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

模型弯曲：使用单一模型结构处理数据同化和预测中的模型不足

• 批准号: 0216866
• 负责人: James Hansen
• 金额: $ 66.9万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0216866&HistoricalAwards=false
• 关键词: Model; Bending; Towards; Dealing; Inadequacies

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.

造成数值模型天气预报不准确的误差主要有两个来源：初始条件误差和模型不足。在本项目中，pi将通过数据同化和预测期间物理参数的调整来解决模型不足的问题。他们将使用从预测中产生的模型输出统计（MOS）作为系统未来状态的四维变分数据同化（4d-Var）的伪数据，以找到最小化MOS预测与模型预测之间不匹配的初始条件。第二个独立的MOS应用，然后应用于预测的4d-Var状态。这个过程被称为“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将模型“弯曲”到大气的真实状态。将开发自校正模型，使选定的参数成为基于物理约束的时间和空间缓慢变化的函数。在此期间，获得这些参数的MOS，然后用于“预测”正向运行的优化参数。这个过程被称为“模型弯曲”。将使用不同复杂程度的模型来更好地理解模型的不足之处。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。该项目为培养数据同化和可预测性等急需领域的研究生提供了良好的机会。