Collaborative Research: WCR: Incorporation of Model Bias and Uncertainty in Land Surface Hydrologic Flux Prediction Using a Data Assimilation Network

合作研究：WCR：使用数据同化网络将模型偏差和不确定性纳入陆地表面水文通量预测

• 批准号: 0333154
• 负责人: Dara Entekhabi
• 金额: $ 10.31万
• 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: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2006-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0333154&HistoricalAwards=false
• 关键词: Collaborative; Research; WCR; Incorporation; Model; Collaborative; Research; WCR; Incorporation; Model

0333154EntekhabiA major weakness of current data assimilation algorithms is that they typically do not account for model errors or assume that the model errors are unbiased. However biases are inevitably introduced in the models due to poorly specified forcing and parameters. Understanding the variance and memory of these errors is key to properly including their representation in data assimilation frameworks and will ultimately lead to improving our ability to predict variations in hydrologic fluxes.In this project we will focus on incorporating biases and other errors that can occur as a result of erroneous forcing and parameterizations in a data assimilation framework. The first tasks in our proposed research project will consist of numerical simulations to characterize the types of errors that are specific to land surface hydrologic modeling. The goal is to develop parsimonious probabilistic error models (that account for biases and other model errors) that are necessary inputs to data assimilation algorithms. Variational data assimilation experiments will then be performed to design an observing system that can ultimately be incorporated into a real-time data assimilation framework and is capable of including these parameterizations of model biases and errors. The final set of research tasks will be to incorporate the error models and observing system into a real-time (operational) data assimilation framework using the Ensemble Kalman Filter.The expected results of this study include the characterization of those errors that are caused by the inevitable misspecification of surface forcing and model parameters and incorporate them into land data assimilation systems.

0333154Entekhabia当前数据同化算法的主要弱点是它们通常不考虑模型误差或假设模型错误是公正的。 然而，由于指定强迫和参数，模型中不可避免地引入偏见。 了解这些错误的差异和记忆是正确的关键，包括它们在数据同化框架中的表示，并最终将提高我们预测水文通量变化的能力。在该项目中，我们将专注于在数据同化框架框架中，由于错误强迫和参数而导致的偏见和其他错误。我们提出的研究项目中的第一个任务将包括数值模拟，以表征特定于土地表面水文建模的错误类型。 目的是开发对数据同化算法的必要输入，开发出简约的概率错误模型（解释了偏见和其他模型错误）。 然后，将执行各种数据同化实验，以设计一个观察系统，该系统最终可以纳入实时数据同化框架中，并能够包括这些模型偏差和错误的参数化。最终的研究任务集将是将错误模型和观察系统合并到使用集合Kalman滤波器的实时（操作）数据同化框架中。这项研究的预期结果包括表征这些错误是由不可避免的不可避免的表面强迫和模型参数不可避免地纳入土地数据同化系统中的错误。