Facilitate Visualization and Distribution of NASA\u27s Environmental Science Data through Open Standards and Open Source Software for Geospatial

This paper introduces the utilization of open standards and open source software for visualization and distribution of geospatial environmental science data at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). The ORNL DAAC is one of the NASA Earth Observing System Data and Information System (EOSDIS) data centers. A big challenge for the ORNL DAAC (https://daac.ornl.gov) is to efficiently manage over a thousand heterogeneous environmental data, collected through field campaigns, aircraft/satellite observations, and model simulations. ORNL DAAC also has to provide tools to easily find, visualize, and access the heterogeneous data. To address this challenge, the ORNL DAAC has leveraged Open Geospatial Consortium (OGC) standards and open source software to develop the Spatial Data Access Tool (SDAT, https://webmap.ornl.gov/ogc). SDAT is a suite of open standards-based web mapping, subsetting, and transformation services and applications that allow users to visualize and download geospatial data in customized spatial/temporal extents, formats, and projections. The open source MapServer/Geospatial Data Abstraction Library (GDAL) powers the backend OGC Web services of SDAT. Open source Javascript libraries, including OpenLayers, GeoExt, and proj4js, were used to create the SDAT Web User Interface and MapWidget, a light-weight Javascript library that allows SDAT visualization to be easily embedded on any webpage. SDAT also provides KML files to enable interactive data visualization in the popular Google Earth application or any KML-compatible client. SDAT provides a common framework and standard service interfaces for ORNL DAAC data holdings. SDAT user interface hides their heterogeneity from end users, and promotes their usage. SDAT facilitates integration of ORNL DAAC data resources with other related data systems. In 2016, SDAT served more than 2 million mapping requests and 72 thousand customized data downloads from over 2500 distinct data users

NASA ESDIS Standards Office

The ESDIS Standards Office (ESO) assists NASAs Earth Science Data and Information System (ESDIS) project in formulating standards policy for NASA Earth Science Data Systems, coordinates standards activities within ESDIS, and provides technical expertise and assistance with standards related tasks within the NASA Earth Science Data System Working Groups (ESDSWG)

Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control

This paper presents a robust maximum power point tracking (MPPT) control scheme for a grid-connected permanent magnet synchronous generator based wind turbine (PMSG-WT) using perturbation observation based nonlinear adaptive control. In the proposed control scheme, system nonlinearities, parameter uncertainties, and external disturbances of the PMSG-WT are represented as a lumped perturbation term, which is estimated by a high-gain perturbation observer. The estimate of the lumped perturbation is employed to compensate the actual perturbation and further achieve adaptive feedback linearizing control of the original nonlinear system, without requiring the detailed system model and full state measurements. The effectiveness of the proposed control scheme is verified through both simulation studies and experimental tests. The results show that, compared with the conventional vector controller and the standard feedback linearizing controller, the proposed control strategy provides higher power conversion efficiency and has better dynamic performances and robustness against parameter uncertainties and external disturbances

Nonlinear adaptive speed control of a permanent magnet synchronous motor: A perturbation estimation approach

This paper presents a nonlinear adaptive control (NAC) scheme for the speed regulation of a permanent magnet synchronous motor (PMSM) based on perturbation estimation and feedback linearizing control. All PMSM system’s unknown nonlinearities, parameter uncertainties, and external disturbances including unknown time-varying load torque disturbance, are defined as lumped perturbation terms, which are estimated by designing perturbation observers. The estimates are used to adaptively compensate the real perturbations and achieve adaptive feedback linearizing control of the original nonlinear system. The proposed control scheme does not require accurate system model and full state feedback. Stability of the close-loop system with proposed NAC is investigated via Lyapunov theory, and the effectiveness of proposed NAC scheme is verified through both simulation and experimental studies. Both simulation and experimental results show that the proposed NAC scheme can provide less regulation error in speed tracking, better dynamic performance and robustness against parameter uncertainties and load torque disturbance, compared with conventional vector control and load torque estimated based control

Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Huntzinger, D. N., Schaefer, K., Schwalm, C., Fisher, J. B., Hayes, D., Stofferahn, E., Carey, J., Michalak, A. M., Wei, Y., Jain, A. K., Kolus, H., Mao, J., Poulter, B., Shi, X., Tang, J., & Tian, H. Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems. Environmental Research Letters, 15(2), (2020): 025005, doi:10.1088/1748-9326/ab6784.Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.This work was supported by NASA'S Arctic Boreal Vulnerability Experiment (ABoVE; https://above.nasa.gov); NNN13D504T. Funding for the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP; https://nacp.ornl.gov/MsTMIP.shtml) activity was provided through NASA ROSES Grant #NNX10AG01A. Data management support for preparing, documenting, and distributing model driver and output data was performed by the Modeling and Synthesis Thematic Data Center at Oak Ridge National Laboratory (MAST-DC; https://nacp.ornl.gov), with funding through NASA ROSES Grant #NNH10AN681. Finalized MsTMIP data products are archived at the ORNL DAAC (https://daac.ornl.gov). We also acknowledge the modeling groups that provided results to MsTMIP. The synthesis of site-level soil respiration, temperature, and moisture data reported in Carey et al 2016a, 2016b) was funded by the US Geological Survey (USGS) John Wesley Powell Center for Analysis and Synthesis Award G13AC00193. Additional support for that work was also provided by the USGS Land Carbon Program. JBF carried out the research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged

A critical survey of technologies of large offshore wind farm integration : summary, advances, and perspectives

Offshore wind farms (OWFs) have received widespread attention for their abundant unexploited wind energy potential and convenient locations conditions. They are rapidly developing towards having large capacity and being located further away from shore. It is thus necessary to explore effective power transmission technologies to connect large OWFs to onshore grids. At present, three types of power transmission technologies have been proposed for large OWF integration. They are: high voltage alternating current (HVAC) transmission, high voltage direct current (HVDC) transmission, and low-frequency alternating current (LFAC) or fractional frequency alternating current transmission. This work undertakes a comprehensive review of grid connection technologies for large OWF integration. Compared with previous reviews, a more exhaustive summary is provided to elaborate HVAC, LFAC, and five HVDC topologies, consisting of line-commutated converter HVDC, voltage source converter HVDC, hybrid-HVDC, diode rectifier-based HVDC, and all DC transmission systems. The fault ride-through technologies of the grid connection schemes are also presented in detail to provide research references and guidelines for researchers. In addition, a comprehensive evaluation of the seven grid connection technologies for large OWFs is proposed based on eight specific indicators. Finally, eight conclusions and six perspectives are outlined for future research in integrating large OWFs

The PBase Scientific Workflow Provenance Repository

Scientific workflows and their supporting systems are becoming increasingly popular for compute-intensive and data-intensive scientific experiments. The advantages scientific workflows offer include rapid and easy workflow design, software and data reuse, scalable execution, sharing and collaboration, and other advantages that altogether facilitate “reproducible science”. In this context, provenance – information about the origin, context, derivation, ownership, or history of some artifact – plays a key role, since scientists are interested in examining and auditing the results of scientific experiments. However, in order to perform such analyses on scientific results as part of extended research collaborations, an adequate environment and tools are required. Concretely, the need arises for a repository that will facilitate the sharing of scientific workflows and their associated execution traces in an interoperable manner, also enabling querying and visualization. Furthermore, such functionality should be supported while taking performance and scalability into account. With this purpose in mind, we introduce PBase: a scientific workflow provenance repository implementing the ProvONE proposed standard, which extends the emerging W3C PROV standard for provenance data with workflow specific concepts. PBase is built on the Neo4j graph database, thus offering capabilities such as declarative and efficient querying. Our experiences demonstrate the power gained by supporting various types of queries for provenance data. In addition, PBase is equipped with a user friendly interface tailored for the visualization of scientific workflow provenance data, making the specification of queries and the interpretation of their results easier and more effective

Call to action for global access to and harmonization of quality information of individual earth science datasets

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision-and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges