# Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
## 3 Results
### Model Sensitivity to Image Resolution  
When the testing scans are down-sampled to half resolution (from a range gate of 50 m to an effective range gate of 100 m) and input into the CNN for wake identification and characterization, the success rate declines. However, the model remains skillful in identifying and correctly masking wakes when present in scans, with a 92.19% success rate (Table 3). This down-sampling exercise is meant to indicate whether large-area scans covering a large portion of an entire wind plant can be used to detect the approximate location and extent of individual wakes. Down-sampling the scans results in an increase in subjectively identifiable wake fragments (111 wake fragments under half-resolution, 98 under full resolution). This is illustrated by Figure 12, where all three wakes in the full resolution scan are more fragmented when subjected to half-resolution down-sampling. When considering the failure modes for wake fragments under half-resolution, 71% of model failures under half-resolution are attributed to false negatives (the CNN is unable to detect the wake fragments when they are present). Thus, the high success rate for wake fragment identification under full resolution is likely attributed to specific flow patterns that differentiate wake fragments from other local, low-velocity turbulent flow. The number of insufficiently masked wakes (IoU \(<\) 60%) increases, with insufficient wake masks representing 20% of the model failure. Wake asymmetry, width and length for all complete (boundaries completely included in the scan) wakes and wake fragments that exhibit adequate IoU when compared to subjective annotation (\(\geq\) 0.6) exhibit some sensitivity to input data resolution, and there is a clear reduction in IoU for the half-resolution scans irrespective of the prevailing atmospheric conditions (Figure 13). Down-sampling results in an increase in the calculated median wake width for all scans (Figure 13). Further, the normalized wind fields exhibit wakes that become more irregularly shaped, indicated by an increasing asymmetry (Figure 12). Note that these results are reasonable, as a spatial resolution of 100 m is rather coarse for  
Figure 12: (a) Full-resolution scan image; (b) half-resolution scan image. The reduction in resolution results in increased number of wake fragments and modified wake characteristics. Scan parameters: arc scan, captured on 11 September 2018, 05:14 UTC, elevation angle \(\varnothing\) = 1.5\({}^{\circ}\), azimuth angle \(\theta\in[\mathbf{290}^{\circ},\mathbf{345}^{\circ}]\), azimuthal resolution 1\({}^{\circ}\), range gate 50 m, 55 s accumulation time. the 90-m diameter wind turbines generating the wakes. For each inflow class (Table 2), the sample size of full and complete wakes that are accurately characterized by the CNN is greater than 20. Although wake characteristics exhibit marked differences according to atmospheric conditions, the overall relative differences in the wake width, length and asymmetry magnitude between each atmospheric class are similar for full and half resolutions. The IoU varies between classes of atmospheric conditions in a similar manner to wake width, with both being highest under daytime low wind speeds and nighttime high wind speeds. Conversely, the wake length is largest for the higher wind speeds in both daytime and nighttime scans, and smallest for low wind speeds and daytime conditions, when unstable stratification is expected to erode wakes more rapidly (Figure 13). Intra-class patterns in wake width generally follow trends in the model IoU, potentially indicating that the model accuracy is more sensitive to wake width than wake length. This could be attributed to the existence of a minimum threshold of wake width or length needed for wakes to be visibly detected; in this case, it is likely that wakes will have an adequate length to be detected when compared to width (which is a numerically smaller dimension, as evidenced in Figure 13).