Model Bending: Towards Dealing with Model Inadequacies in Data Assimilation and Forecasting Using a Single Model Structure

造成数值模式天气预报不准确的主要误差来源有两个：初始条件误差和模型不足。在这个项目中，PI将通过数据同化和预报期间物理参数的调整来解决模式的不足之处。他们将使用预报产生的模式输出统计(MOS)作为系统未来状态的伪数据，在四维变分数据同化(4D-Var)中寻找使MOS预报与模式预报之间的不匹配最小化的初始条件。然后，将第二个独立的MOS应用于所得到的预测4d-Var状态。这一过程被称为“预测4d-Var”，因为它在预测过程中通过4d-Var考虑了模型误差。研究的第二部分是动态改变模型参数，而不是初始条件，使用4d-Var来使模型向大气的真实状态“弯曲”。将开发自校正模型，以允许选定的参数是基于物理约束的时间和空间的缓慢变化函数。在此期间，获得这些参数的最大似然比，然后用于在正向运行中“预测”优化的参数。这一过程被称为“模型弯曲”。将使用不同复杂程度的模型来更好地了解模型的整体不足之处。这项研究将使数值预报中的模型误差受到更多的关注。它具有改善业务预测和指导未来模型开发的潜力。如果成功，这将导致数值模拟方面的重大突破。该项目为在急需的数据同化和可预测性领域培训研究生提供了良好的机会。

There are two major sources of errors that account for inaccurate weather predictions from numerical models: the initial-condition errors and model inadequacies. In this project, the PIs will tackle the model inadequacies problem through data assimilation and adjustment of physical parameters during forecast period. They will use the model output statistics (MOS) produced from forecasts as pseudo-data of the system's future states in a four dimensional variational data assimilation (4d-Var) to find the initial conditions that minimize the mismatch between the MOS forecasts and the model forecasts. A second, independent, application of MOS is then applied to the resulting forecast 4d-Var states. This procedure is termed as a "forecast 4d-Var" as it takes into account the model errors through 4d-Var during forecast. The second part of the research is to dynamically alter model parameters, instead of the initial conditions, using 4d-Var to "bend" the model toward the true state of the atmosphere. Self-correcting models will be developed to allow selected parameters to be slowly varying functions of time and space based on physical constraints. During this period, MOS for these parameters are obtained and then used to "predict" optimized parameters in forward runs. This procedure is termed as "model bending". Models of varying levels of complexity will be used to gain a better understanding of model inadequacies overall. The research will bring more attention into model errors in numerical predictions. It has the potential of improving operational forecasts and directing future model development. If successful, it would lead to a major breakthrough in numerical modeling. The project provides good opportunities for training graduate students in the highly needed areas of data assimilation and predictability.