# Multi-LEO Satellite Stereo Winds
## 4 Discussion
### Applications
#### 4.2.1 Consistency in Polar Day-Night Cloudiness
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Stereo-Wind Case**} & \multicolumn{6}{c}{**Ground Points**} & \multicolumn{6}{c}{**ERA5**} \\ \hline
**Case** & **Pole** & **Date** & **Band** & **N** & \(\mu(\mathbf{V_{w}})\) & \(\sigma(\mathbf{V_{w}})\) & \(\mu(\mathbf{V_{v}})\) & \(\sigma(\mathbf{V_{v}})\) & \(N\) & \(\mu(\mathbf{\Delta V_{w}})\) & \(\sigma(\mathbf{\Delta V_{w}})\) & \(\mu(\mathbf{\Delta V_{v}})\) & \(\sigma(\mathbf{\Delta V_{v}})\) \\  & & & & & **m/s** & **m/s** & **m/s** & **m/s** & & **m/s** & & **m/s** & **m/s** & **m/s** \\ \hline
3A & N & 21 December 2021 & LWIR & 24,702 & \(-\)0.02 & 0.23 & 0.03 & 0.18 & 45,337 & 0.80 & 3.53 & 0.94 & 3.66 \\
3B & N & 21 December 2021 & LWIR & 44,317 & \(-\)0.01 & 0.22 & 0.00 & 0.22 & 275,131 & \(-\)0.17 & 3.00 & 0.21 & 3.15 \\
3A & S & 21 December 2021 & LWIR & 15,252 & 0.00 & 0.25 & 0.10 & 0.26 & 69,251 & 0.53 & 3.65 & \(-\)0.72 & 3.52 \\
3B & S & 21 December 2021 & LWIR & 26,308 & \(-\)0.05 & 0.23 & 0.03 & 0.24 & 160,300 & 0.40 & 2.91 & \(-\)0.32 & 2.86 \\
3A & N & 9 July 2021 & LWIR & 7726 & 0.06 & 0.23 & 0.05 & 0.22 & 68,339 & 0.44 & 3.36 & 0.65 & 3.33 \\
3B & N & 9 July 2021 & LWIR & 17,451 & 0.05 & 0.24 & \(-\)0.05 & 0.22 & 94,714 & \(-\)0.20 & 3.20 & \(-\)0.82 & 3.73 \\
3A & S & 9 July 2021 & LWIR & 23,501 & \(-\)0.03 & 0.26 & 0.10 & 0.28 & 87,784 & 0.59 & 3.66 & 0.15 & 3.44 \\
3B & S & 9 July 2021 & LWIR & 31,642 & 0.03 & 0.23 & 0.00 & 0.24 & 110,322 & 0.19 & 3.00 & 0.03 & 3.64 \\
3B & S & 21 December 2021 & VIS & 63,943 & \(-\)0.01 & 0.18 & 0.03 & 0.18 & 250,146 & 0.42 & 3.18 & \(-\)0.25 & 2.95 \\
3B & N & 21 December 2021 & MVIR & 36,547 & 0.01 & 0.23 & \(-\)0.01 & 0.23 & 57,285 & \(-\)1.31 & 4.31 & \(-\)1.49 & 5.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ground-point statistics and ERA5 reanalysis comparisons. Velocity (\(V\)) components are resolved along \((u,v)\) coordinate axes. The means \(\mu\) and standard deviations \(\sigma\) of each population \(N\) are reported for the indicated case. these retrievals to establish a Gaussian model for the tri-variate distribution of height and horizontal velocity to compare with a presumed uniform distribution for clouds (a height up to 16 km and speed up to 60 m/s captures almost all retrievals). The likelihood ratio between the Gaussian model and uniform distributions provides the discrimination between clear and cloudy. In the following, we compare stereo-wind classifications to the MODIS Level-2 cloud mask at 2 km resolution [66; 67; 68] using a known challenging case, polar winter [69; 70], as an example. The MODIS cloud mask provides per pixel classifications with the labels: \"unknown\", \"cloudy\", \"uncertain clear\", \"probably clear\", and \"clear\". In this analysis, we count the number of each for all retrieval templates. There are no instances of the \"unknown\" label observed, but between \(\sim\)5% and \(\sim\)25% for the \"uncertain clear\" and \"probable clear\" labels was seen in the polar overpasses. Figure 14 is a density plot of stereo-cloudy/clear likelihood ratio versus the MODIS cloud fraction for each template, considering only templates without \"uncertain clear\" pixels. The \"probably clear\" pixels are counted as not cloudy. We can see that most of the population fits in the four corners of the plot. In most cases, the stereo classifier makes a clear choice because there is a rather clear separation between the clear-sky ground cluster and other clusters associated with cloud layers. The corners on the upper right and bottom left are occurrences where MODIS and the stereo method agree. The other corners are where they disagree. Note that the stereo method provides a cloudiness assessment for a template rather than a pixel. It categorizes templates as cloudy, based on trackable cloud features that potentially cover only \(\sim\)10% of the template. This example shows how the stereo method can potentially improve or validate cloud-masking datasets from other sensors.