Improving the Prediction of Tropical Precipitation using a new Convective Parameterization

气候模型是用来预测地球大气对当前二氧化碳增加的反应的主要工具。气候模型广泛应用于季节性天气预报，如预测干旱和寒冷冬季。尽管气候模型非常成功，但它们也有重要的弱点。这些弱点源于在试图表示湍流或涉及云的过程时遇到的困难。对流云在气候模式中尤其难以表现。它们是热带地区的主要云类型，在春夏季中纬度地区非常常见。它们的大小不等，从晴朗天气的小积云到雷暴，经常组织成更大的模式，如飑线和飓风。以目前的计算能力，不可能在气候模式中表示单个对流云的垂直环流。相反，气候模型采用被称为参数化的近似方法。为了进行成功的对流降水预报，对流参数化必须准确模拟模式格框内决定对流云平均增长率的因子。然而，对流云的生长速率对其所处环境中的许多因素极为敏感。对流云以抑制和增强邻近云的增长速率的方式改变背景大气。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表现最差。本研究的目的是改善对流云在气候和天气预报模式中的表现。如果成功，该研究有望改善对流降水的预测，更好地预测极端天气事件的影响，产生更可信的气候预测，改善季节性降水的预测，并通过改进气候模型与陆地表面模型的耦合方式，改善对土壤湿度等农业重要量的预测。

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.