A New Approach to Hydrologic Data Assimilation

水文资料同化的新方法

• 批准号: 0003361
• 负责人: Dennis McLaughlin
• 金额: --
• 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: 2001
• 资助国家: 美国
• 起止时间: 2001-03-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0003361&HistoricalAwards=false
• 关键词: New; Approach; Hydrologic; Data; Assimilation

0003361 McLaughlin Hydrology is experiencing rapid changes as a result of improved scientific understanding and measurement technology. Advances in global modeling and remote sensing are likely to provide large amounts of new information in the coming decades. There will be an increased need for efficient methods to process and interpret all of this information. Many of the most promising data processing options combine observations with model predictions, a process commonly known as data assimilation. The data assimilation methods which have been most successful in practical applications are based on either variational or recursive estimation concepts. Each of these approaches has distinctive advantages and limitations but neither provides a satisfactory solution for very large applications (e.g., applications that work with large amounts of remote sensing data over continental-scale regions).In this project, we propose to develop a computationally efficient and robust approach to hydrologic data assimilation which combines the best aspects of variational and recursive estimation. This work will be methodical in nature but its overall goal is to advance scientific understanding of large-scale hydrologic processes. The data assimilation methods we develop in this project will be tested on a case study, which will provide insight about scientific questions of hydrologic interest. The testing and application phase of our project will rely on our previous experiences with data assimilation techniques and on methods which have been successfully applied in meteorology and oceanography.The case study will be concerned with the estimation of near-surface soil moisture. Such estimates are especially useful for weather prediction and analyses of climate change. The case study will rely on an existing hydrologic/measurement model and will be based on data obtained from SGP97 and SGP99 field experiments in central Oklahoma. The tests carried out in this study will help to demonstrate the benefits of data assimilation in large-scale case studies of particular interest to hydrologists.

0003361由于科学认识和测量技术的提高，McLaughlin水文学正在经历快速变化。 全球建模和遥感方面的进展有可能在今后几十年提供大量新信息。 将越来越需要有效的方法来处理和解释所有这些信息。 许多最有前途的数据处理方法是将联合收割机观测与模式预测相结合，这一过程通常被称为数据同化。 在实际应用中最成功的数据同化方法是基于变分或递归估计概念的。 这些方法中的每一种都具有独特的优点和局限性，但都没有为非常大的应用提供令人满意的解决方案（例如，在这个项目中，我们建议开发一种计算效率高、鲁棒性强的水文数据同化方法，它结合了变分和递归估计的最佳方面。 这项工作将是有条不紊的性质，但其总体目标是促进大规模水文过程的科学认识。 我们在这个项目中开发的数据同化方法将在一个案例研究中进行测试，这将提供有关水文科学问题的见解。 我们项目的测试和应用阶段将依靠我们以前在数据同化技术方面的经验以及在气象学和海洋学中成功应用的方法，案例研究将涉及近地表土壤湿度的估计。 这种估计对天气预测和气候变化分析特别有用。 该案例研究将依赖于现有的水文/测量模型，并将基于从俄克拉荷马州中部的SGP 97和SGP 99现场实验中获得的数据。 在这项研究中进行的测试将有助于证明水文学家特别感兴趣的大规模案例研究中的数据同化的好处。