Are tropical waves an untapped source of predictability in the tropics?

热带波浪是热带地区尚未开发的可预测性来源吗？

• 批准号: 2890062
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890062
• 关键词: tropical; waves; untapped; source; predictability

Weather in the tropics is dominated by the predictable diurnal cycle of convection. In many countries it rains heavily for a short period at approximately the same time every day. However, some days this diurnally driven rainfall is much heavier and more persistent than others and some days it does not rain at all. There is strong evidence in the scientific literature that these variations in rainfall are caused by the passage of atmospheric tropical waves. Furthermore, there is evidence from idealised global prediction experiments that, on large-scales, the atmosphere in the tropics is inherently more predictable than in the extratropics - hypothesised to be the result of tropical wave propagation (Judt, 2020). At present however, forecast models are poor at exploiting this potential predictability and skill falls rapidly in tropical forecasts. For example, convectively-coupled Kelvin waves travel too quickly and decay too quickly in atmospheric models meaning that the associated high impact weather is also misrepresented (Ferrett et al, 2020, Yang et al, 2021). This poses three major questions: i) How does the rapid growth of uncertainty in convection on small scales affect large-scale tropical waves? Are the fundamental limits of tropical weather predictability governed by tropical waves? Why are Kelvin waves, and their coupling with convective rainfall, not well represented in models, and how does this damage forecasts?This PhD project will begin by investigating the structure, dynamics and life-cycle of convectively-coupled Kelvin waves and how these differ in observations and models. The embedded convection will be examined in a frame of reference moving with the waves. We will use reanalysis, observation data, global ensemble forecast data and a unique set of large tropical domain high resolution ensemble forecasts produced by UK Met Office. The purpose of this PhD project is then to understand the interaction between the waves and embedded convection and then to use this to investigate how the small initial differences in forecasts grow with time and upscale from the convection to the tropical waves. How does this affect the longer-range predictions on large-scales that are essential to early warning of high impact weather with sufficient lead time for emergency action?In ensemble forecasting a set of forecasts is started at the same time, each with slightly different initial conditions and physical parameter settings. The purpose of ensembles is to predict the growth in uncertainty in the forecast of the atmospheric state at later times associated with chaos. In the Tropics, ensemble forecasts are considered under-dispersive, meaning that the forecast ensemble spread is on average much smaller than the forecast error. This is a major problem because it means that ensembles are less likely to give early warning of the possibility of high impact weather. The reasons for the poorer performance in the Tropics are a major unknown. However, the model representation of the atmospheric phenomena giving rise to chaos is likely to be central. Deep convection and cumulonimbus thunderstorms, associated with the high surface temperature and humidity in the Tropics, results in very rapid growth of spread on scales of 1-100 km. We look to larger scale phenomena for longer-range predictability. For example, equatorial waves that propagate eastwards or westwards along the equator, depending on the wave type (wavelengths >> 1000 km). However, even on these largest scales the current global ensembles do not spread fast enough with lead time, indicating some fundamental problem with forecasting systems. It is hypothesised that it is the interaction between phenomena dominant on different scales that is mis-represented and the project seeks to test this hypothesis. This has application to understanding the behaviour of ensemble forecasts and how to improve them.

热带地区的天气由可预测的昼夜对流周期主导。在许多国家，每天大约同一时间都会出现短时间的大雨。然而，有些日子这种日间驱动的降雨量比其他日子更大、更持久，而有些日子则根本不下雨。科学文献中有强有力的证据表明，降雨量的这些变化是由大气热带波的通过引起的。此外，来自理想化全球预测实验的证据表明，在大尺度上，热带地区的大气本质上比温带地区的大气更容易预测——假设这是热带波传播的结果（Judt，2020）。然而目前，预测模型在利用这种潜在的可预测性方面表现不佳，热带预测的技能迅速下降。例如，对流耦合开尔文波在大气模型中传播太快且衰减太快，这意味着相关的高影响天气也被歪曲了（Ferrett 等人，2020；Yang 等人，2021）。这就提出了三个主要问题：i）小尺度对流不确定性的快速增长如何影响大尺度热带波？热带天气预报的基本限制是否受到热带波浪的控制？为什么开尔文波及其与对流降雨的耦合在模型中没有得到很好的体现，这种损害如何预测？这个博士项目将首先研究对流耦合开尔文波的结构、动力学和生命周期以及它们之间的差异在观察和模型中。嵌入的对流将在随波浪移动的参考系中进行检查。我们将使用再分析、观测数据、全球集合预报数据以及英国气象局制作的一套独特的大型热带地区高分辨率集合预报。这个博士项目的目的是了解波浪和嵌入对流之间的相互作用，然后利用它来研究预测中的微小初始差异如何随着时间的推移而增长，并从对流到热带波浪的规模扩大。这对大范围的长期预测有何影响，这些预测对于高影响天气的早期预警以及采取紧急行动有足够的准备时间至关重要？在集合预报中，一组预报同时启动，每个预报的初始预报略有不同条件和物理参数设置。集合的目的是预测与混沌相关的后期大气状态预测中不确定性的增长。在热带地区，集合预报被认为是分散性不足的，这意味着预报集合分布平均远小于预报误差。这是一个主要问题，因为这意味着系统不太可能对高影响天气的可能性发出早期预警。热带地区表现不佳的原因尚不清楚。然而，引起混乱的大气现象的模型表示可能是核心。深对流和积雨云雷暴与热带地区的高表面温度和湿度有关，导致传播范围在 1-100 公里范围内非常迅速地增长。我们着眼于更大规模的现象以获得更长期的可预测性。例如，赤道波沿着赤道向东或向西传播，具体取决于波类型（波长>> 1000 km）。然而，即使在如此大规模的情况下，当前的全球集合在交付时间内的传播速度也不够快，这表明预测系统存在一些基本问题。据假设，不同尺度上占主导地位的现象之间的相互作用被错误地表述，该项目试图检验这一假设。这有助于理解集合预报的行为以及如何改进它们。