## 3 Results
### Mode Bias Corrections  
The retrieval of SWH from the two modes of satellite operation considered can be compared by considering locations where the satellite changes mode over the open ocean. In these locations when gridding the SWH onto the used 100 km resolution grid the SWH from both modes are likely to be considered for a grid cell. By considering the mean LRM derived SWH to the mean SAR SWH from such grid cells a mode to mode bias can be  
Figure 6: Comparison of our CS2 derived SWH and other satellite products. The top row is Met Office buoy K7 2014 to 2016 and our SWH and RADS data. The second row is a closer look at buoy K7 over 2018 with additional SAMOSA data. Row three is NDBC buoy 48,214 in 2014-(08 to 10). The bottom row is for the Swift floating wave-buoys deployed in the Beaufort sea in 2014-(08 to 09). The blue lines are the complete buoy wave height record, with crosses indicating the 6 hour mean value coinciding with a satellite record within 100 km of the buoy location. calculated. Approximately 10,000 such locations were found for the daily collected data over the 9 year of processed SWH. Measurements are ignored if there are fewer than 4 LRM or SAR retrievals. Figure 7 shows correlation between mode offset and average retrieved SWH and the seasonal cycle of mode offset. The left panel shows a clear correlation between the bias and the wave state. For SWH \(<\)5 m, the SAR mode gives a lower SWH, with a higher value for SWH \(>\)5 m, although there is wider distribution for higher SWH. There is also a seasonal development with a lower offset in summer when wave heights are typically lower. [17] incorporate CS2 data into their combined data set using the RADS data product. As [17] have performed extensive validation of the RADS SWH data product to a long time series of in situ observations, comparing our new SWH data product with the corrected RADS data enables to evaluate the performance of our new SWH against the current scientific standard. The RADS SWH data, \(s\) is adjusted to a corrected SWH\({}_{\text{C}}\) using a 3 rd order polynomial with  
\[\text{SWH}_{\text{c}}=\ \left\{ \begin{array}{ll}(0.4889+0.4712s+0.1546s^{2}-0.0145s^{3})&s\leq 2.45\\ (-0.1057+1.0058s)&s>2.45\end{array} \right. \tag{7}\]  
in order to correlate various satellite measurements with a buoy measurement archive. The correction given by Equation (7) is displayed as the green line in Figure 8. Figure 6 shows that our SAR mode SWH is highly correlated with in situ wave buoy measurements, while 7 shows that our new SWH has an emergent bias between LRM and SAR modes which can be used to adjust the SWH SWH\({}_{L}\) retrieved in LRM mode to the corrected SWH\({}_{Lc}=0.38+\text{SWH}_{L}/1.07\) by using the slope and intercept in Figure 7. We then compare our adjusted SWH (noting that the new SWH collected in SAR mode remains unchanged) to the corrected RADS data presented by [17] (second row of Figure 8). The best fit slope becomes closer to unity for all cases shown in Figure 8. For LRM mode, in the scatter plot between the raw data (top left), there is a visible correlation between the scatter and the green correction line of [17] for SWH \(<\)1.5 m. The corrected version has no such shape and also has a best fit slope of near unity (0.97), and intercept of 0.01 m. As the analysis of [17] considers buoy data at lower latitudes, where CS2 operates in LRM mode, this result shows that our adjusted LRM SWH is in close correlation with existing corrected satellite SWH data. As our corrected LRM is matched to our raw SAR data this result (along with the improved slope to the SAR in the central column of Figure 8) shows that our SAR mode retrieved SWH is marked improvement on the pLRM processed RADS data, particularly in regions close to the sea ice edge. Figure 7: Difference between our retrieved SAR and LRM mode SWH 2011–2019. Plot to the left compares all coincident LRM and SAR mode SWH retrievals vs. the unscaled SWH.