SI2-SSI: REAL-TIME LARGE-SCALE PARALLEL INTELLIGENT CO2 DATA ASSIMILATION SYSTEM

智力优势：该提案旨在通过创建一个最先进的自主软件平台来满足这一需求，该平台用于在数据同化系统内实时整合现场和卫星大气CO2测量，以每周至每两周生成全球陆地和海洋CO2交换估计数。拟议的软件基础设施将能够通过多级管道自主处理大量数据，而不会出现传统上与此类处理相关的延迟。在DA组件中，我们将提供用于估计全球CO2交换的多种DA算法的选项。用户将首次有能力使用这些多种方法作为比较CO2交换估计数的单一系统的一部分，并更好地了解各种DA方法的相对优势。作为该软件分析组件的一部分，我们将通过借鉴模式识别和高维统计推断中的一系列技术来构建碳气候监测系统。该系统将能够检测和分析任何用户指定的时空窗口内的CO2交换的局部变化。更广泛的影响：研究人员和政府机构可使用这一软件评价碳循环的自然组成部分和人为碳排放量，并设计新的卫星以改进对二氧化碳的监测。所有数据和软件都将公开提供，并尽可能使用开源开发平台。 作为该项目的一部分开发的算法原型将用于密歇根大学的本科生和研究生课程，并将在线提供给其他机构的教育工作者。最后，该项目将培养三名研究生，重点是培养他们在地球科学、统计学、计算机科学和大气科学领域的跨学科技能。

Intellectual Merit: This proposal seeks to address this need by creating a state-of-the-art autonomous software platform for real-time integration of in-situ and satellite-based atmospheric CO2 measurements within a Data Assimilation (DA) system for producing estimates of global land and oceanic CO2 exchange at weekly to bi-weekly intervals. The proposed software infrastructure will be capable of autonomous processing of large volumes of data through a multi-stage pipeline, without the delays conventionally associated with such processing. Within the DA component, we will provide options for multiple DA algorithms for estimating global CO2 exchange. Users will, for the first time, have the capability to use these multiple methods as part of a single system for comparing estimates of CO2 exchange, and to obtain an improved understanding of the relative advantages of the various DA methods. As part of the analysis component of the software, we will build a carbon-climate surveillance system by drawing from a range of techniques in pattern recognition and high-dimensional statistical inference. This system will be able to detect and analyze localized variations in CO2 exchange within any user-specified spatio-temporal window. In addition, summaries of the CO2 exchange will be provided at annual and monthly temporal scales for continents and countries.Broader Impacts: This software can be used by researchers and governmental institutions for evaluating both the natural components of the carbon cycle and anthropogenic carbon emissions, as well as in the design of new satellites for improved monitoring of CO2. All data and software will be publicly available and open-source development platforms will be used whenever possible. The algorithm prototypes developed as part of this project will be used in undergraduate and graduate courses at the University of Michigan, and will be made available online for educators at other institutions. Finally, the project will train three graduate students, with a focus on developing their cross-disciplinary skills in the field of Earth science, statistics, computer science, and atmospheric science.