NSF Award
2318221
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.<br/><br/>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.<br/><br/>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.
