AMMA Further Analysis: Convective life-cycles over African continental surfaces

西非人口依靠降雨生存，因为大多数地区的降雨量是农业生产的最重要限制因素。降雨对人类消费和水力发电用水的供应也很重要，而季节性降雨则影响到疟疾等某些疾病。目前，我们对这一地区降雨量的预测能力极差。该项目旨在提高我们对西非暴雨的了解，并提高我们预测暴雨的能力。通过与来自英国和西非的天气预报员的知识交流计划，我们将把新的科学认识纳入日常和长期预测。在萨赫勒地区的季风季节，大部分降雨是由称为积雨云的大型雷暴云带来的，积雨云可向上延伸20公里进入大气层。这些积雨云聚集在一起形成大的，有组织的风暴，范围达数百公里，产生强烈的降雨，导致土壤非常潮湿。通常，在一场暴风雨之后，湿土的斑块与仍然非常干燥的土壤相邻。反过来，土壤水分模式位于混合分布的植被之上，从森林到农业，稀树草原和沙漠。我们知道，陆地表面的这种模式可以与接下来几天的风暴相互作用，了解这些过程对于准确的天气预报非常重要。在过去，量化陆地表面和云之间的相互作用的一个主要问题是缺乏来自这个偏远地区的有用测量。在NERC资助的AMMA（非洲季风多学科分析）研究取得成功之后，我们现在拥有了大陆热带地区前所未有的观测数据。使用这个数据集，我们将分析风暴与陆地表面相互作用的情况，从而改进天气预测模型。西非季风是一种气流，它将潮湿的空气从大西洋带入非洲大陆：这种湿度为带来降雨的风暴提供了条件。然而，水分也被较小的云（称为积云）从低层潮湿的季风层中输送出来。凝结云向上混合水分的速度影响着整个西非地区的水收支，因此如果我们要做出正确的预测，我们需要评估它们的影响。我们将利用AMMA期间测量的化学示踪剂，以了解和评估现实和模型中的充血混合率。我们将使用英国气象局的天气和气候预测模型-“统一模型”（UM）。UM非常适合这类研究，因为它可以用来描述大气中的各种现象，从一公里左右的小云，到整个全球大气。在姐妹项目（Cascade）中取得的进展意味着现在我们有了一个UM版本，可以解决整个大陆的云，并评估这些云之间的相互作用。这种相互作用在引发新风暴方面与陆地表面状态一样重要-一场大雷暴在大气中形成了一系列波浪模式，这些波浪在整个大陆上移动数千公里，通常可能在偏远地区引发新的风暴。通过在UM的级联版本中解决这些过程，我们的目标是了解在UM的天气预测版本中需要什么来使它们正确。UM也被用作气候预测模型：目前，西非的气候预测对未来降雨量的预测不一致，这阻碍了有助于地方和国际政府规划的有效缓解战略。从该项目中获得的知识不仅有助于对风暴事件的日常预测，而且有助于对未来降雨趋势的预测。

The population of West Africa rely on rainfall for their survival, since rainfall in most areas is the most important limit on agricultural production. Rainfall is also important in the availability of water for human consumption and for hydroelectric power generation, while the seasonal rainfall influences certain diseases such as malaria. Currently, our ability to predict rainfall in this region is extremely poor. This project aims to improve our understanding of the rain-bringing storms of West Africa, and improve our ability to forecast them. Through a knowledge-exchange programme with weather forecasters from the UK and West Africa, we will feed the new scientific understanding into daily and longer-term predictions. In the Sahel region during the monsoon season, most of the rain is delivered by large thunderstorm clouds, known as cumulonimbus, which can extend 20 kilometres upwards into the atmosphere. These cumulonimbus clouds band together into large, organised storms, several hundred km in extent, that produce intense rain, leading to patches of very wet soil. Often, after a storm, patches of wet soil lie adjacent to soil which remains extremely dry. In turn, the soil moisture patterns lie on top of a mixed distribution of vegetation, from forest to agriculture, savanna and desert. We know that such patterns in the land surface can interact with storms on the following days, and it is important to understand these processes for accurate weather forecasting. In the past, a major problem in quantifying this interaction between the land surface and cloud has been a lack of useful measurements from this remote region. Following the successful NERC-funded AMMA (African Monsoon Multidisciplinary Analysis) study, we now have unprecedented observational data for this region of the continental tropics. Using this dataset, we will analyse cases in which storms interact with the land surface, and therefore improve weather prediction models. The West African Monsoon is an airflow which brings humid air from the Atlantic Ocean into the continent: this humidity feeds the rain-bringing storms. However, moisture is also transported out of the low level, humid monsoon layer by smaller clouds, known as cumulus congestus. The rate at which moisture is mixed upwards by congestus clouds influences the water budget of the entire West African region, so we need to assess their effects if we are to get the forecast right. We will make use of chemical tracers measured during AMMA, to understand and evaluate the rates of congestus mixing, in reality and in models. We will use the UK Met Office weather and climate prediction model - the 'Unified Model' (UM). The UM is excellent for this kind of study because it can be used to describe a wide range of phenomena in the atmosphere, from small clouds a kilometre or so across, or the whole global atmosphere. Advances made in a sister project (Cascade) mean that now we have a version of the UM which can resolve clouds right across the continent, and assess the interactions between these clouds. Such interactions are just as important as the land surface state in setting off new storms -a big thunderstorm sets of patterns of waves in the atmosphere which move thousands of km across the continent and may commonly set off a new storm at a remote location. By resolving these processes in the Cascade version of the UM, we aim to understand what is needed to get them right in a weather prediction version of the UM. The UM is also used as a climate prediction model: currently, climate predictions for West Africa disagree on projections of future rainfall, which hinders effective mitigation strategies that could help in planning for local and international governments. The knowledge gained from this project will help not only in the day-to-day prediction of storm events but also in prediction of future rainfall trends.