Improving the Prediction of Tropical Precipitation using a new Convective Parameterization

使用新的对流参数化改进热带降水的预测

• 批准号: 155649-2012
• 负责人: Folkins, Ian
• 金额: $ 1.97万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=522658
• 关键词: Improving; Prediction; Tropical; Precipitation; using

Climate models are the main tools used to predict how the earth's atmosphere will respond to the current increases in carbon dioxide. Climate models are widely used in seasonal weather forecasting, such as in the prediction of droughts and cold winters. Although climate models have been very successful, they do have important weaknesses. These weaknesses originate from difficulties in trying to represent processes that are turbulent or involve clouds. Convective clouds are especially difficult to represent in climate models. They are the main cloud type in the tropics and are very common in mid-latitudes during spring and summer. They range in size from small fair weather cumulus to thunderstorms, and are often organized into larger patterns such as squall lines and hurricanes. With current computing power, it is impossible to represent the vertical circulations of individual convective clouds within a climate model. Instead, climate models resort to approximate methods called parameterizations. To make a successful convective rainfall forecast, a convective parameterization must accurately simulate the factors which determine the average growth rate of the convective clouds inside a model grid box. However, the growth rates of convective clouds are extremely sensitive to a large number factors in their environment. Convective clouds modify the background atmosphere in ways which suppress and enhance the growth rates of neighboring clouds. Because of these difficulties, climate and weather forecast models perform most poorly in regions where convective clouds are most frequent. The purpose of this research is to improve the representation of convective clouds in climate and weather forecast models. If successful, the research can be expected to improve the forecasting of convective precipitation, better predict the impacts of extreme weather events, generate more credible climate projections, improve the forecasting of seasonal precipitation, and by improving the way climate models couple to land surface models, improve the prediction of quantities important to agriculture such as soil moisture.

气候模型是用于预测地球大气的主要工具，如何应对二氧化碳的当前增加。气候模型被广泛用于季节性天气预报，例如在干旱和寒冷冬季的预测中。尽管气候模型非常成功，但它们确实具有重要的弱点。这些弱点源于试图代表湍流或涉及云的过程的困难。在气候模型中，对流云特别困难。它们是热带地区的主要云类型，在春季和夏季中期非常普遍。它们的大小从天气晴朗的云母到雷暴，通常会组织成较大的模式，例如挤压线条和飓风。使用当前的计算能力，无法在气候模型中代表单个对流云的垂直循环。相反，气候模型诉诸于称为参数化的近似方法。为了进行成功的对流降雨预测，对流的参数化必须准确模拟确定模型网格盒中对流云的平均生长速率的因素。但是，对流云的增长率对其环境中的大量因素极为敏感。对流云通过抑制和提高相邻云的生长速度的方式改变了背景气氛。由于这些困难，气候和天气预报模型在对流云最频繁的地区的表现最差。这项研究的目的是改善气候和天气预报模型中对流云的表示。如果成功的话，可以预期这项研究可以改善对流降水的预测，更好地预测极端天气事件的影响，产生更可靠的气候预测，改善对季节降水的预测，并通过改善气候模型的方式来降落表面模型，改善对农业重要的数量预测，例如土壤水分。