@article{peng_gross_edmunds_2022, title={Crosswalks among stewardship maturity assessment approaches promoting trustworthy FAIR data and repositories}, volume={9}, ISSN={["2052-4463"]}, url={https://doi.org/10.1038/s41597-022-01683-x}, DOI={10.1038/s41597-022-01683-x}, abstractNote={Abstract}, number={1}, journal={SCIENTIFIC DATA}, author={Peng, Ge and Gross, Wendy S. and Edmunds, Rorie}, year={2022}, month={Sep} }
 @article{peng_lacagnina_downs_ganske_ramapriyan_ivánová_wyborn_jones_bastin_shie_et al._2022, title={Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets}, url={https://doi.org/10.5334/dsj-2022-008}, DOI={10.5334/dsj-2022-008}, abstractNote={Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re) use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity. 2 Peng et al. Data Science Journal DOI: 10.5334/dsj-2022-008 1. BACKGROUND Informed decisions on whether and how to (re)use particular digital datasets rely on knowledge about aspects of data and metadata quality, including their completeness, accuracy, provenance and timeliness (Digital Science et al. 2019; Peng et al. 2021a). Quality assessments also improve the reliability and usability of both data and metadata (Callahan et al. 2017) and are crucial for supporting open-source science and data-driven policy-making processes (Peng et al. 2020a; 2021a). A dataset in this article refers to a collection of data that is identifiable (ISO 19115-1 2014), and has the potential to be curated or published by a single actor (W3C 2020). A particular dataset can digitally represent a group of observations, a data product from a specific version of a processing algorithm based on observations, output of numerical model(s), or outcomes of laboratory experiments. Dataset quality information embodies information about the quality or state of data (input, output, and ancillary), metadata, documentation, software, procedures, processes, workflows, and infrastructure that were created or utilized during the entire lifecycle of a dataset (Peng et al. 2021a). Therefore, the focus of this article is on dataset quality – not just data quality. To be effectively shared and utilized, quality information needs to be consistently curated, preferably traceable, and appropriately documented (Peng et al. 2021a). The granularity of this quality documentation may vary – sometimes be very fine (e.g., per-observation in the case of volunteered observations) but the critical common resolution required to support FAIR data publishing is the individual dataset level.1 Quality assessment results also need to be represented consistently, updated regularly, and should be integrable across systems, services, and tools to enable improved data sharing (Henzen et al. 2021; Wagner et al. 2021; Peng et al. 2021a). While the needs for assessments about the quality of data and related information for a particular dataset are well recognized, an approach for a framework to evaluate and present such quality information to data users (e.g., Figgemeier et al. 2021) may not have been sufficiently developed and/or sufficiently addressed for disciplinary or interdisciplinary use. In response, an international workshop was held virtually on 13 July 2020 to pursue the needs and challenges for preparing and documenting dataset quality information consistently during the complete dataset lifecycle by a group of global Earth science, interdisciplinary domain experts. A number of challenges were identified in Peng et al. (2020b), and three are highlighted below. First, the selection of relevant quality attribute(s) (e.g., accuracy, completeness, relevancy, timeliness, etc.) is largely dependent upon context and can yield multiple quality categories and practical dimensions (Lee et al. 2002; Ramapriyan et al. 2017; Redman 1996; Wang & Strong 1996). This multi-dimensionality makes the assessment of dataset quality a complex endeavor. For example, the quality attribute of completeness can refer to the completeness of data values in both spatial and temporal spaces, or the completeness of metadata elements or content. The multi-dimensionality of dataset quality has been discussed in detail by Peng et al. (2021a). An example of grouping dataset quality into four aspects (i.e., science, product, stewardship, and service) through the entire dataset lifecycle is shown in Figure 1. For each aspect, three important stages are listed along with selected quality attributes which do not constitute an exhaustive list. Those dataset lifecycle stages do not necessarily cover all activities. They may not necessarily happen sequentially, and also may occur in more than one quality aspect. For example, the ‘Evaluate’ part of the lifecycle in the ‘Product’ quadrant may overlap with the ‘Science’ by influencing the ‘Validate’ part. However, generally speaking, activities in the dataset lifecycle identified in the ‘Science’ quadrant occur before those in the ‘Product’ quadrant as noted by the direction of the arrows in Figure 1. Note that the term ‘Develop’ used in the ‘Science’ quadrant also includes data observation/acquisition. The feedback and improvement cycle can occur in any one of the stages. 1 https://www.gbif.org/data-quality-requirements. 3 Peng et al. Data Science Journal DOI: 10.5334/dsj-2022-008 Second, quality attributes are often not defined, measured, or captured consistently, even within one discipline. Moroni and colleagues recently observed such complexity as it pertains to the uncertainty of Earth science data (Moroni et al. 2019). Consistency in defining quality attributes and converging to standardized assessment models may be optimal for sharing, but more progress needs to be made, and whether such consistency is achievable remains to be seen. A step towards cross-domain interoperability, however, may be achieved by thorough documentation of domain-specific quality assessment techniques and metrics and the full provenance of the quality assessment. This allows transformations to be applied to dataset quality scores when this is possible and appropriate, e.g., computation of an exceedance value or quantile from a mean and standard deviation (Bastin et al. 2013, Section 5.1). The third challenge is associated with the paradigm shift in the capabilities of the designated community of scientific data: from domain literate with familiarity of the scientific context and intended use of data products, to potential users representing diverse fields of inquiry (Baker et al. 2016), with increasing demand for machine interoperability. Therefore, the existence of a wide range of stakeholders and data users, including those with very little or no science background, should be considered to facilitate the analysis, interpretation, understanding of research data and related information and in some cases acted upon (Peng et al. 2021a). Any effort to maximize the sharing of quality information requires collaboration among members of the entire community across science, data management, and technology domains. Recognizing that, 32 workshop participants – all international domain experts – issued an open ‘call-to-action for global access to and harmonization of quality information of individual Earth science datasets’ (Peng et al. 2021a). In response to that action call and further motivated by the needs of and interest from the global Earth science community, the International FAIR Dataset Quality Information (FAIR-DQI) Community Guidelines Working Group was formed. Working group members comprise international domain experts, such as data producers and contributors, data managers and curators from scientific institutes and data centers, and data consumers and publishers. Given their common interest in dataset quality information, this group of people can be regarded as a ‘Community of Practice (CoP)’ (E. Wenger-Trayner & B. Wenger-Trayner 2015). Together, the members of this group possess valuable first-hand knowledge and expertise in dealing with the challenges of developing, managing, disseminating, Science}, journal={Data Science Journal}, author={Peng, Ge and Lacagnina, Carlo and Downs, Robert R. and Ganske, Anette and Ramapriyan, Hampapuram K. and Ivánová, Ivana and Wyborn, Lesley and Jones, Dave and Bastin, Lucy and Shie, Chung-lin and et al.}, year={2022}, month={Mar} }
 @article{peng_downs_lacagnina_ramapriyan_ivánová_moroni_wei_larnicol_wyborn_goldberg_et al._2021, title={Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets}, url={https://doi.org/10.5334/dsj-2021-019}, DOI={10.5334/dsj-2021-019}, abstractNote={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.}, journal={Data Science Journal}, author={Peng, Ge and Downs, Robert R. and Lacagnina, Carlo and Ramapriyan, Hampapuram and Ivánová, Ivana and Moroni, David and Wei, Yaxing and Larnicol, Gilles and Wyborn, Lesley and Goldberg, Mitch and et al.}, year={2021}, month={May} }
