Space–time correlations are fundamental to statistical theories and turbulence modelling. However, experimental studies of space–time correlations are often restricted to the requirements of high spatially- and temporally-resolved data, especially in the atmospheric surface layer (ASL). In this study, based on the simultaneous multipoint temperature fluctuations measured at different streamwise positions with the application of distributed temperature sensing, the longitudinal space–time correlations of temperature fluctuations (C_TT(r, τ)) were directly measured in the near-neutral, unstable, and stable ASL. Our results show that, unlike Taylor’s frozen turbulence hypothesis, the elliptic model can relate the space–time correlation C_TT(r, τ) to space correlation (C_TT(r_E, 0)) in the ASL, where r_E = ((r − U_eτ)² + (V_eτ)²)^1/2, U_e is the convection velocity, and V_e is the sweeping velocity. Furthermore, we also provide a predictive method for estimating C_TT(r, τ) in the ASL based on the elliptic model. With the application of our new method, C_TT(r, τ) can be estimated from one-point measurements in the near-neutral, unstable, and stable ASL by using U_e and V_e, and the predicted C_TT(r, τ) is similar to the directly measured results. This indicates that our method can be used to reconstruct C_TT(r, τ) in the ASL.

时空相关性是统计理论和湍流建模的基础。然而，时空相关性的实验研究通常仅限于对高空间和时间分辨数据的要求，特别是在大气表层 (ASL) 中。本研究基于分布式温度传感在不同流向位置同时测得的多点温度波动，在近中性区域直接测量了温度波动的纵向时空相关性（ C _TT ( r , τ )）。 ，不稳定，稳定的 ASL。我们的结果表明，与泰勒的冻结湍流假设不同，椭圆模型可以关联时空相关C_TT ( r , τ ) 与 ASL 中的空间相关性 ( C _TT ( r _E , 0))，其中r _E  = (( r  -  U _e τ ) ²  + ( V _e τ ) ² ) ^1/2 , U _e是对流速度， V _e是扫掠速度。此外，我们还提供了一种估计C _TT ( r , τ) 在基于椭圆模型的 ASL 中。应用我们的新方法，可以使用U _e和V _e从近中性、不稳定和稳定 ASL 中的单点测量值估计C _TT ( r , τ ) ，以及预测的C _TT ( r , τ ) 与直接测量的结果相似。这表明我们的方法可用于重建ASL 中的C _TT ( r , τ )。