## 2 Materials and Methods  
The original version of NAVSLaM is valid for applications only over the ocean, and it only requires atmospheric data at a single level above the surface. A newer version of NAVSLaM, which is used in this study, is valid over either land or ocean surfaces, and it requires inputs of measured or modeled atmospheric data (wind speed, air temperature, and humidity) at two height levels within the ASL. When using measured data as inputs to NAVSLaM, the values are normally averaged over a 5 to 30 min time interval. The experiment took place on a coastal bluff overlooking the Monterey Bay from 16 March 2021 through 20 April 2021; data were collected nearly continuously throughout this timespan. The sensor platform itself, shown on the left in Figure 1, was placed on a coastal bluff about 70 m from the water's edge at high tide. Since this new NAVSLaM model requires inputs of wind speed and air temperature collected at two different heights above the ground, the sensor tripod platform deployed an array of sensors clustered around two different heights. The base of the platform had one HygroVUE5 (Campbell Scientific) hygrometer fixed at 1.2 m above the surface with a CSAT3 sonic anemometer (Campbell Scientific) at 1.1 m. At the top of the platform, a second HygroVUE5 hygrometer and CSAT3 anemometer (both shown in Figure 1 on the right) were fixed at 4.4 and 4.2 m above the ground, respectively. Height measurements were taken from the center of the instrument to the surface. The hygrometers were enclosed in solar radiation shields, and a GPS unit was used for timestamps. All the data were recorded using a CR6 dataloger (Campbell Scientific); the CSAT3s were sampled at 50 Hz while the hygrometers recorded a sample every 5 s.  
In addition to providing wind speed, the sonic temperatures recorded by the two CSAT3 anemometers were used for point estimates of the refractive index structure parameter \(C_{n}^{2}\). These CSAT3-derived values of \(C_{n}^{2}\) were then compared to the NAVSLaM  
Figure 1: Sensor platform (**left**) and close-up of two of the sensors (**right**). The upper sensor is a HygroVUE5 hygrometer, and the one on the right is a CSAT3 sonic anemometer. predictions at the respective heights of the sonics. This approach provided sufficient vertical resolution to discern the differences between \(C_{n}^{2}\) at the measured heights in a robust, weatherized platform. The conversion of the sonic anemometer data to \(C_{n}^{2}\) is a multi-step process. First, the temperature structure-function, \(C_{T}^{2}\), can be estimated from sonic temperatures measured using the CSAT3s. These data were split into tranches with 8192 samples each (corresponding to 164 s of collection time acquired at 50 Hz), then an estimated power spectral density (PSD) was obtained using [PERSON]'s method applied to each tranche. The relationship of the PSD \(S_{T}\) and frequency-dependent temperature structure function \(C_{T}^{2}\) is given by [5]:  
\[C_{T}^{2}(f)=4\bigg{(}\frac{2\pi}{U}\bigg{)}^{2/3}S_{T}(f)f^{5/3} \tag{1}\]  
where \(U\) is the mean wind speed as measured by the sonic anemometers for a particular tranche. This equation assumes that \(f\) is a temporal frequency that corresponds to a spatial frequency within the inertial subrange. Hence, \(S_{T}(f)\) plotted on a log-log plot should be linear with a characteristic \(-5/3\) slope within temporal frequencies that correspond to the (spatial) inertial subrange; a linear fit to the log-log data with this slope was used to extract the value of \(C_{T}^{2}\) for each tranche of data. For this experiment, a frequency span between 1 and 5 Hz was chosen to estimate \(C_{T}^{2}\), as the PSD slope was near \(-5/3\) throughout the collection period within this frequency window. Turbulent cell sizes that are smaller than a sonic anemometer's transducer experience a path-averaging effect that attenuates the PSD spectrum at higher frequencies [10; 11].