 @article{peng_lacagnina_ivánová_downs_ramapriyan_ganske_jones_bastin_wyborn_bastrakova_et al._2021, title={International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets}, volume={4}, url={https://doi.org/10.31219/osf.io/xsu4p}, DOI={10.31219/osf.io/xsu4p}, abstractNote={Under the auspices of the Earth Science Information Partners (ESIP) and with collaboration among members of the ESIP Information Quality Cluster (IQC), the Barcelona Supercomputing Center (BSC) Evaluation and Quality Control (EQC) team, and the Australia/New Zealand Data Quality Interest Group (AU/NZ DQIG), a community effort has been undertaken by international Earth Science domain experts. The objective of this effort is to develop global community guidelines with practical recommendations to promote the representation, sharing and reuse of quality information at the dataset level, leveraging the experiences and expertise of a team of interdisciplinary domain experts and community best practices. The community guidelines are inspired by the guiding principles of findability, accessibility, interoperability, and reusability (FAIR) and aim to help stakeholders such as science data centers, repositories, data producers and publishers, data managers and stewards, etc., i) to capture, describe, and represent quality information of their datasets in a way that is in line with the FAIR guiding principles; ii) to allow for the maximum discovery, trust, sharing, reuse and value of their datasets; and iii) to enable global access to and integration of dataset quality information. The vision of developing these guidelines is to promote the creation and use of freely and openly shared dataset quality information that is consistently described, readily available in community standardized formats, and capable of being integrated across commonly-used Earth science systems and tools for search and access with explicitly expressed usage licenses.}, publisher={Center for Open Science}, author={Peng, Ge and Lacagnina, Carlo and Ivánová, Ivana and Downs, Robert R. and Ramapriyan, Hampapuram and Ganske, Anette and Jones, Dave and Bastin, Lucy and Wyborn, Lesley and Bastrakova, Irina and et al.}, year={2021}, month={Apr} }
 @article{lacagnina_peng_ivanova_downs_ramapriyan_moroni_wei_wyborn_jones_ganske_2021, title={International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets}, url={https://doi.org/10.1002/essoar.10508481.1}, DOI={10.1002/essoar.10508481.1}, abstractNote={Earth and Space Science Open Archive This is a preprint and has not been peer reviewed. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science DatasetsAuthorsCarloLacagninaiDGePengiDIvanaIvanovaiDRobertDownsiDHampapuramRamapriyaniDDavidMoroniiDYaxingWeiiDLesleyWyborniDDaveJonesAnetteGanskeiDSee all authors Carlo LacagninaiDCorresponding Author• Submitting AuthorBarcelona Supercomputing CenteriDhttps://orcid.org/0000-0001-9434-9809view email addressThe email was not providedcopy email addressGe PengiDNC State University and NOAA’s National Centers for Environmental Information (NCEI)iDhttps://orcid.org/0000-0002-1986-9115view email addressThe email was not providedcopy email addressIvana IvanovaiDCurtin UniversityiDhttps://orcid.org/0000-0001-6836-3463view email addressThe email was not providedcopy email addressRobert DownsiDColumbia University of New YorkiDhttps://orcid.org/0000-0002-8595-5134view email addressThe email was not providedcopy email addressHampapuram RamapriyaniDNASA Goddard Space Flight CentiDhttps://orcid.org/0000-0002-8425-8943view email addressThe email was not providedcopy email addressDavid MoroniiDNASA Jet Propulsion LaboratoryiDhttps://orcid.org/0000-0003-2994-557Xview email addressThe email was not providedcopy email addressYaxing WeiiDOak Ridge National LaboratoryiDhttps://orcid.org/0000-0001-6924-0078view email addressThe email was not providedcopy email addressLesley WyborniDAustralian National UniversityiDhttps://orcid.org/0000-0001-5976-4943view email addressThe email was not providedcopy email addressDave JonesStormCenter Communicationsview email addressThe email was not providedcopy email addressAnette GanskeiDTechnische Informationsbibliothek (TIB)iDhttps://orcid.org/0000-0003-1043-4964view email addressThe email was not providedcopy email address}, author={Lacagnina, Carlo and Peng, Ge and Ivanova, Ivana and Downs, Robert and Ramapriyan, Hampapuram and Moroni, David and Wei, Yaxing and Wyborn, Lesley and Jones, Dave and Ganske, Anette}, year={2021}, month={Oct} }
 @article{lacagnina_peng_ivanova_downs_ramapriyan_moroni_wei_wyborn_jones_ganske_2021, title={International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets}, url={https://doi.org/10.1002/essoar.10508601.1}, DOI={10.1002/essoar.10508601.1}, abstractNote={Earth and Space Science Open Archive This is a preprint and has not been peer reviewed. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science DatasetsAuthorsCarloLacagninaiDGePengiDIvanaIvanovaiDRobertDownsiDHampapuramRamapriyaniDDavidMoroniiDYaxingWeiiDLesleyWyborniDDaveJonesAnetteGanskeiDSee all authors Carlo LacagninaiDCorresponding Author• Submitting AuthorBarcelona Supercomputing CenteriDhttps://orcid.org/0000-0001-9434-9809view email addressThe email was not providedcopy email addressGe PengiDNC State University and NOAA’s National Centers for Environmental Information (NCEI)iDhttps://orcid.org/0000-0002-1986-9115view email addressThe email was not providedcopy email addressIvana IvanovaiDCurtin UniversityiDhttps://orcid.org/0000-0001-6836-3463view email addressThe email was not providedcopy email addressRobert DownsiDColumbia University of New YorkiDhttps://orcid.org/0000-0002-8595-5134view email addressThe email was not providedcopy email addressHampapuram RamapriyaniDNASA Goddard Space Flight CentiDhttps://orcid.org/0000-0002-8425-8943view email addressThe email was not providedcopy email addressDavid MoroniiDNASA Jet Propulsion LaboratoryiDhttps://orcid.org/0000-0003-2994-557Xview email addressThe email was not providedcopy email addressYaxing WeiiDOak Ridge National LaboratoryiDhttps://orcid.org/0000-0001-6924-0078view email addressThe email was not providedcopy email addressLesley WyborniDAustralian National UniversityiDhttps://orcid.org/0000-0001-5976-4943view email addressThe email was not providedcopy email addressDave JonesStormCenter Communicationsview email addressThe email was not providedcopy email addressAnette GanskeiDTechnische Informationsbibliothek (TIB)iDhttps://orcid.org/0000-0003-1043-4964view email addressThe email was not providedcopy email address}, author={Lacagnina, Carlo and Peng, Ge and Ivanova, Ivana and Downs, Robert and Ramapriyan, Hampapuram and Moroni, David and Wei, Yaxing and Wyborn, Lesley and Jones, Dave and Ganske, Anette}, year={2021}, month={Nov} }
 @article{peng_smith_wingo_ramachandran_2021, title={Stewardship Best Practices for Improved Discovery and Reuse of Heterogeneous and Cross-Disciplinary Earth System Data}, url={https://doi.org/10.1002/essoar.10509377.1}, DOI={10.1002/essoar.10509377.1}, abstractNote={,}, author={Peng, Ge and Smith, Deborah and Wingo, Stephanie and Ramachandran, Rahul}, year={2021}, month={Dec} }
 @article{dunn_lief_peng_wright_baddour_donat_dubuisson_legeais_siegmund_silveira_et al._2021, title={Stewardship Maturity Assessment Tools for Modernization of Climate Data Management}, url={https://doi.org/10.5334/dsj-2021-007}, DOI={10.5334/dsj-2021-007}, abstractNote={High quality and well-managed climate data are the cornerstone of all climate services. Consistently assessing how well the data are managed is one way to establish or demonstrate the trustworthiness of the data. This paper presents the World Meteorological Organization’s (WMO) Stewardship Maturity Matrix for Climate Data (SMM-CD) and the subsidiary SMM-CD for National and Regional Purposes (SMM-CD_NRP). Both these matrices have been developed with the support of the WMO and its High-Quality Global Data Management Framework for Climate (HQ-GDMFC). These self-assessment tools enable data managers to discover WMO recommended data stewardship practices, determine a roadmap for future development and improvement, as well as compare their process against other data providers. Datasets which have been maturity assessed are included in the WMO Climate Data Catalogue, where users can include the results of these maturity assessments into their decision-making process. The SMM-CD contains four categories (data access, usability and usage, quality management, and data management) each of which has a number of aspects, with scores assigned to one of five levels. A smaller number of categories in the SMM-CD_NRP are assigned to four levels appropriate for operationally produced datasets which are national or regional in scope. We explore a number of case studies where these matrices have been applied, as well as supply links to where the Guidance Documents}, journal={Data Science Journal}, author={Dunn, Robert and Lief, Christina and Peng, Ge and Wright, William and Baddour, Omar and Donat, Markus and Dubuisson, Brigitte and Legeais, Jean-François and Siegmund, Peter and Silveira, Reinaldo and et al.}, year={2021}, month={Feb} }
