# Observation and Numerical Simulation of a Windshear Case at an Airport in the Qinghai-Tibet Plateau
## 3 Results and Discussion
### Using an Alternative Turbulence Scheme  
As in [PERSON] et al. (2022) [13], the choice of turbulence parameterization scheme is studied in this paper, namely, the use of [PERSON] (1963) [16] scheme for all five domains. The wind-EDR plot from this simulation is shown in Figure 5b. There are a number of short-comings in this simulation, namely:  
1. _The jet core is rather_ weak, with a maximum wind speed of around 30 knots only;
2. The intrusion of westerly wind does not follow the actual observations as closely as in the previous simulation;
3. The EDRs appear to be rather low and do not show up a clear pattern at the interface between the easterly and westerly on 1 December 2017. Moreover, the time series of the wind speed is shown in Figure 7b. In general, using this parameterization scheme alone, there are more rapid fluctuations in the wind speed (with a larger error statistically in comparison with actual observations, in the order of 2-3 m/s, as described below), and the distinct pulses of the wind speed in the actual observation do not appear in the simulation. By comparing with the actual observations, it is again confirmed that the [PERSON] (1980) [17] scheme, at least for the inner domains, would be more useful in reproducing the windshear features at an area of complex terrain. Despite being developed in the 1980s, the [PERSON] (1980) [17] scheme is still considered to produce realistic results compared with other subscale turbulence model ([PERSON] and [PERSON], 2016) [19] and it has been implemented in many numerical weather prediction models. Alternatively, the [PERSON] (1963) [16] closure scheme may be used ([PERSON] and [PERSON], 2016) [19]. It is rather common because it is simple to implement and has high numerical stability. However, as pointed out in a number of studies (e.g., [PERSON] et al., 2010) [11], the [PERSON] scheme has only dissipative terms and it may tend to amplify the kinetic energy dissipation. This is consistent with the current results, namely, comparing Figure 5a,b; the latter generally has higher values over height and time, though the maximum value of EDR in Figure 5a is slightly larger. Moreover, both dissipative and productive terms of turbulence are present in the [PERSON] (1980) [17] scheme and it may be more realistic in simulating the coherent structures, which take the form of a number of pulses in the easterly wind in the present case (indicated by red arrows  
Figure 7: (**a**) is the simulation result of Deardorff scheme. The time series of the simulated wind speed at and around the jet cores. The three pulses of the wind speed are highlighted in arrows. (**b**) is the simulation result of the Smagorinsky scheme. in Figure 3b). This is consistent with the simulation results (Figure 7a versus Figure 7b). The mathematical formulation of the two subgrid scale turbulence closure schemes could be found in the above-mentioned references as well as the documentation of the RAMS model. To analyze the performance of the two sets of simulations, a comparison between the simulated results and the actual observations is conducted using statistical analysis. The period under consideration covers from 12 UTC to 16 UTC. The heights covered include from 100 m above ground level to 1900 m above ground level. For wind speed, the Deardorff scheme gives a mean absolute error of 1.79 m/s, whereas the Smagorinsky scheme gives 2.44 m/s. For wind direction, the former gives a mean absolute error of 15.3\({}^{\circ}\), whereas the latter gives 17.9\({}^{\circ}\). For EDR, the former gives a mean absolute error of 0.000845 m\({}^{2}\)s\({}^{-3}\), and the simulated values are positively correlated with the actual observations with a correlation coefficient squared of around 0.16; the latter gives a mean absolute error of 0.00132 m\({}^{2}\)s\({}^{-3}\), and the simulated values are negatively correlated with the actual observations with a correlation coefficient squared of \(-0.38\). As such, the Deardorff scheme is much better than the Smagorinsky scheme based on the error statistics.