Objective: Wildfire spread exhibits a high degree of uncertainty, largely owing to complex fire-atmosphere interactions. Heat released during combustion induces rapid temperature increases and highly variable airflow, creating a strongly nonstationary environment. However, existing studies typically rely on the Reynolds averaging and decomposition(RAAD) method to analyze fire-induced turbulence in airflow. RAAD assumes that the airflow field is stationary within a fixed averaging window, an assumption that fundamentally contradicts the transient nature of wildfire environments. Consequently, RAAD often fails to effectively separate large-scale trends and submesoscale motions from genuine turbulent fluctuations, leading to a systematic overestimation of turbulence parameters. This study aims to overcome the limitations of traditional stationarity assumptions by proposing a turbulence reconstruction method based on a nonstationary model. The primary objective is to accurately characterize the vertical distribution and heterogeneous features of turbulence over a fire zone during the passage of a heading fire, thereby providing a reliable physical basis for improving wildfire behavior prediction, smoke transport simulation, and the parameterization of coupled fire-atmosphere numerical models. Methods: High-frequency(10 Hz) three-dimensional wind speed and temperature data were obtained from a large-scale prescribed burn experiment conducted in the New Jersey Pine Barrens and archived by the USDA Forest Service Research Data Archive. The analysis focused on measurements collected from a meteorological tower located within the burn unit during the passage of a heading fire. Three sonic anemometers were installed at heights of 3, 10, and 20 m. To address the nonstationarity of the observations, empirical mode decomposition(EMD) was employed. EMD is an adaptive, data-driven signal processing technique suited for analyzing nonlinear and nonstationary processes. Unlike RAAD, which employs a fixed time-averaging window, EMD decomposes wind and temperature signals into a set of intrinsic mode functions based on local characteristic time scales. This method separated the original signal into three components: a time-varying mean wind flow, sub-mesoscale motions(low-frequency trends), and turbulent fluctuations. A frequency threshold of 0.01 Hz was applied to distinguish turbulent motion from low-frequency motions. The turbulence components were then reconstructed to calculate key parameters, including turbulence kinetic energy(TKE), friction velocity, and sensible heat flux. These results were rigorously compared with those obtained using the traditional RAAD method to quantify the biases introduced by the stationarity assumption. Results: Spectral analysis validated the proposed method, as the reconstructed turbulence power spectra at all measurement heights closely followed the theoretical -2/3 slope in the inertial subrange. The comparative analysis revealed several critical findings: 1. The RAAD method significantly overestimated turbulence intensity. Peak TKE values derived from RAAD were approximately twice those obtained using the nonstationary EMD-based method. Similarly, peak friction velocities calculated using RAAD were approximately four times higher than those derived from the proposed method. This discrepancy indicates that RAAD misinterpreted rapid nonstationary trends(e.g., the abrupt increase in wind speed induced by the advancing fire front) as turbulent fluctuations. 2. Analysis of the reconstructed data revealed distinct vertical patterns. During the passage of the heading fire, the magnitudes of TKE and turbulent momentum flux at heights of 3 and 10 m were comparable. By contrast, values at a height of 20 m were significantly higher than those in the lower layers, suggesting enhanced shear or entrainment activity near the canopy top or at the plume-atmosphere interface. 3. A crucial discrepancy was observed in the vertical distribution of sensible heat flux. The nonstationary model showed that the peak turbulent sensible heat flux was highest at 3 m and decreased with height (3 m > 10 m > 20 m), which is consistent with the physical reality that the primary heat source is located in the surface fuel bed. By contrast, the traditional RAAD method yielded physically unreasonable results, showing higher heat flux peaks at 20 m than near the surface. This error arose because RAAD failed to filter out nonturbulent, low-frequency temperature ramps caused by the approaching fire front. Conclusions: This study demonstrates that the traditional stationarity assumption is not applicable to the highly transient environment of a heading wildfire. By employing an EMD-based nonstationary model, the proposed method effectively isolated the time-varying mean flow and sub-mesoscale motions, thereby retrieving physically representative turbulence statistics. The results confirm that traditional methods systematically overestimate turbulence parameters and can distort the vertical profile of heat transport. The corrected turbulence characteristics identified in this study—specifically, the vertical attenuation of sensible heat flux and the distinct intensification of turbulent momentum flux at higher altitudes—offer critical empirical evidence for calibrating subgrid-scale turbulence parameterization schemes in coupled fire-atmosphere models (e.g., WRF-Fire). Furthermore, accurate quantification of friction velocity and TKE offers significant guidance for improving the prediction accuracy of firebrand transport (spotting) and near-field smoke dispersion.