Development of a Data Assimilation Capability Towards Ecological Forecasting in a Data-Rich Era

俄克拉荷马大学获得了一笔赠款，用于开发数据同化生态平台（EcoPAD），用于生态学中的数据同化和预测。EcoPAD将包括以下组件：(1)专门为解决生态问题而设计的核心计算算法（例如生态模型），(2)各种数据同化优化技术，(3)将输入EcoPAD的各种数据库，以及(4)EcoPAD的各种功能。这些功能使用户能够(i)估计模型参数或状态变量，（ii）量化估计参数和生态系统预测状态的不确定性（例如，美国的碳汇），（iii）评估模型结构，（iv）评估采样策略，以及(v)进行生态预测。EcoPAD将在三个空间尺度上进行开发、测试和应用。首先，EcoPAD将根据杜克森林和俄克拉荷马州大草原的实验数据进行开发，以检验其提取生态系统对气候变化响应信息的能力。其次，该项目将在北美的AmeriFlux涡流通量测量网络上开发EcoPAD，以检查其预测净生态系统交换的日、季节和年际动态的能力。第三，EcoPAD预测生态系统未来状态及其服务的能力将在区域和大陆尺度上进行测试和评估。EcoPAD有潜力成为一个强大的生态信息学工具，吸收来自测量传感器网络的数据，并产生数据产品，这些数据产品将有助于制定关于资源管理和减缓气候变化的政策。该项目将通过学生和博士后培训、生态建模综合课程以及对K-12学生和教师的推广，将教学、培训和学习结合起来。NEON和其他传感器网络旨在为社会提供更好的生态服务。数据同化和生态预报是这些网络成功的关键因素。本项目将发展EcoPAD，从海量数据中提取生态预测信息，直接服务社会，为政策制定提供参考。该项目的研究成果将有助于开展生态系统服务教育。欲了解更多信息，请访问PI实验室网站http://bomi.ou.edu/luo/。

The University of Oklahoma is awarded a grant to develop an Ecological Platform for Assimilation of Data (EcoPAD) for data assimilation and forecasting in ecology. EcoPAD will include components of (1) core computational algorithms (e.g., ecological models) that are specifically designed to solve ecological issues, (2) a variety of optimization techniques for data assimilation, (3) various data bases that will feed into EcoPAD, and (4) diverse functions of EcoPAD. The functions enable users to (i) estimate model parameters or state variables, (ii) quantify uncertainty of estimated parameters and projected states of ecosystems (e.g., carbon sinks in USA), (iii) evaluate model structures, (iv) assess sampling strategies, and (v) conduct ecological forecasting. EcoPAD will be developed, tested, and applied at three spatial scales. First, EcoPAD will be developed against plot-level data from experiments in the Duke Forest and Oklahoma prairies to examine its capability of extract information on ecosystem responses to climate change. Second, the project will develop EcoPAD at the AmeriFlux network of eddy-flux measurements in the North America to examine its capability of forecasting diurnal, seasonal, and interannual dynamics of net ecosystem exchange. Third, EcoPAD's capability of forecasting future states of ecosystems and their services will be tested and evaluated at regional and continental scales. EcoPAD has the potential to become a powerful eco-informatics tool that assimilate data from measurement sensor networks and to generate data products that will be useful for policy making on resource management and climate change mitigation. This project will have integrated teaching, training, and learning through student and post-doc training, integrated courses in ecological modeling, and outreach to K-12 students and teachers. NEON and other sensor networks have been designed to provide better ecological service to the society. Data assimilation and ecological forecasting are key components for success of those networks. This project will develop EcoPAD to extract information from massive data for ecological forecasts, which can directly serve the society and be useful for policy making. Results from this project will be useful for education of public on ecosystem services. For further information see the PI's lab website at http://bomi.ou.edu/luo/.