Mathematical Sciences: Integrating Heterogeneous Geo- physcal Data by Combining Error Structures: An Inter- disciplinary Pilot Project

数学科学：通过组合误差结构整合异构地球物理数据：跨学科试点项目

• 批准号: 9418904
• 负责人: Gad Levy
• 金额: $ 8.83万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-03-15 至 1998-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9418904&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Integrating; Heterogeneous; Geo

9418904 Levy Operational weather and climate predictions models of the near future have three fundamental requirements: 1. Data management facilities that allow the assimilation and manipulation of massive amounts of heterogeneous and imprecise data obtained from a variety of sources. 2. A modeling technology, both physical and statistical, that allows the integration of partial and heterogeneous data, accounting for the realistic non-linear physics of the problem and the often complex error structure encountered. 3. Efficient support of these functions within the data assimilation cycle. Responding effectively to these requirements calls for an interdisciplinary approach. A collaboration which will bring together complementary expertise in the computational, statistical, and the geosciences for cross training and joint research is proposed. The goal is to initiate cross training which will enable the collaborators to address, as a team, crucial problems in the atmospheric data assimilation cycle. The feasibility of applying sophisticated theoretical solutions in practice by building software tools will be tested. By implementing an intensive interdisciplinary plan that will include a colloquium series, cross-teaching, workshops, and regular conferences, the collaborators will broaden their understanding of the complementary fields to be able to jointly propose and develop a general methodology and a prototype for a specific demonstration system. In the demonstration system, a physical Boundary Layer (BL) model will be used to integrate and manage massive satellite sensed data. The processing of such data inevitably involves complex gridding, interpolation, data quality control; and averaging, as well as the infusion of arbitrary ancillary data sets and estimation of error covariance structure. Techniques to provide these functions and apply them to the specific case of scatterometer (ERS-1) stress data will be studied, a nd the feasibility of synthesizing them from current disciplinary methods will be explored. The resulting data will be processed through a Planetary Boundary Layer Model (PBLM) to link them to atmospheric numerical prediction and general circulation models. Consequently, the system that will be proposed at the end of the pilot project will make full use of BL physics, heterogeneous error structure statistics, and high quality data management to obtain better models.

9418904征费的运营天气和气候预测模型具有三个基本要求：1。数据管理设施，允许对大量的异构和不精确的数据进行同化和操纵，从各种源来获得。 2。一种建模技术，无论是物理和统计，允许集成部分和异质数据，考虑了问题的现实非线性物理以及遇到的经常复杂的误差结构。 3。在数据同化周期内对这些功能的有效支持。 对这些要求有效响应，要求采用跨学科方法。 提出了一项合作，将汇集到跨培训和联合研究的计算，统计和地球科学方面的互补专业知识。 目的是启动交叉训练，这将使合作者能够作为一个团队解决大气数据同化周期中的关键问题。 将测试通过构建软件工具在实践中应用复杂的理论解决方案的可行性。 通过实施一个密集的跨学科计划，该计划将包括座谈会系列，跨教学，讲习班和常规会议，合作者将扩大对互补领域的理解，以便能够共同提出并开发一般的方法论和针对特定示范系统的原型。 在演示系统中，将使用物理边界层（BL）模型来整合和管理大量卫星感应的数据。 此类数据的处理不可避免地涉及复杂的格栅，插值，数据质量控制；和平均，以及对任意辅助数据集的输注和误差协方差结构的估计。 将研究提供这些功能并将其应用于散射仪（ERS-1）应力数据的特定情况的技术，并将探索从当前学科方法中合成它们的可行性。 所得数据将通过行星边界层模型（PBLM）处理，以将其链接到大气数值预测和一般循环模型。 因此，在试点项目末尾将提出的系统将充分利用BL物理学，异质误差结构统计以及高质量的数据管理，以获得更好的模型。