# Construction of Nighttime Cloud Layer Height and Classification of Cloud Types
## 4 Results and Discussion  
### Performance of the NSRM Method  
To evaluate the performance of the NSRM method, we applied the method to global A-Train dataset composed of nighttime track on the 16 th of each month in 2009 for a total of 143 orbits. Analyses were restricted to between latitudes 60\({}^{\circ}\) N and 60\({}^{\circ}\) S. Due to the scarcity of other sources of vertical profiles, we adopted the reconstruction algorithm, or the so-called dead zone test, to evaluate the performance [18; 22; 32]. The reconstruction meant reconstituting profiles along the track, which made it comparable to the actual measurements made by active sensors. A dead zone was created by defining the selection range for potential donors as \([i-m_{1},\,i-n]\cup[i+n,\,i+m_{2}]\), which barred the selection of potential donors from the nearest \(\pm n\) pixels. This dead zone made the reconstruction mimic the process of matching off-track recipient \(n\) pixels away. Therefore, it could give an approximate indication of how well the NSRM method can be expected to perform. Figure 2 gives an example of the reconstruction of the CloudSat profiles along the first nighttime track on 16 January 2009 from 60\({}^{\circ}\) N to 60\({}^{\circ}\) S. Various cloud structures were observed along the track, providing a good illustration of the method. The blue area in the top panel represents cloud structure observed by CloudSat; the darker blue represents top layer, and the lighter blue indicates other layers. The red dots present the CTH retrieved by MODIS. In the majority of pixels (-70%), the CTH inferred from CloudSat and from MODIS were within 2 km. In the following analysis, we focused on these measurements, as the NSRM method is based on the assumption that the inputs from both sensors are reasonably accurate. In addition, as the dead zone range increased, the possibility that a suitable donor could not be found within searching range also increased (as the colorless pixels shown in the figure). The rates of failing to find a matching donor were 3.4% at 10 km, 7.1% at 50 km, 16.1% at 200 km, and 24.4% at 400 km. Diagnostic variables, including root mean square error (RMSE) and mean deviation (MD), were calculated for further analysis. These variables were defined as:  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}{(H_{i}^{\prime}-H_{i})}^{2}}{n}}, \tag{9}\]  
and  
\[MD=\frac{1}{n}{\sum\limits_{i=1}^{n}}\big{|}H_{i}^{\prime}-H_{i}\big{|}, \tag{10}\]  
where \(H_{i}^{\prime}\) and \(H_{i}\) denote estimated and observed height of cloud, respectively. To analyze the impact of choosing different values of tolerance factor \(\alpha\) and \(\beta\) and figure out the most suitable values to apply to the algorithm, we took the following steps to test each constrain separately. First, diagnostic variables were calculated for different values of \(\beta\), while \(\alpha\) was set to 0.3, a value recommended by [PERSON] et al. [22], a study that performed a similar analysis but without constraints on the BTD. Data from 16 January and 16 May 2009 were reconstructed for dead zone ranges from 50 to 400 km (Figure 3). The MD and the RMSE of CTH and CBH were given between the original C-C profiles and the reconstructed profiles. Figure 3: Mean deviation (MD) (**a,b**) and root mean square error (RMSE) (**c,d**) of CTH and CBH between the original profiles and the reconstructed profiles, changing with \(\beta\) and dead zone. Figure 2: Reconstruction of the cloud profile measured by CloudSat on 16 January 2009 from 60\({}^{\circ}\) N to 60\({}^{\circ}\) S. The original profile is shown in panel (**a**). The dead zones were set to 10 km (**b**), 50 km (**c**), 200 km (**d**), and 400 km (**e**). The red dots indicate the original cloud top height (CTH) from MODIS, while the green dots indicate the original cloud base height (CBH) from the active sensor. The results show that MD and RMSE increased when \(\beta\) or dead zone increased.