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

西非人口依靠降雨生存，因为大部分地区的降雨是限制农业生产的最重要因素。降雨对人类消费和水力发电用水的供应也很重要，而季节性降雨会影响疟疾等某些疾病。目前，我们对这一地区降雨的预测能力非常差。这个项目旨在提高我们对西非暴雨的了解，并提高我们预测它们的能力。通过与英国和西非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更长期的预测。在萨赫勒地区，在季风季，大部分降雨是由被称为积雨云的大型雷雨云输送的，它可以向上延伸20公里进入大气层。这些积雨云聚集在一起形成了大的、有组织的风暴，范围达数百公里，产生了暴雨，导致了一片片非常潮湿的土壤。通常，暴风雨过后，湿润的土壤会堆积在土壤旁边，而土壤仍然非常干燥。反过来，土壤水分模式位于植被的混合分布之上，从森林到农业，从稀树草原到沙漠。我们知道，陆地表面的这种模式可能会在接下来的几天与风暴相互作用，了解这些过程对于准确的天气预报是很重要的。过去，量化地表和云之间的这种相互作用的一个主要问题是缺乏这一偏远地区的有用测量数据。在NERC资助的AMMA(非洲季风多学科分析)研究成功之后，我们现在拥有了这一大陆热带地区前所未有的观测数据。利用这个数据集，我们将分析风暴与地表相互作用的情况，从而改进天气预报模型。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，水汽也被称为积云的小云从低层潮湿的季风层输送出去。水汽被拥挤的云层向上混合的速度影响着整个西非地区的水收支，所以我们需要评估它们的影响，如果我们要预测正确的话。我们将利用在AMMA期间测量的化学示踪剂，来了解和评估现实中和模型中的拥堵混合率。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对于这类研究来说是非常好的，因为它可以用来描述大气中的一系列现象，从一公里左右的小云到整个全球大气。在一个姊妹项目(Cascade)中取得的进展意味着，现在我们有了一个版本的UM，它可以解析整个大陆的云，并评估这些云之间的相互作用。在引发新风暴方面，这种相互作用与陆地表面状态一样重要--一种大雷雨--大气中的一组波浪模式，在非洲大陆上移动数千公里，通常会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的天气预报版本中需要什么来正确地解决这些问题。UM还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，这阻碍了有效的缓解战略，这些战略可能有助于当地和国际政府的规划。从这个项目中获得的知识不仅将有助于对风暴事件的日常预测，而且将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.