measurements in the southern hemisphere, for the operational numerical weather prediction (NWP) system at the European Centre for Medium-Range Weather Forecasts (ECMWF) [37]. With the assimilation of COSMIC RO data operationally by the ECMWF since December 2006, there has been an apparent improvement in the short-range forecast and analysis.