MITRE: Mesoscale convective systems over India, Tracking, Research, and Experimentation

印度每年80%的降雨量来自夏季季风，夏季季风从6月到9月活跃。这种降雨的主要原因是对流，太阳能加热表面（和低层大气的表面变暖）导致上升气流将潮湿空气提升到较冷的高层大气中，在那里凝结并下雨。对流存在于一系列尺度上，从几十米宽的单个单元到数千公里宽的热带风暴和旋风。然而，在季风中，它最常见的是组织成几公里到几百公里规模的系统，称为中尺度对流系统（MCS）。MCS具有复杂的结构，通常由对流上升气流和层状外流区域组成，它们负责大部分的季风降雨总量以及全年发生的大多数极端降雨事件。尽管它们的重要性，他们还没有在印度进行详细的研究，由于在跟踪和收集有关它们的信息的困难，这个项目的目的是大大扩大我们的了解MCS的变化在印度和周边地区，探索他们的作用，维持和修改季风，量化如何以及他们的预测和发现潜在的预测误差来源，并预测他们对气候变化的反应。将与气象局、国家中期天气预报中心（德里）、印度热带气象研究所（浦那）和印度德里技术研究所的合作伙伴合作开展工作，第一项任务是跟踪卫星数据中的MCS，在那里它们显示为大面积的非常冷的云，以创建一个MCS轨迹的数十年数据库。使用这个数据库，我们将试图了解是什么原因导致MCS优先发生在特定位置（例如土壤水分，垂直风切变）和特定时间（例如季风变化），以及它们的结构和强度如何变化，由于环境（例如喜马拉雅山）和气象（例如当地大气稳定性）的刺激。然后，该项目将转向以季风为导向的观点，我们将试图了解MCS如何帮助季风在6月开始时向北推进通过印度，以及它们如何与每个季节多次通过季风印度的热带低气压相互作用。从用于天气预报到用于预测气候变化。利用我们合作机构的存档预报数据，我们将量化这些模型在短期（< 7天）、中期（7-15天）和季节性（15-60天）范围内捕获MCS的效果。我们将探讨预测误差的来源，如对流的时间，并测试统计技术是否可以用来改善未来的预测。最后，我们将量化MCS频率，强度和行为将如何响应预测的气候变化情景。我们将对已建立的气候模型（如CMIP 6）输出应用跟踪技术，但也将运行我们自己的高分辨率模拟，重点放在印度，能够更准确地模拟MCS。我们希望，该项目的方法和结果将是有价值的季风和对流系统在地球仪工作的其他研究人员，印度天气预报员，并为政策制定者探索长期减灾和水安全战略在南亚。

India receives 80% of its yearly rainfall during the summer monsoon, which is active from June through September. The primary cause of this rainfall is convection, where solar heating of the surface (and surface warming of the lower atmosphere) causes updrafts which lift moist air into the cooler upper atmosphere where it condenses and rains out. Convection exists on a range of scales, from individual cells tens of metres across, to tropical storms and cyclones thousands of kilometres across. In the monsoon, however, it is most commonly organised into systems from several to several hundred kilometres in scale, known as mesoscale convective systems (MCSs). MCSs have a complex structure, typically consisting of regions of both convective updrafts and stratiform outflow, they are responsible for a majority of the total monsoon rainfall as well as a majority of the extreme rainfall events occurring year-round. Despite their importance, they have not been studied in great detail over India, owing to the difficulty in tracking and collecting information about them.This project aims to vastly expand our understanding of the variability of MCSs over India and the surrounding region, to explore their role in sustaining and modifying the monsoon, to quantify how well they are forecast and uncover potential sources of forecast error, and to project their response to climate change. Work will be carried out in collaboration with partners at the Met Office, the National Centre for Medium-Range Weather Forecasting (Delhi), the Indian Institute of Tropical Meteorology (Pune) and the Indian Institute of Technology Delhi.The first task will be to track MCSs in satellite data, where they show up as large areas of very cold cloud, to create a multi-decade database of MCS tracks. Using this database, we will seek to understand what causes MCSs to occur preferentially in particular locations (e.g. soil moisture, vertical wind shear) and at particular times (e.g. monsoon variability), as well as how their structure and intensity change due to environmental (e.g. the Himalayas) and meteorological (e.g. local atmospheric stability) stimuli. The project will then move to a monsoon-orientated point of view, where we will seek to understand how MCSs assist the monsoon in advancing northward through India during its onset in June, and how they interact with the tropical depressions that pass through monsoonal India several times per season.The second half the project focus on investigating MCS behaviour in models, ranging from those used for weather forecasting to those used to project climate change. Using archived forecast data from our partner institutes, we will quantify how well MCSs are captured in these models, at short range (< 7 days), medium range (7-15 days) and seasonal range (15-60 days). We will explore sources of forecast error, such as the timing of convection, and test whether statistical techniques could be used to improve future forecasts. Finally, we will quantify how MCS frequency, intensity, and behaviour will respond to projected climate change scenarios. We will apply tracking techniques to established climate model (e.g. CMIP6) output, but will also run our own high-resolution simulations focused over India, capable of more accurate MCS simulation. We expect that the methodology and results of this project will be valuable to other researchers working on monsoons and convective systems across the globe; to forecasters of Indian weather; and for policymakers exploring long term disaster mitigation and water security strategies over South Asia.