Large-eddy simulation (LES) of wind farms is often limited by the computational cost required to represent many turbine rows and to obtain statistically converged wake and power statistics. Here, we present LES of an extended wind-farm configuration using the PALM model system, where cyclic lateral boundary conditions are employed to emulate interior-farm interaction in an idealized neutral boundary layer. The setup consists of nine identical horizontal-axis wind turbines arranged in a staggered array within the computational domain. Time-averaged hub-height fields show coherent wake corridors with a mean inflow-speed reduction of $23.7$% (array-mean across turbines) relative to an undisturbed background wind speed, and peak wake deficits reaching $71.4$% in the near-wake region. Turbulence levels increase markedly in the wake shear layers, with hub-height turbulence intensity enhanced by $32.2$% in the rotor region compared to background conditions; correspondingly, the peak hub-height SGS-TKE increases by a factor of 6.74 relative to background. Vertically averaged profiles indicate a momentum deficit within the turbine layer and gradual recovery aloft; the streamwise turbulent momentum flux remains predominantly negative, demonstrating the downward transport of higher-momentum air from above as a key recovery mechanism. Turbine rotor-power statistics show an initial adjustment followed by a quasi-stationary regime, with a farm-mean rotor power of $1.93$ MW and persistent inter-turbine variability characterized by a mean coefficient of variation of $61.2$%. Overall, the results demonstrate that the proposed extended-farm LES approach enables computationally efficient quantification of wake dynamics, vertical momentum transport, and their impact on power variability under idealized neutral wind-farm conditions.

Building-resolving urban climate simulations require detailed three-dimensional geospatial data that are often unavailable for hot-arid cities. This study examines whether a Local Climate Zone (LCZ)-based workflow can provide a physically interpretable urban representation under such conditions. The LCZ-wizard was used to generate a heterogeneous $2\times 2$ km domain containing ten built and four natural LCZ classes in an Aswan-motivated hot-arid setting. A clear-sky summer simulation was conducted with the PALM model system at 2 m horizontal resolution. LCZ-conditioned distributions of near-surface air temperature and the Universal Thermal Climate Index (UTCI) were analysed. Mean daytime air temperatures differed by only 0.57 K among built classes, whereas the nighttime difference between compact high-rise LCZ 1 and lightweight low-rise LCZ 7 reached 5.42 K. Daytime UTCI showed a clearer morphological signal: LCZ 1 had the lowest built-class mean (36.33 °C), while LCZ 7 had the highest (38.72 °C), a pattern consistent with the combined radiative, geometric, aerodynamic, and thermal properties of the compact LCZ representation. This ordering reversed at night, with compact classes exhibiting greater nocturnal heat retention. The daytime UTCI 95th percentile exceeded the 46 °C extreme-heat-stress threshold in LCZs 1–8, showing that exposed locations remain hazardous even where class-mean conditions are more favourable. The mean daytime UTCI of the dense-tree class was 4.74 K lower than that of bare soil or sand under the prescribed soil and vegetation conditions. The results reveal a diurnal trade-off for the prescribed case: compact classes are associated with lower daytime radiant exposure but greater nocturnal heat retention. The numerical values represent one idealised clear-sky, low-wind summer situation and should not be interpreted as a validated climatology of Aswan or as directly transferable to other wind and stability regimes. The study provides a proof of concept for using LCZ-derived PALM domains in controlled process studies of data-scarce hot-arid cities.