ITR/AP: Collaborative Research: Diversifying Ensembles with Stochastic Convection

综合预报是利用日益强大的计算机系统改进数值天气预报的有效途径。经验表明，集合预报的偏差不够大，因此大气的真实状态往往超出了集合的范围。在这个项目中，pi试图通过对对流过程的扰动来探索模式气候的可变性，从而扩大传播。该方法是在积云参数化方案中引入随机噪声来解释亚网格尺度效应。波动的设计将基于来自云分辨模型、雷达、飞机和无线电探空仪的高分辨率数据。这些想法将在具有不同类型复杂性的模型中实现。这些结果有可能为改善主要气象中心的集合预报系统提供新的方法，也有助于提高对现有积云参数化的理解。这个项目由两位博士合作完成。科罗拉多大学CIRES的布莱恩·梅普斯和托马斯·哈米尔以及亚利桑那大学的史蒂文·马伦博士。

An effective way of using increasing powerful computer systems in improving numerical weather prediction is through ensemble forecasting. Experience shows that the ensemble forecasts do not diverge enough so that often the true state of the atmosphere lies outside the spread of the ensemble. In this project, the PIs seek to broaden the spread by exploring the variability of model's climate through the perturbation of the convective processes. The approach is to introduce stochastic noise in cumulus parameterization schemes to account for the sub-grid scale effects. The design of the fluctuations will be based on high-resolution data from cloud-resolving models, radars, aircraft, and radiosondes. These ideas will be implemented in models with different types of complexity. The results have the potential of providing new methods for improving ensemble forecast systems at major meteorological centers and also leading to improved understanding of existing cumulus parameterization. This project is being done collaboratively between Drs. Brian Mapes and Thomas Hamill, CIRES, University of Colorado and Dr. Steven Mullen, University of Arizona.