Abstract. An experiment was conducted to study turbulence along a 149-km path between the Mauna Loa and Haleakala mountain tops using digital cameras and light-emitting diode (LED) beacons. Much of the path is over the ocean, and a large portion of the path is 3 km above sea level. On the Mauna Loa side, six LED beacons were placed in a roughly linear array with pair spacings from 7 to 62 m. From the Haleakala side, a pair of cameras separated by 83.8 cm observed these beacons. Turbulence along the path induces tilts on the wavefronts, which results in displacements of the LED spots in the images. The image motion is caused by unwanted noise sources such as camera platform motion. Differential motion between spots cancels much of this noise, and this differential motion is weighted by the turbulence along the path in different ways depending on the geometry between the sources and the cameras. A camera motion insensitive weighting function is developed to deal with this observational issue. A linear combination of these weighting functions is then used to generate a composite weighting function, which better rejects turbulence near the sources and receivers and is most sensitive to turbulence in the portion of the path out over the ocean. This technique is used to estimate turbulence in this region. The long range involved caused very strong scintillation in the image, which added new challenges to the data processing. A resulting estimate for Cn2 of 4 × 10 − 17 m − 2 / 3 is in good agreement with the Hufnagel–Valley HV5/7 model and the results of numerical weather modeling.

中文翻译：

---

149 公里路径上的光学湍流测量

摘要。使用数码相机和发光二极管 (LED) 信标进行了一项实验，以研究 Mauna Loa 和 Haleakala 山顶之间 149 公里路径上的湍流。大部分路径都在海洋上空，大部分路径位于海平面以上 3 公里处。在 Mauna Loa 一侧，六个 LED 信标被放置在一个大致线性阵列中，对间距从 7 到 62 m。从哈雷阿卡拉一侧，一对相距 83.8 厘米的摄像机观察到这些信标。沿路径的湍流会导致波前倾斜，从而导致图像中 LED 光点的位移。图像运动是由不需要的噪声源引起的，例如相机平台运动。点之间的微分运动抵消了大部分噪音，并且这种微分运动由沿路径的湍流以不同方式加权，具体取决于源和相机之间的几何形状。开发了相机运动不敏感加权函数来处理这个观察问题。然后使用这些加权函数的线性组合来生成复合加权函数，它可以更好地抑制源和接收器附近的湍流，并且对海洋上方路径部分的湍流最敏感。该技术用于估计该区域的湍流。所涉及的远距离在图像中造成非常强烈的闪烁，这给数据处理增加了新的挑战。Cn2 的结果估计值为 4 × 10 − 17 m − 2 / 3，与 Hufnagel-Valley HV5/7 模型和数值天气模型的结果非常一致。