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

9418904 Levy业务天气和气候预测模型有三个基本要求：1.数据管理设施，允许同化和操纵从各种来源获得的大量不同种类和不精确的数据。2.一种物理和统计建模技术，可将部分和不同种类的数据结合起来，考虑到问题的实际非线性物理和经常遇到的复杂错误结构。3.在数据同化周期内有效支持这些功能。对这些要求的有效回应需要跨学科的方法。建议开展一项合作，将计算、统计和地球科学方面的互补专门知识汇集在一起，进行交叉培训和联合研究。目标是启动交叉培训，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。通过构建软件工具将复杂的理论解决方案应用于实践的可行性将得到测试。通过实施一项密集的跨学科计划，其中将包括一系列座谈会、交叉教学、讲习班和定期会议，合作者将扩大他们对互补领域的理解，以便能够共同提出和开发特定示范系统的一般方法和原型。在演示系统中，将使用物理边界层模式来整合和管理海量卫星遥感数据。这些数据的处理不可避免地涉及复杂的网格化、内插、数据质量控制、平均以及任意辅助数据集的输入和误差协方差结构的估计。将研究提供这些函数的技术并将它们应用于散射计(ERS-1)应力数据的具体情况，并探索从当前的学科方法中综合这些函数的可行性。由此产生的数据将通过行星边界层模式(PBLM)进行处理，以便将它们与大气数值预报和大气环流模式联系起来。因此，将在试点项目结束时提出的系统将充分利用BL物理、异质错误结构统计和高质量的数据管理，以获得更好的模型。

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.