 @article{peng_downs_lacagnina_ramapriyan_ivánová_moroni_wei_gilles_wyborn_goldberg_et al._2020, title={Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets}, volume={12}, url={https://doi.org/10.31219/osf.io/nwe5p}, DOI={10.31219/osf.io/nwe5p}, abstractNote={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 and 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, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Community practical guidelines will allow for global access and harmonization of quality information at the level of individual Earth science datasets and support open science.}, publisher={Center for Open Science}, author={Peng, Ge and Downs, Robert R. and Lacagnina, Carlo and Ramapriyan, Hampapuram and Ivánová, Ivana and Moroni, David F. and Wei, Yaxing and Gilles, Larnicol and Wyborn, Lesley and Goldberg, Mitchell and et al.}, year={2020}, month={Dec} }
 @article{peng_lacagnina_downs_ivanova_moroni_ramapriyan_wei_larnicol_2020, title={Laying the Groundwork for Developing International Community Guidelines to Effectively Share and Reuse Digital Data Quality Information – Case Statement, Workshop Summary Report, and Path Forward}, url={https://doi.org/10.31219/osf.io/75b92}, DOI={10.31219/osf.io/75b92}, abstractNote={This document provides background for and summarizes main takeaways of a workshop held virtually to kick off the development of community guidelines for consistently curating and representing dataset quality information in a way that is in line with the FAIR principles.}, author={Peng, Ge and Lacagnina, Carlo and Downs, Robert R. and Ivanova, Ivana and Moroni, David F. and Ramapriyan, H.K. and Wei, Yaxing and Larnicol, Gilles}, year={2020}, month={Aug} }
 @article{carlson_elger_peng_wagner_klump_2020, title={Promoting Global Sharing of Earth System Science Data Through Free and Open Access Data Publication}, url={https://doi.org/10.31219/osf.io/cq9rz}, DOI={10.31219/osf.io/cq9rz}, abstractNote={In less than one decade the open-access data journal Earth System Science Data (ESSD, a member of the Copernicus Open Access Publisher family) grew from a start-up venture into one of the highest-rated journals in global environmental science. Stimulated by data needs of the International Polar Year 2007-2008, ESSD now serves a very broad community of data providers and users, ensuring that users get free and easy access to quality data products and that providers gain full public credit for preparing, describing and sharing those products. Adopting technology and practices from research journals, ESSD moved data publication from an abstract concept to a working enterprise; several publishers now support similar data-sharing journals. As it confronts increasing challenges and barriers, ESSD serves as a prominent voice for and an example of emphatic fully-free fully-open global data access. Data journals such as ESSD clearly meet a strong community need.}, author={Carlson, David and Elger, Kirsten and Peng, Ge and Wagner, Johannes and Klump, Jens}, year={2020}, month={Dec} }
 @article{matthews_peng_meier_brown_2020, title={Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections}, volume={12}, ISSN={["2072-4292"]}, url={https://doi.org/10.3390/rs12050807}, DOI={10.3390/rs12050807}, abstractNote={Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and examine the sensitivity of sea ice extents and their decadal trends to sea ice concentration threshold values. Threshold choice can affect the timing of annual SIE minimums: a threshold choice as low as 30% can change the timing to August instead of September. Threshold choice impacts the value of annual SIE minimums: in particular, changing the threshold from 15% to 35% can change the annual SIE by more than 10% in magnitude. Monthly SIE data distributions are seasonally dependent. Although little impact was seen for threshold choice on data distributions during annual minimum times (August and September), there is a strong impact in May. Threshold choices were not found to impact the choice of optimal statistical models characterizing annual minimum SIE time series. However, the first ice-free Arctic summer year (FIASY) estimates are impacted; higher threshold values produce earlier FIASY estimates and, more notably, FIASY estimates amongst all considered models are more consistent. This analysis suggests that some of the threshold choice impacts to SIE trends may actually be the result of biased data due to surface melt. Given that the rapid Arctic sea ice depletion appears to have statistically changed SIE characteristics, particularly in the summer months, a more extensive investigation to verify surface melt impacts on this data set is warranted.}, number={5}, journal={REMOTE SENSING}, author={Matthews, Jessica L. and Peng, Ge and Meier, Walter N. and Brown, Otis}, year={2020}, month={Mar} }
 @article{peng_matthews_wang_vose_sun_2020, title={What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?}, volume={8}, ISSN={["2225-1154"]}, url={https://doi.org/10.3390/cli8010015}, DOI={10.3390/cli8010015}, abstractNote={The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.}, number={1}, journal={CLIMATE}, publisher={MDPI AG}, author={Peng, Ge and Matthews, Jessica L. and Wang, Muyin and Vose, Russell and Sun, Liqiang}, year={2020}, month={Jan} }
 @article{ramapriyan_downs_dozier_duerr_folk_frew_hoebelheinrich_mattmann_peng_2019, title={Bruce Barkstrom (1944–2018)}, volume={100}, url={http://dx.doi.org/10.1029/2019eo115561}, DOI={10.1029/2019eo115561}, abstractNote={Bruce R. Barkstrom, principal investigator for NASA missions involved with understanding Earth’s radiation budget, committed his life to analyzing, interpreting, and stewarding Earth science data.}, note={Published on 11 February 2019.}, journal={EOS}, author={Ramapriyan, H.K. and Downs, R.R. and Dozier, J. and Duerr, R. and Folk, M. and Frew, J. and Hoebelheinrich, N. and Mattmann, C.A. and Peng, G.}, year={2019}, month={Feb} }
 @article{peng_milan_ritchey_partee_zinn_mcquinn_casey_lemieux_ionin_jones_et al._2019, title={Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project}, url={https://doi.org/10.5334/dsj-2019-041}, DOI={10.5334/dsj-2019-041}, abstractNote={Assessing the stewardship maturity of individual datasets is an essential part of ensuring and improving the way datasets are documented, preserved, and disseminated to users. It is a critical step towards meeting U.S. federal regulations, organizational requirements, and user needs. However, it is challenging to do so consistently and quantifiably. The Data Stewardship Maturity Matrix (DSMM), developed jointly by NOAA’s National Centers for Environmental Information (NCEI) and the Cooperative Institute for Climate and Satellites–North Carolina (CICS-NC), provides a uniform framework for consistently rating stewardship maturity of individual datasets in nine key components: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. So far, the DSMM has been applied to over 800 individual datasets that are archived and/or managed by NCEI, in support of the NOAA’s <em>OneStop</em> Data Discovery and Access Framework Project. As a part of the <em>OneStop</em>-ready process, tools, implementation guidance, workflows, and best practices are developed to assist the application of the DSMM and described in this paper. The DSMM ratings are also consistently captured in the ISO standard-based dataset-level quality metadata and citable quality descriptive information documents, which serve as interoperable quality information to both machine and human end-users. These DSMM implementation and integration workflows and best practices could be adopted by other data management and stewardship projects or adapted for applications of other maturity assessment models.}, journal={Data Science Journal}, author={Peng, Ge and Milan, Anna and Ritchey, Nancy A. and Partee, Robert P., II and Zinn, Sonny and McQuinn, Evan and Casey, Kenneth S. and Lemieux, Paul, III and Ionin, Raisa and Jones, Philip and et al.}, year={2019}, month={Aug} }
