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

印度80%的年降雨量是在夏季季风期间获得的，夏季季风从6月到9月活跃。这种降雨的主要原因是对流，在对流中，表面的太阳加热(和低层大气的表面升温)引起上升气流，将潮湿的空气提升到较冷的高层大气中，在那里它凝结并降雨出去。对流存在于一系列尺度上，从几十米宽的单个单元格到数千公里宽的热带风暴和气旋。然而，在季风中，它通常被组织成几到几百公里大小的系统，称为中尺度对流系统(MCSs)。MCSS具有复杂的结构，通常由对流上升气流和层状流出区组成，它们是季风总降雨量的主要来源，也是全年发生的大多数极端降雨事件的主要原因。尽管它们很重要，但由于难以跟踪和收集有关它们的信息，印度尚未对它们进行非常详细的研究。本项目旨在极大地扩大我们对印度及周边地区MCS的可变性的了解，探索它们在维持和修改季风方面的作用，量化它们的预报效果，找出预报误差的潜在来源，并预测它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(Pune)和印度理工学院的合作伙伴合作开展工作。第一项任务是在卫星数据中跟踪MCS，在那里它们显示为大片非常冷的云，以创建MCS路径的数十年数据库。利用这个数据库，我们将设法了解是什么原因导致MCS优先出现在特定地点(例如土壤湿度、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因环境(例如喜马拉雅山脉)和气象(例如当地大气稳定性)刺激而发生变化。然后，项目将转向以季风为导向的观点，在那里我们将试图了解MCS如何帮助季风在6月开始时通过印度向北推进，以及它们如何与每个季节几次经过印度季风的热带低压相互作用。项目的后半部分专注于研究MCS在模式中的行为，从用于天气预报的模式到用于预测气候变化的模式。使用我们合作研究所的存档预报数据，我们将量化这些模式在短期(&lt；7天)、中期(7-15天)和季节性(15-60天)范围内捕捉MCS的情况。我们将探索预报误差的来源，例如对流的时间，并测试是否可以使用统计技术来改进未来的预报。最后，我们将量化MCS的频率、强度和行为将如何响应预测的气候变化情景。我们将把跟踪技术应用于已建立的气候模式(如CMIP6)的输出，但也将运行我们自己的以印度为重点的高分辨率模拟，能够进行更准确的MCS模拟。我们预计，该项目的方法和结果将对全球从事季风和对流系统研究的其他研究人员、印度天气预报员以及探索南亚长期减灾和水安全战略的政策制定者有价值。

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.