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

热带地区的天气主要由可预测的对流日循环所控制。在许多国家，每天大约在同一时间下一小段时间的大雨。然而，有些日子，这种昼夜驱动的降雨量比其他日子更重，更持久，有些日子根本不下雨。科学文献中有强有力的证据表明，这些降雨量的变化是由大气热带波的通过引起的。此外，从理想化的全球预测实验中有证据表明，在大尺度上，热带地区的大气本质上比热带外地区更可预测-假设是热带波传播的结果（Judt，2020）。然而，目前，预报模型在利用这种潜在的可预测性和技能迅速下降福尔斯在热带预报差。例如，对流耦合开尔文波在大气模型中传播得太快，衰减得太快，这意味着相关的高影响天气也被歪曲了（Ferrett等人，2020年，Yang等人，2021年）。这就提出了三个主要问题：i）小尺度对流中不确定性的快速增长如何影响大尺度热带波？热带天气可预测性的基本限制是否受热带波的支配？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，这种损害是如何预测的？这个博士项目将开始通过调查对流耦合开尔文波的结构，动力学和生命周期，以及如何在观测和模型这些不同。嵌入对流将在随波运动的参考系中进行研究。我们将使用再分析，观测数据，全球集合预报数据和英国气象局制作的一套独特的大型热带域高分辨率集合预报。这个博士项目的目的是了解波和嵌入式对流之间的相互作用，然后用它来研究预测中的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。这对大尺度的长期预测有何影响？长期预测对于高影响天气的早期预警至关重要，并有足够的时间采取紧急行动？在集合预报中，一组预报同时开始，每个预报具有略微不同的初始条件和物理参数设置。合奏的目的是预测增长的不确定性，在未来的时间与混沌相关联的大气状态的预测。在热带地区，集合预报被认为是欠分散的，这意味着预报集合传播平均比预报误差小得多。这是一个主要问题，因为这意味着合奏不太可能对高影响天气的可能性发出早期预警。在热带地区表现较差的原因是一个主要的未知数。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地面温度和湿度有关，导致1 - 100 km尺度的传播非常迅速。我们着眼于更大尺度的现象，以获得更长期的可预测性。例如，沿赤道向东或向西传播的赤道波，取决于波的类型（波长>> 1000 km）。然而，即使在这些最大的规模，目前的全球合奏没有传播足够快的提前时间，表明一些基本的问题与预测系统。据推测，这是在不同尺度上占主导地位的现象之间的相互作用被错误地描述，该项目试图测试这一假设。这对理解集合预报的行为以及如何改进它们具有应用价值。

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.