Improving Understanding and Diagnosis of Jet-Stream Turbulence

湍流是与天气有关的飞机事故的主要原因，也是许多人对航空旅行感到恐惧的根本原因。对湍流的一种估计表明，每年有超过63，000次遇到中度或更大的湍流，5000次遇到严重或更大的湍流。34年来，美国报告了883起与湍流有关的死亡事件。湍流也可以损坏飞机，通过撕裂风和发动机，就像1992年在科罗拉多发生的一次极端湍流事件一样。湍流的经济成本不仅仅是人员伤亡和损失，航班延误、检查、维修和事故后调查也会造成损失。据估计，仅美国航空公司每年的总成本就接近2亿美元。虽然英国/欧盟航空公司和欧盟领空的湍流成本无法获得，但假设湍流的发生和航空旅行的密度与美国相似，并且欧盟与美国的面积大致相同，那么成本应该是可比的。从气候预测中诊断出的中纬度湍流在大气二氧化碳增加的情况下会增加，在本世纪晚些时候会增加一倍或两倍。因此，气候变化造成的湍流成本将导致湍流事件大幅增加。晴空湍流，缩写为CAT，是指在晴空中远离云层的湍流。CAT对飞行员来说很难发现，对预报员来说也很难预测。难以预测的原因之一是，CAT被认为有多个来源，没有一个单一的预测工具适用于所有来源。CAT的一个可疑来源是流体动力学不稳定性的释放，这是大气中不同力量之间的不平衡，导致空气大而快的加速。这种加速可能会产生大气现象，如滚动型环流或波浪状运动，导致CAT。目前，我们对流体动力学不稳定性如何形成，释放，产生湍流，并恢复稳定的理解不完全。在这项拟议的研究中，我们将从三个来源观察湍流。一个是来自威尔士的垂直指向雷达，它可以探测到急流中的湍流。第二个是飞行员手动报告湍流。第三个是来自飞机上的自动化仪器。我们将利用这些观察来了解CAT形成的条件及其与流体动力不稳定性的关系。由于这些观测是单次测量的快照，因此计算机模型模拟产生CAT的真实的和理想化的天气现象对于确定不稳定性如何形成，不稳定性和由此产生的湍流如何演变以及大气如何在不稳定性释放后恢复平衡至关重要。在观察结果的背景下，我们将构建CAT从起源到生长再到死亡的生命周期。鉴于这些新的见解，我们将开发模型输出工具（称为诊断），以量化不稳定性释放的影响，并评估这些诊断在北美，北大西洋和欧洲的表现。通过这种方式，对CAT生命周期的更好理解将有助于更好地预测射流湍流，并降低成本和对乘客和机组人员的伤害。

Turbulence is the leading cause of weather-related aircraft incidents and the underlying cause of many people's fear of air travel. One estimate of turbulence indicates over 63,000 encounters with moderate-or-greater turbulence and 5000 encounters with severe-or-greater turbulence annually. In 34 years, the US reported 883 fatalities associated with turbulence. Turbulence can also damage aircraft, by tearing off winds and engines, as happened in an extreme turbulent event over Colorado in 1992. The economic costs of turbulence are more than just injuries and damage, with flight delays, inspections, repairs, and post-accident investigations also taking their toll. Estimates of the total cost to US carriers alone are nearly $200 million annually. Although the costs of turbulence to UK/EU airlines and over EU airspace are not available, assuming the occurrence of turbulence and the density of air travel are similar to that over the US and that the EU is about the same size as the US, then costs should be comparable.Moreover, climate change is exacerbating the problem. Midlatitude turbulence diagnosed from climate projections increase under increasing atmospheric carbon dioxide, with a doubling or trebling later this century. Thus, the costs of turbulence due to climate change will lead to a substantial increase in turbulent events. Clear-air turbulence, abbreviated as CAT, is turbulence that occurs away from clouds in clear air. CAT is difficult for pilots to detect and for forecasters to predict. One of the reasons that it is difficult to predict is that CAT is believed to have multiple sources and no single forecasting tool works for all of the sources. One suspected source of CAT is the release of hydrodynamic instability, an imbalance between different forces in the atmosphere that lead to large and rapid accelerations of the air. Such accelerations may produce atmospheric phenomena such as roll-type circulations or wave-like motions that result in CAT. Presently, we have an incomplete understanding of how hydrodynamic instability forms, releases, produces turbulence, and returns to stability. In this proposed research, we will look at observations of turbulence from three sources. One is from a vertically pointing radar in Wales that can detect turbulence at the jet stream. A second one is from pilots manually reporting turbulence. A third is from automated instrumentation aboard aircraft. We will use these observations to understand the conditions in which CAT forms and its relationship to hydrodynamic instability. Because these observations are snapshots in time from single measurements, computer model simulations of real and idealised weather phenomena that produce CAT will be critical to determine how the instability forms, how the instability and resulting turbulence evolves, and how the atmosphere returns to balance after the release of the instability. Within the context of the results from the observations, we will construct the life cycle of CAT from its origin, to its growth, to its demise. Given these new insights, we will develop tools for model output (called diagnostics) to quantify the impacts from the release of the instability and evaluate the performance of these diagnostics over North America, the North Atlantic Ocean, and Europe. In this way, improved understanding of the CAT life cycle will lead to better predictions of jet-stream turbulence, as well as reduced costs and injuries to passengers and flight crew.