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Todd P. Lane, Robert D. Sharman, Stanley B. Trier, Robert G. Fovell, and John K. Williams

Anyone who has flown in a commercial aircraft is familiar with turbulence. Unexpected encounters with turbulence pose a safety risk to airline passengers and crew, can occasionally damage aircraft, and indirectly increase the cost of air travel. Deep convective clouds are one of the most important sources of turbulence. Cloud-induced turbulence can occur both within clouds and in the surrounding clear air. Turbulence associated with but outside of clouds is of particular concern because it is more difficult to discern using standard hazard identification technologies (e.g., satellite and radar) and thus is often the source of unexpected turbulence encounters. Although operational guidelines for avoiding near-cloud turbulence exist, they are in many ways inadequate because they were developed before the governing dynamical processes were understood. Recently, there have been significant advances in the understanding of the dynamics of near-cloud turbulence. Using examples, this article demonstrates how these advances have stemmed from improved turbulence observing and reporting systems, the establishment of archives of turbulence encounters, detailed case studies, and high-resolution numerical simulations. Some of the important phenomena that have recently been identified as contributing to near-cloud turbulence include atmospheric wave breaking, unstable upper-level thunderstorm outflows, shearing instabilities, and cirrus cloud bands. The consequences of these phenomena for developing new en route turbulence avoidance guidelines and forecasting methods are discussed, along with outstanding research questions.

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Amy McGovern, Kimberly L. Elmore, David John Gagne II, Sue Ellen Haupt, Christopher D. Karstens, Ryan Lagerquist, Travis Smith, and John K. Williams

Abstract

High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as the impact on savings through renewable energy. Prediction of these events has improved substantially with greater observational capabilities, increased computing power, and better model physics, but there is still significant room for improvement. Artificial intelligence (AI) and data science technologies, specifically machine learning and data mining, bridge the gap between numerical model prediction and real-time guidance by improving accuracy. AI techniques also extract otherwise unavailable information from forecast models by fusing model output with observations to provide additional decision support for forecasters and users. In this work, we demonstrate that applying AI techniques along with a physical understanding of the environment can significantly improve the prediction skill for multiple types of high-impact weather. The AI approach is also a contribution to the growing field of computational sustainability. The authors specifically discuss the prediction of storm duration, severe wind, severe hail, precipitation classification, forecasting for renewable energy, and aviation turbulence. They also discuss how AI techniques can process “big data,” provide insights into high-impact weather phenomena, and improve our understanding of high-impact weather.

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James Edson, Timothy Crawford, Jerry Crescenti, Tom Farrar, Nelson Frew, Greg Gerbi, Costas Helmis, Tihomir Hristov, Djamal Khelif, Andrew Jessup, Haf Jonsson, Ming Li, Larry Mahrt, Wade McGillis, Albert Plueddemann, Lian Shen, Eric Skyllingstad, Tim Stanton, Peter Sullivan, Jielun Sun, John Trowbridge, Dean Vickers, Shouping Wang, Qing Wang, Robert Weller, John Wilkin, Albert J. Williams III, D. K. P. Yue, and Chris Zappa

The Office of Naval Research's Coupled Boundary Layers and Air–Sea Transfer (CBLAST) program is being conducted to investigate the processes that couple the marine boundary layers and govern the exchange of heat, mass, and momentum across the air–sea interface. CBLAST-LOW was designed to investigate these processes at the low-wind extreme where the processes are often driven or strongly modulated by buoyant forcing. The focus was on conditions ranging from negligible wind stress, where buoyant forcing dominates, up to wind speeds where wave breaking and Langmuir circulations play a significant role in the exchange processes. The field program provided observations from a suite of platforms deployed in the coastal ocean south of Martha's Vineyard. Highlights from the measurement campaigns include direct measurement of the momentum and heat fluxes on both sides of the air–sea interface using a specially constructed Air–Sea Interaction Tower (ASIT), and quantification of regional oceanic variability over scales of O(1–104 mm) using a mesoscale mooring array, aircraft-borne remote sensors, drifters, and ship surveys. To our knowledge, the former represents the first successful attempt to directly and simultaneously measure the heat and momentum exchange on both sides of the air–sea interface. The latter provided a 3D picture of the oceanic boundary layer during the month-long main experiment. These observations have been combined with numerical models and direct numerical and large-eddy simulations to investigate the processes that couple the atmosphere and ocean under these conditions. For example, the oceanic measurements have been used in the Regional Ocean Modeling System (ROMS) to investigate the 3D evolution of regional ocean thermal stratification. The ultimate goal of these investigations is to incorporate improved parameterizations of these processes in coupled models such as the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) to improve marine forecasts of wind, waves, and currents.

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

Capsule Summary

Current research applying artificial intelligence to the Earth and environmental sciences is progressing quickly, with emerging developments in terms of efficiency, accuracy, and discovery.

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Molly Baringer, Mariana B. Bif, Tim Boyer, Seth M. Bushinsky, Brendan R. Carter, Ivona Cetinić, Don P. Chambers, Lijing Cheng, Sanai Chiba, Minhan Dai, Catia M. Domingues, Shenfu Dong, Andrea J. Fassbender, Richard A. Feely, Eleanor Frajka-Williams, Bryan A. Franz, John Gilson, Gustavo Goni, Benjamin D. Hamlington, Zeng-Zhen Hu, Boyin Huang, Masayoshi Ishii, Svetlana Jevrejeva, William E. Johns, Gregory C. Johnson, Kenneth S. Johnson, John Kennedy, Marion Kersalé, Rachel E. Killick, Peter Landschützer, Matthias Lankhorst, Tong Lee, Eric Leuliette, Feili Li, Eric Lindstrom, Ricardo Locarnini, Susan Lozier, John M. Lyman, John J. Marra, Christopher S. Meinen, Mark A. Merrifield, Gary T. Mitchum, Ben Moat, Didier Monselesan, R. Steven Nerem, Renellys C. Perez, Sarah G. Purkey, Darren Rayner, James Reagan, Nicholas Rome, Alejandra Sanchez-Franks, Claudia Schmid, Joel P. Scott, Uwe Send, David A. Siegel, David A. Smeed, Sabrina Speich, Paul W. Stackhouse Jr., William Sweet, Yuichiro Takeshita, Philip R. Thompson, Joaquin A. Triñanes, Martin Visbeck, Denis L. Volkov, Rik Wanninkhof, Robert A. Weller, Toby K. Westberry, Matthew J. Widlansky, Susan E. Wijffels, Anne C. Wilber, Lisan Yu, Weidong Yu, and Huai-Min Zhang
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