Stratocumulus As a Gravity Wave Observatory

大气层充满了内部重力波，就像你在水面上看到的波一样，但它发生在天空的每一个高度。这些无数重叠的内波有许多波长，许多深度和许多传播速度，这取决于它们的许多来源。来源包括风切变，云和风暴，吹过山脉的风，以及鲜为人知的大尺度湍流，这些湍流可以从急流和其他风特征中产生。在波峰处，空气可能饱和并形成可见的云。你经常可以在天空中看到短波（波长小于几公里）作为条纹云图案。较长的波（100公里或更长）在时间上更多地作为天空覆盖的周期性脉冲，但从卫星图像上可以看到它们是平行的条纹或带状物或云量增加或减少的弧。这些条纹与运动波相协调，将我们可以在动画中跟踪的各个云特征聚集在一起并分散开来。在低云层（层积云）甲板上，这在海洋的凉爽部分特别常见，这些波特别明显，并且很容易测量，跟踪和收集仅从卫星图像的统计数据。这些非常直接的相机测量的统计数据将告诉科学家很多关于经常神秘的波源的性质，波的传播路径，以及波如何调制单个云粒子和云特征或细胞的性质。该项目还将照亮整个云层甲板上的波浪效果。例如，一个非常深的云层增厚事件可以导致降水，从而导致长期的天空晴朗，而即使是适度的振幅波驱动云量接近50%，否则多云或晴朗的天空。这些甲板规模的净效应可能对地球的热量收支很重要，因为云层反射阳光。利用计算机档案中现有的大量卫星图像，该项目将能够阐明波浪的季节性，甚至寻找几十年来波浪度或波浪特征的趋势。为了测量这些波浪，该项目将使用层积云区域的图像对，使用粒子图像速度测量（PIV）软件跟踪特征或纹理元素，同时计算时间上的亮度差异。PIV测量的水平速度主要是平流（顺风运动），但速度的空间梯度是有用的。在云特征会聚和亮度增加的地方，特别是在可以在许多卫星图像帧中跟踪的周期性细长区域中，可以推断出上升的波峰，并且可以估计其波列的属性。在某些情况下，一个明确的来源可以被识别（如飓风），但在许多情况下，来源可能很远或不清楚，假设风场中包含微妙的平流非线性。该项目将寻求自动化和优化波识别算法（PIV，光流），并将该技术扩展到夜间识别的红外图像（具有较低的分辨率和动态范围），以尽量减少虚假识别等。随着算法的改进，它们将被部署用于摄取数十年的卫星图像档案。其结果将是波度数据集，供社区和项目工作人员共享，以描述和理解这些神秘但无处不在的云调制波现象的来源，属性和影响。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The atmosphere is full of internal gravity waves, something like the waves you can see on a water surface but occurring at every altitude in the sky. These myriad overlapping internal waves have many wavelengths, and many depths, and many propagation speeds, depending on what are their many sources. Sources include wind shear, clouds and storms, wind blowing over mountains, and little-understood larger-scale shudders that can emanate from jet streams and other wind features. At the crest of a wave, air may saturate and form a visible cloud. You can often see short waves (less than a few km wavelength) in the sky as striped cloud patterns. Longer waves (100 km or more) are experienced more as periodic pulses of sky cover in time, but they can be seen from satellite images as parallel stripes or belts or arcs of enhanced and reduced cloudiness. These stripes are coordinated with waves of motion, drawing together and spreading apart the individual cloud features which we can track in animations. In low cloud (stratocumulus) decks, which are especially common over the cool parts of the ocean, these waves are especially obvious, and easy to measure, track, and gather statistics about from satellite imagery alone. These statistics of very direct camera measurements will tell scientists a lot about the nature of the often-mysterious wave sources, wave propagation pathways, and the nature of how waves modulate individual cloud particles, and cloud features or cells. The project will also illuminate wave effects on the cloud deck as a whole. For instance, a very deep cloud thickening event can lead to precipitation and thus a long-lasting sky clearing, while even modest amplitude waves drive the cloud cover toward 50% in otherwise overcast or clear skies. These deck-scale net effects may be important to Earth’s heat budget, as clouds reflect sunlight. With the massive quantities of satellite imagery now available in computer archives, this project will be able to elucidate wave seasonality and even look for trends in waviness or wave characteristics across the decades.To measure these waves, the project will work with image pairs over stratocumulus areas, tracking features or texture elements with Particle Image Velocimetry (PIV) software while simultaneously computing brightness differences in time. The horizontal velocity measured by PIV is mostly advective (motion downwind), but spatial gradients in velocity are informative. Where cloud features converge and brightness increases, especially in periodic elongated zones that can be tracked across many satellite image frames, a rising wave crest can be inferred, and the properties of its wave train can be estimated. In some cases, a clear source can be identified (like a hurricane), but in many cases the source may be far away or unclear, hypothesized to include subtle advective nonlinearities in the wind field. The project will seek to automate and optimize wave identification algorithms (PIV, optical flow), and to extend the technique to infrared imagery (with lower resolution and dynamic range) for nighttime identification, to minimize spurious identifications, etc. To engage a community of expertise, the project will focus initially on well-studied recent situations such as field campaigns. As the algorithms are improved, they will be deployed to ingest many decades of satellite imagery archives. The result will be datasets of waviness, to be shared for community as well as project staff efforts to characterize and understand the sources, properties, and impacts of these mysterious but ubiquitous cloud-modulating wave phenomena.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.