Relationships Between Cloud and Thermodynamic Variables

气候变化中一系列极其重要的不确定性 理论是那些与云/辐射/热力学/ 动态互动 其中最麻烦的 不确定性是与大自然有关的， 尺度热力学过程导致观察到的复杂 云的结构在任何给定的位置和时间。 目前， 气候模型通常将云的部分和液态水 相对湿度或对流降水 rates. 所用公式是在理论和实验结果的基础上， 一些短期的详细的实地实验。 全球分析， 需要长期观测来验证这些参数化 并测试它们对地区、季节或气候的适用性 在田间试验中未直接取样的 本项目将以最佳方式进行此类分析， 定期观测数据。 这些数据将包括 七种云覆盖率的卫星导出估计 从国际卫星云气候学推导出的“类型” 项目，热力学和动力学变量从操作 欧洲中期天气中心的分析 降水预报与地面标准观测 测量. 除了使用先前导出的云 类型，PI将导出并使用新的云规范 在标准压力水平下的分数。这些参数的变化 对于地球仪的季节和年际时间尺度 将被用来得出新的和评估目前的相互关系 用于气候和长期云参数化 预测模型

An extremely important set of uncertainties in climate change theories are those associated with cloud/radiative/thermodynamic/ dynamic interactions. Among the most troublesome of these uncertainties are those associated with the nature of the large- scale thermodynamics processes leading to the observed complex structure of clouds at any given location and time. Currently, climate models generally relate cloud fractions and liquid water contents to local relative humidity or convection precipitation rates. The formulas used are based on the results of theory and a few short-term detailed field experiments. Analyses of global, long-term observations are needed to verify these parameterizations and to test their applicability to regions, seasons or climatic eras, which were not directly sampled in the field experiments This project will carry out such analyses with the best available regularly observed data. These data will consist of satellite derived estimates of fractional cover of seven cloud 'types' derived from the International Satellite Cloud Climatology Project, thermodynamics and dynamics variables from the operational analyses of the European Centre for Medium-Range Weather Forecasting and standard observations of precipitation form surface measurements. In addition to using the previously derived cloud types, the PI will derive and utilize a new specification of cloud fraction at standard pressure level. Variations of these parameters for much of the globe over seasonal and interannual time scales will be used to derive new and assess current interrelationships used in the cloud parameterizations of climate and long-range forecast models.