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

有两个主要来源的误差，占不准确的天气预报数值模式：初始条件的错误和模式的不足之处。 在这项计划中，专业研究员会在预报期间透过数据同化及调整物理参数，以解决模式不足的问题。 他们将使用从预测中产生的模型输出统计（MOS）作为四维变分数据同化（4d-Var）中系统未来状态的伪数据，以找到最小化MOS预测和模型预测之间不匹配的初始条件。 然后，将MOS的第二独立应用应用于所得到的预测4d-Var状态。 这个过程被称为“预测4d-Var”，因为它考虑了预测过程中通过4d-Var的模型误差。 研究的第二部分是动态地改变模式参数，而不是初始条件，使用4d-Var“弯曲”模式向真实的大气状态。 将开发自校正模型，使选定的参数能够根据物理限制缓慢变化的时间和空间函数。 在此期间，获得这些参数的MOS，然后用于“预测”正向运行中的优化参数。 这个过程被称为“模型弯曲”。 不同复杂程度的模型将被用来更好地了解模型的不足之处。该研究将引起人们对数值预测中模型误差的更多关注。 它具有改进业务预测和指导未来模型开发的潜力。 如果成功，它将导致数值模拟的重大突破。 该项目为在数据同化和可预测性等急需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.