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

AMMA 进一步分析：非洲大陆表面的对流生命周期

基本信息

批准号：
NE/G018499/1
负责人：
Douglas Parker
金额：
$ 47.79万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FG018499%2F1
关键词：
AMMA Further Analysis Convective life

项目摘要

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.

西非人口依靠降雨生存，因为大多数地区的降雨是农业生产的最重要限制。降雨对人类消费和水力发电用水的供应也很重要，而季节性降雨影响疟疾等某些疾病。目前，我们预测该地区降雨的能力非常差。该项目旨在提高我们对西非带来降雨的风暴的认识，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项目，我们将把新的科学认识纳入日常和长期预测。在季风季节的萨赫勒地区，大部分降雨是由被称为积雨云的大雷雨云带来的，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大而有组织的风暴，范围达数百公里，产生强降雨，导致土壤非常潮湿。通常，暴风雨过后，一块块潮湿的土壤与极度干燥的土壤相邻。反过来，土壤湿度模式取决于植被的混合分布，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。过去，量化地表与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，我们将分析风暴与陆地表面相互作用的情况，从而改进天气预报模型。西非季风是一股气流，它将大西洋的潮湿空气带入非洲大陆：这种湿度为带来降雨的风暴提供了养分。然而，湿气也会被较小的云（称为积云）从低层潮湿的季风层输送出去。水汽被密集云向上混合的速率影响着整个西非地区的水分收支，所以如果我们想要得到正确的预报，我们需要评估它们的影响。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对于这类研究来说是极好的，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。姊妹项目（Cascade）的进展意味着现在我们有了一个UM版本，可以解析整个大陆的云，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——大雷暴是由大气中的波浪模式组成的，它们在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Cascade版本中解决这些过程，我们的目标是了解在UM的天气预报版本中正确使用这些过程需要什么。该模型还被用作气候预测模型：目前，西非的气候预测在对未来降雨量的预测上存在分歧，这阻碍了有效的缓解战略，而这些战略可能有助于地方和国际政府的规划。从这个项目中获得的知识不仅有助于风暴事件的日常预测，而且有助于预测未来的降雨趋势。