 @article{bliss_steele_peng_meier_dickinson_2019, title={Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record}, volume={14}, ISSN={["1748-9326"]}, url={http://dx.doi.org/10.1088/1748-9326/aafb84}, DOI={10.1088/1748-9326/aafb84}, abstractNote={The seasonal evolution of Arctic sea ice can be described by the timing of key dates of sea ice concentration (SIC) change during its annual retreat and advance cycle. Here, we use SICs from a satellite passive microwave climate data record to identify the sea ice dates of opening (DOO), retreat (DOR), advance (DOA), and closing (DOC) and the periods of time between these events. Regional variability in these key dates, periods, and sea ice melt onset and freeze-up dates for 12 Arctic regions during the melt seasons of 1979–2016 is investigated. We find statistically significant positive trends in the length of the melt season (outer ice-free period) for most of the eastern Arctic, the Bering Sea, and Hudson and Baffin Bays with trends as large as 11.9 d decade−1 observed in the Kara Sea. Trends in the DOR and DOA contribute to statistically significant increases in the length of the open water period for all regions within the Arctic Ocean ranging from 3.9 to 13.8 d decade−1. The length of the ice retreat period (DOR−DOO) ranges from 17.1 d in the Sea of Okhotsk to 41 d in the Greenland Sea. The length of the ice advance period (DOC−DOA) is generally much shorter and ranges from 17.9 to 25.3 d in the Sea of Okhotsk and Greenland Sea, respectively. Additionally, we derive the extent of the seasonal ice zone (SIZ) and find statistically significant negative trends (SIZ is shrinking) in the Sea of Okhotsk, Baffin Bay, Greenland Sea, and Barents Sea regions, which are geographically open to the oceans and influenced by reduced winter sea ice extent. Within regions of the Arctic Ocean, statistically significant positive trends indicate that the extent of the SIZ is expanding as Arctic summer sea ice declines.}, number={4}, journal={ENVIRONMENTAL RESEARCH LETTERS}, author={Bliss, Angela C. and Steele, Michael and Peng, Ge and Meier, Walter N. and Dickinson, Suzanne}, year={2019}, month={Apr} }
 @article{peng_arguez_meier_vamborg_crouch_jones_2019, title={Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions}, volume={4}, ISSN={["2306-5729"]}, url={https://doi.org/10.3390/data4030122}, DOI={10.3390/data4030122}, abstractNote={The climate normal, that is, the latest three full-decade average, of Arctic sea ice parameters is useful for baselining the sea ice state. A baseline ice state on both regional and local scales is important for monitoring how the current regional and local states depart from their normal to understand the vulnerability of marine and sea ice-based ecosystems to the changing climate conditions. Combined with up-to-date observations and reliable projections, normals are essential to business strategic planning, climate adaptation and risk mitigation. In this paper, monthly and annual climate normals of sea ice parameters (concentration, area, and extent) of the whole Arctic Ocean and 15 regional divisions are derived for the period of 1981–2010 using monthly satellite sea ice concentration estimates from a climate data record (CDR) produced by NOAA and the National Snow and Ice Data Center (NSIDC). Basic descriptions and characteristics of the normals are provided. Empirical Orthogonal Function (EOF) analysis has been utilized to describe spatial modes of sea ice concentration variability and how the corresponding principal components change over time. To provide users with basic information on data product accuracy and uncertainty, the climate normal values of Arctic sea ice extents (SIE) are compared with that of other products, including a product from NSIDC and two products from the Copernicus Climate Change Service (C3S). The SIE differences between different products are in the range of 2.3–4.5% of the CDR SIE mean. Additionally, data uncertainty estimates are represented by using the range (the difference between the maximum and minimum), standard deviation, 10th and 90th percentiles, and the first, second, and third quartile distribution of all monthly values, a distinct feature of these sea ice normal products.}, number={3}, journal={DATA}, author={Peng, Ge and Arguez, Anthony and Meier, Walter N. and Vamborg, Freja and Crouch, Jake and Jones, Philip}, year={2019}, month={Sep} }
 @article{peng_privette_tilmes_bristol_maycock_bates_hausman_brown_kearns_2018, title={A Conceptual Enterprise Framework for Managing Scientific Data Stewardship}, url={https://doi.org/10.5334/dsj-2018-015}, DOI={10.5334/dsj-2018-015}, abstractNote={Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.}, journal={Data Science Journal}, author={Peng, Ge and Privette, Jeffrey L. and Tilmes, Curt and Bristol, Sky and Maycock, Tom and Bates, John J. and Hausman, Scott and Brown, Otis and Kearns, Edward J.}, year={2018}, month={Jun} }
 @inproceedings{peng_ramapriyan_moroni_2018, title={ESIP Information Quality Cluster - Fostering collaborations in managing Earth Science Data Quality}, booktitle={Research Data Alliance (RDA) Europe,}, author={Peng, G. and Ramapriyan, H.K. and Moroni, D.}, year={2018}, month={Jan} }
 @article{peng_milan_ritchey_partee_zinn_mcquinn_casey_lemieux_ionin_jones_et al._2018, title={Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project}, volume={10}, url={https://doi.org/10.31219/osf.io/fp3js}, DOI={10.31219/osf.io/fp3js}, abstractNote={Assessing the stewardship maturity of individual datasets is an essential part of ensuring and improving the way datasets are documented, preserved, and disseminated to users. It is a critical step towards meeting U.S. federal regulations, organizational requirements, and user needs. However, it is challenging to do so consistently and quantifiably. The Data Stewardship Maturity Matrix (DSMM), developed jointly by NOAA’s National Centers for Environmental Information (NCEI) and the Cooperative Institute for Climate and Satellites–North Carolina (CICS-NC), provides a uniform framework for consistently rating stewardship maturity of individual datasets in nine key components: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. So far, the DSMM has been applied to over 900 individual datasets that are archived and/or managed by NCEI, in support of the NOAA’s OneStop Data Discovery and Access Framework Project. As a part of the OneStop-ready process, tools, implementation guidance, workflows, and best practices are developed to assist the application of the DSMM and described in this paper. The DSMM ratings are also consistently captured in the ISO standard-based dataset-level quality metadata and citable quality descriptive information documents, which serve as interoperable quality information to both machine and human end-users. These DSMM implementation and integration workflows and best practices could be adopted by other data management and stewardship projects or adapted for applications of other maturity assessment models.}, publisher={Center for Open Science}, author={Peng, Ge and Milan, Anna and Ritchey, Nancy A. and Partee, Robert P., II and Zinn, Sonny and McQuinn, Evan and Casey, Kenneth S. and Lemieux, Paul, III and Ionin, Raisa and Jones, Philip and et al.}, year={2018}, month={Oct} }
 @article{peng_matthews_yu_2018, title={Sensitivity Analysis of Arctic Sea Ice Extent Trends and Statistical Projections Using Satellite Data}, volume={10}, ISSN={["2072-4292"]}, url={http://www.mdpi.com/2072-4292/10/2/230}, DOI={10.3390/rs10020230}, abstractNote={An ice-free Arctic summer would have pronounced impacts on global climate, coastal habitats, national security, and the shipping industry. Rapid and accelerated Arctic sea ice loss has placed the reality of an ice-free Arctic summer even closer to the present day. Accurate projection of the first Arctic ice-free summer year is extremely important for business planning and climate change mitigation, but the projection can be affected by many factors. Using an inter-calibrated satellite sea ice product, this article examines the sensitivity of decadal trends of Arctic sea ice extent and statistical projections of the first occurrence of an ice-free Arctic summer. The projection based on the linear trend of the last 20 years of data places the first Arctic ice-free summer year at 2036, 12 years earlier compared to that of the trend over the last 30 years. The results from a sensitivity analysis of six commonly used curve-fitting models show that the projected timings of the first Arctic ice-free summer year tend to be earlier for exponential, Gompertz, quadratic, and linear with lag fittings, and later for linear and log fittings. Projections of the first Arctic ice-free summer year by all six statistical models appear to converge to the 2037 ± 6 timeframe, with a spread of 17 years, and the earliest first ice-free Arctic summer year at 2031.}, number={2}, journal={REMOTE SENSING}, publisher={MDPI AG}, author={Peng, Ge and Matthews, Jessica L. and Yu, Jason T.}, year={2018}, month={Feb} }
