Characterizing Cumulus Cloud Cover with Transilient Matrices

低层大气中的浅积云的模式模拟表明，未来状态的预测对这些云在大气初始状态下的代表性的敏感性。 在天气和气候模式中处理这些云的数值方案已经存在多年，但新技术仍在继续建立和改进这些方案。 这项研究将使用一种称为瞬态矩阵的数学工具，以更好地描述这些云在模型中对低层大气的表示，从而改善基于这些云的未来天气和气候状态的预测。 这些改进反过来又将通过更好地理解天气和气候模式对低层大气中浅积云特征的敏感性而造福于社会。 拟议中的研究旨在测试与浅积云相关的云层覆盖量的假设。 其中一个假设是将云量与云的消散速度联系起来，其时间尺度取决于云的大小和涡流扩散率;这个尺度与气候模式中使用的典型尺度相差两个数量级。 时间尺度的表达将使用一个成熟的数学工具，称为瞬态矩阵（TM），从大涡模拟（LES）和云雷达观测输出进行测试和完善。 由于不同的全球气候模式（GCM）之间的浅积云覆盖的差异已被确定为气候预测的不确定性的主要贡献，这项研究有可能改善未来气候的预测。

Model simulations of shallow cumulus clouds in the lower atmosphere demonstrate the sensitivity of predictions of future states to the representation of these clouds in the initial state of the atmosphere. Numerical schemes to handle these clouds in weather and climate models have been in existence for many years, yet new techniques continue to build upon and improve these schemes. This research will use a mathematical tool, called Transilient Matrices, to better characterize these clouds in the model's representation of the lower atmosphere and in turn improve forecasts of future weather and climate states based on these clouds. These improvements will in turn benefit society through an improved understanding of the sensitivity of weather and climate models to the characterization of shallow cumulus clouds in the lower atmosphere. The proposed research seeks to test hypotheses for the amount of cloud cover associated with shallow cumulus. One such hypothesis relates the amount of cloud cover to the rate of cloud detrainment by a timescale that depends on cloud size and eddy diffusivity; this scale differs from the typical scale used in climate models by two orders of magnitude. The expression for timescale will be tested and refined using a well established mathematical tool known as the Transilient Matrix (TM) to output from large-eddy simulation (LES) and observations from cloud radar. Since differences in shallow cumulus cloud cover among various Global Climate Models (GCMs) have been identified as major contributions to uncertainty in climate projections, this research has the potential of improving forecasts of future climate.