4-D Variational Data Assimilation and Parameter Estimation with the Full Physics NMC Spectral Model

这项研究的总体目标是确定在数值天气预报模式中处理数据同化的物理上最具代表性和计算效率最高的方法。这一奖项将允许首席研究员继续研究应用于数值预报模式的变分方法。他将与国家环境预测中心(前国家气象中心或NMC)的科学家合作，系统地探索包括各种物理过程对预报改进和计算成本的贡献。第二个研究主题涉及使用参数估计，使用伴随方法作为改进预报的另一种手段。在这种情况下，PI将探索模型对季节、地理和气象参数的敏感性。首席调查员的工作已经并可能继续对改进业务天气预报作出贡献，同时促进数值技术的基本知识。***

The overall goal of this research is to determine the physically most representative and computationally efficient ways of handling data assimilation in numerical weather forecast models. This award will allow the principal investigator to continue his research into the use of variational methods applied to numerical forecast models. In collaboration with scientists at the National Center for Environmental Prediction (formerly the National Meteorological Center or NMC), he will systematically explore the contribution to the forecast improvement and computational costs of including various physical processes. A second research topic involves the use of parameter estimation using adjoint methods as another means of improving forecasts. In this case, the PI will explore model sensitivity to seasonal and geographical as well as meteorological parameters. The principal investigator's work has contributed and will likely continue to contribute to improvements in operational weather forecasting while advancing fundamental knowledge of numerical techniques. ***