 @article{peng_steele_bliss_meier_dickinson_2018, title={Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record}, volume={10}, url={http://www.mdpi.com/2072-4292/10/9/1328}, DOI={10.3390/rs10091328}, abstractNote={Information on the timing of Arctic snow and ice melt onset, sea ice opening, retreat, advance, and closing, can be beneficial to a variety of stakeholders. Sea ice modelers can use information on the evolution of the ice cover through the rest of the summer to improve their seasonal sea ice forecasts. The length of the open water season (as derived from retreat/advance dates) is important for human activities and for wildlife. Long-term averages and variability of these dates as climate indicators are beneficial to business strategic planning and climate monitoring. In this study, basic characteristics of temporal means and variability of Arctic sea ice climate indicators derived from a satellite-based climate data record from March 1979 to February 2017 melt and freeze seasons are described. Our results show that, over the Arctic region, anomalies of snow and ice melt onset, ice opening and retreat dates are getting earlier in the year at a rate of more than 5 days per decade, while that of ice advance and closing dates are getting later at a rate of more than 5 days per decade. These significant trends resulted in significant upward trends for anomalies of inner and outer ice-free periods at a rate of nearly 12 days per decade. Small but significant downward trends of seasonal ice loss and gain period anomalies were also observed at a rate of −1.48 and −0.53 days per decade, respectively. Our analyses also demonstrated that the means of these indicators and their trends are sensitive to valid data masks and regional averaging methods.}, number={9}, journal={Remote Sensing}, publisher={MDPI AG}, author={Peng, Ge and Steele, Michael and Bliss, Angela and Meier, Walter and Dickinson, Suzanne}, year={2018}, month={Aug}, pages={1328} }
 @article{peng_2018, title={The State of Assessing Data Stewardship Maturity – An Overview}, volume={17}, url={https://doi.org/10.5334/dsj-2018-007}, DOI={10.5334/dsj-2018-007}, abstractNote={Data stewardship encompasses all activities that preserve and improve the information content, accessibility, and usability of data and metadata. Recent regulations, mandates, policies, and guidelines set forth by the U.S. government, federal other, and funding agencies, scientific societies and scholarly publishers, have levied stewardship requirements on digital scientific data. This elevated level of requirements has increased the need for a formal approach to stewardship activities that supports compliance verification and reporting. Meeting or verifying compliance with stewardship requirements requires assessing the current state, identifying gaps, and, if necessary, defining a roadmap for improvement. This, however, touches on standards and best practices in multiple knowledge domains. Therefore, data stewardship practitioners, especially these at data repositories or data service centers or associated with data stewardship programs, can benefit from knowledge of existing maturity assessment models. This article provides an overview of the current state of assessing stewardship maturity for federally funded digital scientific data. A brief description of existing maturity assessment models and related application(s) is provided. This helps stewardship practitioners to readily obtain basic information about these models. It allows them to evaluate each model’s suitability for their unique verification and improvement needs.}, journal={Data Science Journal}, publisher={Ubiquity Press, Ltd.}, author={Peng, Ge}, year={2018}, month={Mar} }
 @inproceedings{moroni_ramapriyan_peng_2017, place={San Diego, CA, USA}, title={A Platform to provide international and inter-agency support for data and information quality solutions and best practices}, booktitle={International Ocean Vector Winds Science Team Meeting}, author={Moroni, D. and Ramapriyan, H. and Peng, G.}, year={2017}, month={May} }
 @inproceedings{peng_2017, place={Washington, DC}, title={Data Stewardship Maturity Matrix – Introduction and Application}, url={http://www.digitalpreservation.gov/meetings/DSA2017/Day_1/10_CP_Part_1_GePeng_NOAA.pdf}, booktitle={Library of Congress Annual Digital Preservation - DSA Meeting}, author={Peng, G.}, year={2017}, month={Sep} }
 @inproceedings{zinn_relph_peng_milan_rosenberg_2017, place={Bethesda, MD, USA}, title={Design and implementation of automation tools for DSMM diagrams and reports}, booktitle={ESIP 2017 Winter Meeting}, author={Zinn, S. and Relph, J. and Peng, G. and Milan, A. and Rosenberg, A.}, year={2017}, month={Jan}, pages={11–13} }
 @article{ramapriyan_peng_moroni_shie_2017, title={Ensuring and Improving Information Quality for Earth Science Data and Products}, volume={23}, url={http://www.dlib.org/dlib/july17/ramapriyan/07ramapriyan.html}, DOI={10.1045/july2017-ramapriyan}, abstractNote={Information about quality is always of concern to users, whether they are buying a car or some other consumer goods, or using scientific data for research or an application. To facilitate consistent quality evaluation and description of quality information on data products for the Earth Science community, we formally introduce and define four constituents of information quality — scientific, product, stewardship and service. As requirements to ensure and improve information quality increase across government, industry and academia, there have been considerable efforts toward improving information quality during the last decade. Given this background, the Information Quality Cluster (IQC) of the Federation of Earth Science Information Partners (ESIP) has been active with membership from multiple organizations, participating voluntarily on a "best-effort" basis. This paper summarizes existing efforts on information quality with emphasis on Earth science data and outlines the current development and evaluation of relevant use cases. The IQC, with its open membership policy, is well positioned to bring together people from various disciplines and iteratively address the relevant challenges and needs of the Earth science data community. Moving forward, the IQC pledges to continue facilitating the development and implementation of data quality standards and best practices for the international Earth science community.}, journal={D.-Lib Magazine}, author={Ramapriyan, H. and Peng, G. and Moroni, D. and Shie, C.-L.}, year={2017}, month={Jul} }
 @misc{peng_2017, title={Getting to know and to use DSMM}, url={https://figshare.com/articles/Getting_To_Know_And_To_Use_DSMM/5346343}, DOI={10.6084/m9.figshare.5346343}, journal={Figshare}, author={Peng, G.}, year={2017}, month={Aug} }
 @inproceedings{peng_lief_ansari_2017, place={Bethesda, MD, USA}, title={Improving Stewardship of Scientific Data Through the Use of a Maturity Matrix – A Success Story}, volume={9}, url={https://www.slideshare.net/gepeng86/improving-stewardship-of-scientific-data-through-use-of-a-maturity-matrix}, booktitle={2017 NOAA Enterprise Data Management Workshop}, author={Peng, G. and Lief, C. and Ansari, S.}, year={2017}, month={Jan}, pages={–} }
 @inproceedings{peng_meier_bliss_steele_dickinson_2017, place={New Orleans, LA}, title={Spatial and Temporal Means and Variability of Arctic Sea Ice Climate Indicators from Satellite Data}, url={https://figshare.com/articles/Spatial_and_Temporal_Means_and_Variability_of_Arctic_Sea_Ice_Climate_Indicators_from_Satellite_Data/5613613}, DOI={https://doi.org/10.6084/m9.figshare.5613613.v1}, booktitle={2017 American Geophysical Union Fall Meeting}, publisher={American Geophysical Union Fall Meeting}, author={Peng, G. and Meier, W. and Bliss, A.C. and Steele, M. and Dickinson, S.}, year={2017}, month={Dec} }
 @article{peng_meier_2017, title={Temporal and regional variability of Arctic sea-ice coverage from satellite data}, volume={76}, url={https://www.cambridge.org/core/journals/annals-of-glaciology/article/temporal-and-regional-variability-of-arctic-seaice-coverage-from-satellite-data/C367531F435C070AA57D84E780288DA1#fndtn-information}, DOI={10.1017/aog.2017.32}, abstractNote={ABSTRACT}, journal={Annals of Glaciology}, author={Peng, G. and Meier, W.N.}, year={2017}, month={Nov} }
 @article{peng_2017, title={The State of Assessing Data Stewardship Maturity – An Overview}, volume={23}, url={https://osf.io/7w2gj/}, DOI={10.17605/OSF.IO/7W2GJ}, journal={Open Science Framework}, author={Peng, Ge}, year={2017}, month={Dec}, pages={7 2} }
