野火作用下流场非平稳性显著,但传统的Reynolds平均和分解方法(Reynolds averaging and decomposition,RAAD)因平稳假设难以有效分离趋势项,常导致湍流参数高估。因此,为了揭示顺风野火过境时火场上空湍流的垂直分布与异质特性,该文提出了一种基于非平稳模型的湍流分量重构方法,围绕一次大尺度计划烧除过程中火场内气象塔不同高度处的三方向风速、温度观测数据,选用基于经验模态分解的非平稳模型,分离时变平均风、次中尺度脉动与湍流脉动,并进行湍流数据重构。然后分析了湍流特性参数随高度的变化规律,并与平稳模型获得的结果进行对比。结果表明：与RAAD方法相比,该研究所提方法显著降低了湍流参数的高估;顺风野火过境时,湍流动能与湍流动量通量在3 m和10 m高度处量级较为接近,而在20 m高度处显著高于低层;湍流感热通量的峰值则随高度增加呈递减趋势。该研究突破传统平稳性假设的局限性,提供了更符合物理本质的火场湍流统计特征,为校准耦合火-大气数值模型中的亚格子湍流参数化方案提供了关键实测依据,同时对提高近场烟雾扩散的预测精度具有重要的指导意义。

[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.