Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS)

季风为南亚的农业和工业提供了大部分用水，因此对10亿人的福祉至关重要。季风的活跃期和间歇期对农业的成功有着重大影响，而降雨量的逐年变化则在国际范围内产生经济后果。人口增长和经济发展意味着了解和预测季风因此至关重要。尽管如此，我们模拟季风的能力，以及对未来几天到季节的尺度进行预测的能力，受到迅速发展的大误差的限制。印度天气预报模型的性能相对较差，这是由于陆地、海洋和大气之间的关系非常强烈和复杂，它们通过对流过程联系在一起，其形式是带来降雨的积雨云。预报误差的产生主要是因为对流云与大尺度环流或地面条件没有准确联系，而且这些误差会持续很长时间。在世界范围内，天气和气候预测模型的分辨率正在提高，但季风降雨的误差并没有减少。由于缺乏对季风系统的陆地、海洋和大气部分在一系列时间和空间尺度上的详细观测，因此无法更透彻地了解季风对流云和陆地表面的过程及其与大规模环流的相互作用。该项目将使用一个在印度和邻近海洋上空进行新测量的方案，以提高印度-联合王国社区的季风预报能力。FAAM研究飞机的第一个分遣队前往印度，与密集的地面观测活动相结合，将收集对陆地表面、陆地和海洋上的边界层结构以及大气剖面的新观测。我们将对陆地表面的能量和水交换进行一系列新的长期测量。一个季风季节的研究测量结果将与印度业务网络的长期观测结果相结合。观测将侧重于两个样带：印度北方平原，覆盖从灌溉农业到雨水灌溉农业的各种地表类型，以及从湿润到干燥的气候带;西高止山脉，从陆地到海洋的过渡地带，以及整个地形。观测分析将代表季风过程的独特和前所未有的特征，季风过程将控制降雨的陆地、海洋和大气模式联系起来。长期测量将允许计算各种因素之间的统计关系。观测分析将直接用于改进气象局和NCMRWF的预报。两个机构用于天气预报的气象局统一模型将在观测期间以一系列不同的方式建立。特别是，我们将率先测试开发一个新的100米分辨率的大气模型，我们预计将大大提高陆地-海洋-大气相互作用的代表性。另一个优先事项是改进季风预报中的陆地表面模型。通过将小区域的甚高分辨率模式的结果与代表全球天气模式的较低分辨率模式的结果进行比较，将有可能描述控制季风降雨的关键过程，并指出在天气预测或气候预测等不同应用中需要如何表示这些过程。通过在一系列尺度上进行模型评价，对降雨过程形成简单的理论认识，并与负责改进业务模型的小组合作，该项目将直接导致季风预报的改进。通过改进降雨预测，我们预计这项工作将在印度和国际上产生经济影响。

The monsoon supplies the majority of water for agriculture and industry in South Asia, and is therefore critical to the well-being of a billion people. Active and break periods in the monsoon have a major influence on the success of farming, while year-to-year variations in the rainfall have economic consequences on an international scale. The growing population and developing economy mean that understanding and predicting the monsoon is therefore vital. Despite this, our capability to model the monsoon, and to make forecasts on scales from days to the season ahead is limited by large errors that develop quickly. The relatively poor performance of weather prediction models over India is due to a very strong and complex relationship between the land, ocean and atmosphere, which are linked by the process of convection, in the form of the rain-bringing cumulonimbus clouds. Forecast errors occur primarily because the convective clouds are not accurately linked to the large-scale circulation or to the surface conditions, and these errors persist to long time scales. Worldwide, weather and climate forecast models are gaining resolution, and yet the errors in monsoon rainfall are not diminishing. A lack of detailed observations of the land, ocean and atmospheric parts of the monsoon system, on a range of temporal and spatial scales, is preventing a more thorough understanding of processes in monsoon convective clouds and at the land surface, and their interaction with the large-scale circulation. This project will use a programme of new measurements over India and the adjacent oceans to advance monsoon forecasting capability in the Indo-UK community. The first detachment of the FAAM research aircraft to India, in combination with an intensive ground-based observation campaign, will gather new observations of the land surface, the boundary layer structure over land and ocean, and atmospheric profiles. We will institute a new long-term series of measurements of energy and water exchanges at the land surface. Research measurements from one monsoon season will be combined with long-term observations on the Indian operational networks. Observations will be focused on two transects: in the northern plains of India, covering a range of surface types from irrigated to rain-fed agriculture, and wet to dry climatic zones; and across the Western Ghats, with transitions from land to ocean and across orography. The observational analysis will represent a unique and unprecedented characterization of monsoon processes linking the land, ocean and atmospheric patterns which control the rainfall. Long-term measurements will allow the computation of statistical relationships between the various factors. The observational analysis will feed directly into improved forecasting at the Met Office and NCMRWF. The Met Office Unified Model, which is used for weather forecasting at both institutions, will be set up in a range of different ways for the observational period. In particular, we will pioneer the test development of a new 100m-resolution atmospheric model, which we expect to greatly improve the representation of land-ocean-atmosphere interactions. Another priority will be to improve land surface modelling in monsoon forecasts. By comparing the results of the very high resolution models on small domains with lower-resolution models representing the global weather patterns, it will be possible to describe the key processes controlling monsoon rainfall, and to indicate how these need to be represented in different applications, such as weather predictions or climate predictions. Through model evaluation at a range of scales, the development of simple theoretical understanding of the rainfall processes, and working with groups responsible for operational model improvement, the project will lead directly to improvements in monsoon forecasts. By improving rainfall prediction, we expect the work to have an economic impact in India and internationally.