 @article{peng_2017, title={The State of Assessing Data Stewardship Maturity – An Overview}, volume={12}, url={https://doi.org/10.31219/osf.io/7w2gj}, DOI={10.31219/osf.io/7w2gj}, abstractNote={Data stewardship encompasses all activities that preserve and improve the information content, accessibility, and usability of data and metadata. Recent regulations, mandates, policies and guidelines set forth by the U.S. government, federal and funding agencies, scientific societies and scholarly publishers, have levied stewardship requirements on digital scientific data. This raised level of requirements has increased the need for a formal approach to stewardship activities that they support compliance verification. For any entity to meet or verify the compliance with the stewardship requirements, it is necessary to assess the current stage, identify gaps, and define a roadmap forward for improvement if necessary. This, however, touches on standards and best practices in multiple knowledge domains. Therefore, data stewardship practitioners, especially these at data repositories, data service centers or associated with data stewardship programs, can benefit from the knowledge of existing maturity assessment models. This article provides an overview of the current stage of assessing stewardship maturity for federally funded digital scientific data. A brief description of existing maturity assessment models and related application(s) is provided. This helps stewardship practitioners to readily obtain basic information about these models. It allows them to evaluate each model’s suitability for their unique verification and improvement needs.}, publisher={Center for Open Science}, author={Peng, Ge}, year={2017}, month={Dec} }
 @inproceedings{peng_ritchey_milan_zinn_casey_neufeld_lemieux_ionin_partee_collins_et al._2017, place={Bloomington, IN, USA}, title={Towards Consistent and Citable Data Quality Descriptive Information for End-Users}, url={https://figshare.com/articles/Towards_Consistent_and_Citable_Data_Quality_Descriptive_Information_for_End-Users/5336191}, DOI={10.6084/m9.figshare.5336191}, booktitle={2017 DataONE User Group Meeting}, author={Peng, G. and Ritchey, N. and Milan, A. and Zinn, S. and Casey, K.S. and Neufeld, D. and Lemieux, P. and Ionin, R. and Partee, R. and Collins, D. and et al.}, year={2017}, month={Jul}, pages={24–25,} }
 @inproceedings{peng_2017, place={Bloomington, IN}, title={Towards consistent and citable data quality descriptive information for end-users}, booktitle={Federation Summer Meeting}, author={Peng, G.}, year={2017}, month={Jun} }
 @inproceedings{shi_matthews_stegall_peng_2016, place={Qingdao, China}, title={A long-term global dataset of temperature and humidity profiles from HIRS}, booktitle={CLIVAR Open Science Conference}, author={Shi, L. and Matthews, J.L. and Stegall, S. and Peng, G.}, year={2016}, month={Sep} }
 @article{peng_shi_stegall_matthews_fairall_2016, title={An Evaluation of HIRS Near-Surface Air Temperature Product in the Arctic with SHEBA Data}, volume={33}, ISSN={["1520-0426"]}, DOI={10.1175/jtech-d-15-0217.1}, abstractNote={Abstract}, number={3}, journal={JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY}, publisher={American Meteorological Society}, author={Peng, Ge and Shi, Lei and Stegall, Steve T. and Matthews, Jessica L. and Fairall, Christopher W.}, year={2016}, month={Mar}, pages={453–460} }
 @article{peng_lawrimore_toner_lief_baldwin_ritchey_brinegar_delgreco_2016, title={Assessing Stewardship Maturity of the Global Historical Climatology Network-Monthly (GHCN-M) Dataset: Use Case Study and Lessons Learned}, volume={22}, url={http://www.dlib.org/dlib/november16/peng/11peng.html}, DOI={10.1045/november2016-peng}, abstractNote={Assessing stewardship maturity — the current state of how datasets are documented, preserved, stewarded, and made accessible publicly — is a critical step towards meeting U.S. federal regulations, organizational requirements, and user needs. The scientific data stewardship maturity matrix (DSMM), developed in partnership with NOAA's National Centers of Environmental Information (NCEI) and the Cooperative Institute for Climate and Satellites-North Carolina (CICS-NC), provides a consistent framework for assessing stewardship maturity of individual Earth Science datasets and capturing justifications for transparency. The consolidated stewardship maturity information will allow users and decision-makers to make informed use decisions based on their unique data needs. This DSMM was applied to a widely utilized monthly-land-surface-temperature dataset derived from the Global Historical Climatology Network (GHCN-M). This paper describes the stewardship maturity ratings of GHCN-M version 3 and provides actionable recommendations for improving the maturity of the dataset. The results from the use case study show that an application of DSMM like this one is useful to people who produce or care for digital environmental datasets. Assessments can identify the strengths and weaknesses of an individual dataset or organization's preservation and stewardship practices, including how information about the dataset is integrated into different systems.}, journal={D.-Lib Magazine}, author={Peng, G. and Lawrimore, J. and Toner, V. and Lief, C. and Baldwin, R. and Ritchey, N. and Brinegar, D. and Delgreco, S.A.}, year={2016}, month={Nov} }
 @inproceedings{peng_2016, place={Durham, NC, USA}, title={Challenges and Potential Approach in Search Relevance Ranking from a Dataset Maturity Perspective}, booktitle={ESIP 2016 Summer meeting}, author={Peng, G.}, year={2016}, month={Jul} }
 @inproceedings{peng_meier_yu_arguez_2016, place={Qingdao, China}, title={Climate Normals and Variability of Arctic Sea Ice}, booktitle={CLIVAR Open Science Conference}, author={Peng, G. and Meier, W.N. and Yu, J. and Arguez, A.}, year={2016}, month={Sep} }
 @inproceedings{ramapriyan_peng_moroni_shie_2016, place={Denver, CO, USA}, title={Ensuring and Improving Information Quality for Earth Science Data and Products – Role of the ESIP Information Quality Cluster}, booktitle={SciDataCon 2016}, author={Ramapriyan, H. and Peng, G. and Moroni, D. and Shie, C.-L.}, year={2016}, month={Sep}, pages={12–13,} }
 @inproceedings{ritchey_peng_jones_milan_casey_2016, place={San Francisco, CA, USA}, title={Practical Application of the Data Stewardship Maturity Model for NOAA’s OneStop Project}, booktitle={AGU 2016 Fall Meeting}, author={Ritchey, N.A. and Peng, G. and Jones, P. and Milan, A. and Casey, K.}, year={2016}, month={Dec} }
 @inproceedings{peng_2016, place={Greenbelt, MD}, title={Scientific Stewardship - What Is It and What Does It Mean to Us?}, booktitle={2016 CICS Joint Science Conference}, author={Peng, G.}, year={2016}, month={Nov} }
 @article{peng_ritchey_casey_kearns_prevette_saunders_jones_maycock_ansari_2016, title={Scientific Stewardship in the Open Data and Big Data Era — Roles and Responsibilities of Stewards and Other Major Product Stakeholders}, volume={22}, DOI={10.1045/may2016-peng}, abstractNote={Ensuring and improving quality and usability is an important part of scientific stewardship of digital environmental data products, but the roles of the responsible parties — those who manage quality and usability — have been evolving over time and have not always been clearly defined. Recognizing that in the Open Data and Big Data era, effective long-term scientific stewardship of data products requires an integrated and coordinated team effort of experts in multiple knowledge domains — data management, science, and technology — we introduce the following stewardship roles for each of these domains: data steward, scientific steward, and technology steward. This article defines their roles and high-level responsibilities as well as the responsibilities of other major product stakeholders, including data originators and distributors. Defining roles and formalizing responsibilities will facilitate the process of curating and communicating quality information to users. Clearly defined roles will allow effective cross-disciplinary communication and better resource allocation for data stewardship, supporting organizations in meeting the challenges of stewarding digital environmental data products in the Open Data and Big Data era.}, number={5/6}, journal={D-Lib Magazine}, publisher={CNRI Acct}, author={Peng, Ge and Ritchey, Nancy A. and Casey, Kenneth S. and Kearns, Edward J. and Prevette, Jeffrey L. and Saunders, Drew and Jones, Philip and Maycock, Tom and Ansari, Steve}, year={2016}, month={May} }
 @misc{peng_2016, title={Stewards – Knowledge and Communication Hub}, volume={v01r02}, DOI={10.6084/m9.figshare.3189724}, journal={Figshare}, publisher={Figshare}, author={Peng, G.}, year={2016}, month={May}, pages={20160519} }
