# Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry
## 3 Results and Evaluation  
### SWH Estimates  
As we can see in Figure 6, every CYGNSS satellite has approximately the same estimate result (CYGNSS 6 satellite data missing on the day), but in terms of coverage, CYGNSS 4 satellite performs better because the other satellites lack the estimated data near 40\({}^{\circ}\)S.  
Figure 5: Matching process.  
Figure 6: The CYGNSS significant wave height (SWH) estimates compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH model on 1 April 2017 (including CY1, CY2, CY3, CY4, CY5, CY7, and CY8 satellites).  
Table 1 shows the accuracy evaluation of the estimated SWH values of each satellite compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) model. It can be seen that the mean deviation of each satellite is close to \(-0.0137\) m, the RMSE is around 0.2154 m, and the correlation is greater than 0.97. Therefore, in the next experiment, arbitrary satellite data can be used to estimate the SWH.  
Figure 7 shows a comparison of the SWH values estimated from CYGNSS and the ECMWF model in April 2017 (sampled every 5 days). It can be seen that the estimation results are in good agreement with the model. Close to the equatorial region, the SWH of the sea surface is about 2.5m, but the SWH is lower in the Indian Ocean. Large SWH values mainly occur in high latitudes, especially in the southern latitudes. Due to the covering limitation of CYGNSS orbit, we cannot estimate the SWH of the Arctic Ocean or the sea around Antarctica.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Satellite** & **CY1** & **CY2** & **CY3** & **CY4** & **CY5** & **CY7** & **CY8** \\ \hline
**Bias** & \(-0.0137\) & \(-0.0136\) & \(-0.0136\) & \(-0.0136\) & \(-0.0137\) & \(-0.0137\) & \(-0.0139\) \\ \hline
**RMSE** & 0.2159 & 0.2151 & 0.2151 & 0.2158 & 0.2139 & 0.2159 & 0.2158 \\ \hline
**R** & 0.9746 & 0.9752 & 0.9737 & 0.9729 & 0.9736 & 0.9742 & 0.9745 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The CYGNSS SWH estimates and ECMWF model matching results (m).  
Figure 7: The CYGNSS SWH estimate (left) and ECMWF model SWH value (right) in April 2017.  
Comparing the distribution of the parameter \(\mathbf{a}\) and the SWH estimation value in Figure 8, it can be seen the parameter \(\mathbf{a}\) has a small change in the non-swell region but the parameter \(\mathbf{a}\) changes a lot in the swell region, so the SNR estimation is not applicable to the expansion region, and further research is needed to increase the reliability of SWH estimates by SNR in the expansion zone.  
It can be seen from Figure 9 that the residual (compared to ECMWF SWH values) near the coastline is large, and the residual on the west coast of the continent is generally less than 0 m (blue), whereas it is generally more than 0 m (red) on the east coast of the continent. The reason is probably that, under the subtropical high-pressure control, the sea breeze often lands on the east coast, which results in higher precipitation on the east coast of the continental line near the north-south regression line. The precipitation has a positive relationship with the sea surface wind, and the sea surface wind speed affects the sea surface roughness and thus affects the distribution of SWHs.  
Figure 8: Distribution of estimated coefficients in April 2017.  
Figure 9: Residual distribution of CYGNSS SWH estimates in April 2017.