 @inproceedings{peng_ramapriyan_moroni_2016, place={San Francisco, CA, USA}, title={The State of Building a Consistent Framework for Curation and Presentation  of Earth Science Data Quality}, booktitle={AGU 2016 Fall Meeting}, author={Peng, G. and Ramapriyan, H. and Moroni, D.F.}, year={2016}, month={Dec}, pages={12–} }
 @inproceedings{peng_2015, place={Asilomar, CO, USA}, title={A New Paradigm for Ensuring and Improving Data Quality and Usability  – Roles and Responsibilities of Stewards and Stakeholders}, booktitle={2015 ESIP summer meetin}, author={Peng, G.}, year={2015}, month={Jul}, pages={14–17} }
 @inproceedings{austin_peng_2015, place={San Francisco, CA, USA}, title={A Prototype for content-rich decision-making support in NOAA using data as an asset}, booktitle={2015 AGU Fall meeting}, author={Austin, M. and Peng, G.}, year={2015}, month={Dec}, pages={14–18} }
 @article{peng_privette_kearns_ritchey_ansari_2015, title={A Unified Framework for Measuring Stewardship Practices Applied to Digital Environmental Datasets}, volume={13}, url={https://www.jstage.jst.go.jp/article/dsj/13/0/13_14-049/_article}, DOI={10.2481/dsj.14-049}, abstractNote={This paper presents a stewardship maturity assessment model in the form of a matrix for digital environmental datasets. Nine key components are identified based on requirements imposed on digital environmental data and information that are cared for and disseminated by U.S. Federal agencies by U.S. law, i.e., Information Quality Act of 2001, agencies’ guidance, expert bodies’ recommendations, and users. These components include: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. A five-level progressive maturity scale is then defined for each component associated with measurable practices applied to individual datasets, representing Ad Hoc, Minimal, Intermediate, Advanced, and Optimal stages. The rationale for each key component and its maturity levels is described. This maturity model, leveraging community best practices and standards, provides a unified framework for assessing scientific data stewardship. It can be used to create a stewardship maturity scoreboard of dataset(s) and a roadmap for scientific data stewardship improvement or to provide data quality and usability information to users, stakeholders, and decision makers.}, journal={Data Science Journal}, author={Peng, Ge and Privette, Jeffrey L and Kearns, Edward J and Ritchey, Nancy A and Ansari, Steve}, year={2015}, month={Jan} }
 @inproceedings{hou_mayermik_peng_duerr_rosati_2015, place={San Francisco, CA, USA}, title={Assessing information quality: Use case studies for the data stewardship maturity matrix}, booktitle={2015 AGU Fall meeting}, author={Hou, C.-Y. and Mayermik, M. and Peng, G. and Duerr, R. and Rosati, A.}, year={2015}, month={Dec}, pages={14–18} }
 @inproceedings{ritchey_peng_2015, place={San Francisco, CA, USA}, title={Assessing stewardship maturity: use case study results and lessons learned}, booktitle={2015 AGU Fall meeting}, author={Ritchey, N. and Peng, G.}, year={2015}, month={Dec}, pages={14–18} }
 @inproceedings{downs_peng_wei_ramapriyan_moroni_2015, place={San Francisco, CA, USA}, title={Enabling the usability of Earth Science data products and services by evaluating, describing, and improving data quality throughout the data lifecycle }, booktitle={2015 AGU Fall meeting}, author={Downs, R. and Peng, G. and Wei, Y. and Ramapriyan, H. and Moroni, D.}, year={2015}, month={Dec}, pages={14–18} }
 @inproceedings{ramapriyan_moroni_peng_2015, place={San Francisco, CA, USA}, title={Improving information quality for Earth Science data and products – overview}, booktitle={2015 AGU Fall meeting}, author={Ramapriyan, H. and Moroni, D. and Peng, G.}, year={2015}, month={Dec}, pages={14–18} }
 @misc{peng_maycock_2015, title={Increasing sharing, expanding user base, and estimating impact of your research data using service tools and social media}, url={http://www.slideshare.net/gepeng86/peng-maycock-cicsncsharingusecase}, journal={slideshare.com}, author={Peng, G. and Maycock, T.}, year={2015}, month={May} }
 @inproceedings{peng_2015, place={Asilomar, CO, USA}, title={Towards a Consistent Measure of Stewardship Practices}, booktitle={2015 ESIP  Summer Meeting}, author={Peng, G.}, year={2015}, month={Jul}, pages={14–17} }
 @inproceedings{peng_privette_2014, place={Atlanta, GA, USA}, title={A stewardship maturity matrix for assessing the state of environmental data quality and usability}, booktitle={10th Annual Symposium on New Generation Operational Environmental Satellite Systems}, author={Peng, G. and Privette, J.L.}, year={2014}, month={Feb}, pages={2–6,} }
 @inproceedings{peng_cecil_cramer_2014, place={Atlanta, GA, USA}, title={An End-to-End Framework for Probabilistic Uncertainty Characterization of Climate Satellite Data and Products}, booktitle={10th Annual Symposium on New Generation Operational Environmental Satellite Systems}, author={Peng, G. and Cecil, L.D. and Cramer, B.}, year={2014}, month={Feb}, pages={2–6,} }
 @article{directional bias of tao daily buoy wind vectors in the central equatorial pacific ocean from november 2008 to january 2010_2014, url={https://www.jstage.jst.go.jp/article/dsj/13/0/13_14-019/_article}, DOI={10.2481/dsj.14-019}, abstractNote={This article documents a systematic bias in surface wind directions between the TAO buoy measurements at 0°, 170°W and the ECMWF analysis and forecasts. This bias was of the order 10° and persisted from November 2008 to January 2010, which was consistent with a post-recovery calibration drift in the anemometer vane. Unfortunately, the calibration drift was too time-variant to be used to correct the data so the quality flag for this deployment was adjusted to reflect low data quality. The primary purpose of this paper is to inform users in the modelling and remote-sensing community about this systematic, persistent wind directional bias, which will allow users to make an educated decision on using the data and be aware of its potential impact to their downstream product quality. The uncovering of this bias and its source demonstrates the importance of continuous scientific oversight and effective user-data provider communication in stewarding scientific data. It also suggests the need for improvement in the ability of buoy data quality control procedures of the TAO and ECMWF systems to detect future wind directional systematic biases such as the one described here.}, journal={Data Science Journal}, year={2014}, month={Jul} }
 @misc{peng_2014, title={Introduction to Scientific Data Stewardship Maturity Matrix}, url={https://figshare.com/articles/Scientific_Data_Stewardship_Maturity_Matrix/1150243}, DOI={10.6084/m9.figshare.1150243}, journal={Figshare}, author={Peng, Ge}, year={2014}, month={Oct} }
 @article{meier_peng_scott_savoie_2014, title={Verification of a new NOAA/NSIDC passive microwave sea-ice concentration climate record}, volume={33}, ISSN={["1751-8369"]}, url={http://www.polarresearch.net/index.php/polar/article/view/21004}, DOI={10.3402/polar.v33.21004}, abstractNote={A new satellite-based passive microwave sea-ice concentration product developed for the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) programme is evaluated via comparison with other passive microwave-derived estimates. The new product leverages two well-established concentration algorithms, known as the NASA Team and Bootstrap, both developed at and produced by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC). The sea-ice estimates compare well with similar GSFC products while also fulfilling all NOAA CDR initial operation capability (IOC) requirements, including (1) self-describing file format, (2) ISO 19115-2 compliant collection-level metadata, (3) Climate and Forecast (CF) compliant file-level metadata, (4) grid-cell level metadata (data quality fields), (5) fully automated and reproducible processing and (6) open online access to full documentation with version control, including source code and an algorithm theoretical basic document. The primary limitations of the GSFC products are lack of metadata and use of untracked manual corrections to the output fields. Smaller differences occur from minor variations in processing methods by the National Snow and Ice Data Center (for the CDR fields) and NASA (for the GSFC fields). The CDR concentrations do have some differences from the constituent GSFC concentrations, but trends and variability are not substantially different.}, journal={POLAR RESEARCH}, author={Meier, Walter N. and Peng, Ge and Scott, Donna J. and Savoie, Matt H.}, year={2014} }
 @article{peng_meier_scott_savoie_2013, title={A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring}, volume={5}, url={http://www.earth-syst-sci-data.net/5/311/2013/}, DOI={10.5194/essd-5-311-2013}, abstractNote={Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html).  The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1.
                    }, number={2}, journal={Earth System Science Data}, author={Peng, G. and Meier, W. N. and Scott, D. J. and Savoie, M. H.}, year={2013}, pages={311–318} }
 @article{peng_zhang_frank_bidlot_higaki_stevens_hankins_2013, title={Evaluation of Various Surface Wind Products with OceanSITES Buoy Measurements}, volume={28}, ISSN={["1520-0434"]}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000328520200001&KeyUID=WOS:000328520200001}, DOI={10.1175/waf-d-12-00086.1}, abstractNote={Abstract}, number={6}, journal={WEATHER AND FORECASTING}, author={Peng, Ge and Zhang, Huai-Min and Frank, Helmut P. and Bidlot, Jean-Raymond and Higaki, Masakazu and Stevens, Scott and Hankins, William R.}, year={2013}, month={Dec}, pages={1281–1303} }
 @article{evaluation of various surface wind products with oceansites buoy measurements_2013, url={http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-12-00086.1}, DOI={http://dx.doi.org/10.1175/WAF-D-12-00086.1}, abstractNote={Abstract}, journal={Weather and Forecasting}, year={2013} }
 @inproceedings{zhang_peng_vasquez_hankins_fairall_weller_brown_2013, place={Exeter, UK}, title={The SURFA Project: Towards Near-Real-Time Quality Monitoring of NWP Forecasts and Historical Analysis}, booktitle={4th WGNE workshop on systematic error in weather and climate models}, author={Zhang, H.-M. and Peng, G. and Vasquez, L. and Hankins, W. and Fairall, C.W. and Weller, R. and Brown, A.}, year={2013}, month={Apr} }
 @inproceedings{peng_denning_saunders_iwunze_ullman_privette_2012, place={San Francisco, CA}, title={A Low-Cost and Efficient Way to Archive Calibration/Validation Findings for Satellite Data}, note={3 – 7 December.}, booktitle={2012 AGU Fall Meeting}, author={Peng, G. and Denning, M. and Saunders, D. and Iwunze, M. and Ullman, R. and Privette, J.}, year={2012}, month={Dec} }
 @inproceedings{vasquez_peng_urzen_hankins_zhang_2012, place={San Francisco, CA}, title={A Near Real-Time Monitoring System for NWP Forecast and Satellite-Based Products}, note={3 – 7 December.}, booktitle={2012 AGU Fall Meeting}, author={Vasquez, L. and Peng, G. and Urzen, M. and Hankins, W.R. and Zhang, H.-M.}, year={2012}, month={Dec} }
 @article{deriving surface temperature and humidity from long-term hirs observation in high latitudes._2012, journal={J. Atmos. Oceanic. Tech.}, year={2012} }
 @inproceedings{meier_peng_scott_2012, place={San Francisco, CA}, title={Evaluation of a passive microwave sea ice concentration climate data record}, note={3 – 7 December.}, booktitle={2012 AGU Fall Meeting}, author={Meier, W. and Peng, G. and Scott, D.}, year={2012}, month={Dec} }
 @inproceedings{peng_zhang_frank_bidlot_higaki_hankins_2011, place={Vancouver, Canada}, title={A comparison of various Equatorial Pacific surface wind products,}, booktitle={Proc. IGARSS 2011}, author={Peng, G. and Zhang, H.-M. and Frank, H.P. and Bidlot, J. and Higaki, M. and Hankins, W.}, year={2011}, month={Jul}, pages={24 –} }
 @inproceedings{banzon_peng_semunegus_shi_zhao_bates_2011, place={Denver, CO, USA}, title={NOAA multi-decadal records of climate variability}, booktitle={WCRP Open Science Conference}, author={Banzon, P.V. and Peng, G. and Semunegus, H. and Shi, L. and Zhao, X. and Bates, J.J.}, year={2011}, month={Oct}, pages={24 –} }
 @article{shi_peng_bates_2011, title={Surface Air Temperature and Humidity from Intersatellite-Calibrated HIRS Measurements in High Latitudes}, volume={29}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000299497400001&KeyUID=WOS:000299497400001}, DOI={10.1175/JTECH-D-11-00024.1}, abstractNote={Abstract}, number={1}, journal={Journal of Atmospheric and Oceanic Technology}, author={Shi, Lei and Peng, Ge and Bates, John J.}, year={2011}, pages={3–13} }
 @inproceedings{peng_fukumori_harrison_2010, place={Portland, OR, USA}, title={Ocean data assimilation with a global coupled climate model – an assessment of skill and impact}, booktitle={Ocean Science 2010 Conference}, author={Peng, G. and Fukumori, I. and Harrison, M.}, year={2010}, month={Feb}, pages={– 26,} }
 @article{kourafalou_peng_kang_hogan_smedstad_weisberg_2009, title={Evaluation of Global Ocean Data Assimilation Experiment products on South Florida nested simulations with the Hybrid Coordinate Ocean Model}, volume={59}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000262989300004&KeyUID=WOS:000262989300004}, DOI={10.1007/s10236-008-0160-7}, number={1}, journal={Ocean Dynamics}, author={Kourafalou, Vassiliki H. and Peng, Ge and Kang, HeeSook and Hogan, Patrick J. and Smedstad, Ole-Martin and Weisberg, Robert H.}, year={2009}, pages={47–66} }
 @article{peng_garraffo_halliwell_smedstad_meinen_kourafalou_hogan_long_wells_2009, title={TEMPORAL VARIABILITY OF THE FLORIDA CURRENT TRANSPORT AT 27 degrees N}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000269226600005&KeyUID=WOS:000269226600005}, journal={Ocean Circulation and El Nino: New Research}, author={Peng, G. and Garraffo, Z. and Halliwell, G. R. and Smedstad, O. M. and Meinen, C. S. and Kourafalou, V. and Hogan, P. and Long, JA and Wells, DS}, year={2009}, pages={119–137} }
 @inbook{variability of the florida current transport at 27n_2009, url={https://www.novapublishers.com/catalog/product_info.php?products_id=16439}, booktitle={Ocean Circulation and El Nino: New Research}, year={2009} }
 @article{peng_chassignet_kwon_riser_2006, title={Investigation of variability of the North Atlantic Subtropical Mode Water using profiling float data and numerical model output}, volume={13}, url={http://www.sciencedirect.com/science/article/pii/S146350030500082X}, DOI={10.1016/j.ocemod.2005.07.001}, abstractNote={The basic characteristics of the North Atlantic Subtropical Mode Waters (STMW) are well documented in the literature from one-time hydrographic sections or from long-term measurements at one location. From July 1997 to March 1998, 71 vertical profiling floats were deployed in the Western Subtropical North Atlantic region to provide a broader spatial and temporal coverage of STMW. In this study, the STMW properties are estimated from a total of 5352 float temperature profiles in an area covering 30°W–80°W and 20°N–45°N, from January 1998 to December 2000. These STMW properties are compared to those derived from numerical simulation output using the Miami Isopycnic Coordinate Ocean Model (MICOM). The model is configured to the North Atlantic Ocean with a 1-deg horizontal resolution and 20 vertical layers. The model temperature profiles are collocated with the observed float profiles for a direct comparison. The values of the mean STMW temperature derived from both the float data and model output are roughly equal (18.1 °C for floats and 18.0 °C for the model). The model is found to capture well the observed annual cycle of the STMW volume. Given good agreement between float and model profiles, we use a longer MICOM simulation to investigate decadal variability of the STMW renewal rate in terms of the annual subduction rate and its relationship to the large-scale atmospheric circulation pattern changes measured by the North Atlantic Oscillation index. Good correlation is found between the model annual subduction rate anomaly at the Panulirus Station and the NAO winter index anomaly on decadal time scales with NAO leading by 2–3 years.}, number={1}, journal={Ocean Modeling}, author={Peng, Ge and Chassignet, Eric P. and Kwon, Young-Oh and Riser, Stephen C.}, year={2006}, pages={65–85} }
 @book{kourafalou_lee._balotro_peng_johns_ortner_wallcraft_townsend_2006, title={Seasonal variability of circulation and salinity around Florida Bay and the Florida Keys: SoFLA-HYCOM results and comparison to in-situ data}, journal={RSMAS Technical Report 4}, author={Kourafalou, V.H. and Lee., T. and Balotro, R. and Peng, G. and Johns, L. and Ortner, P. and Wallcraft, A. and Townsend, T.}, year={2006}, pages={102} }
 @book{kourafalou_lee._balotro_peng_johns_ortner_wallcraft_townsend_2006, title={The SoFLA-HYCOM (South Florida HYCOM): Atmospheric and ocean modeling support for boundary inputs to the Florida Bay Hydrodynamic model}, journal={Technical report to SFWMD}, author={Kourafalou, V.H. and Lee., T. and Balotro, R. and Peng, G. and Johns, L. and Ortner, P. and Wallcraft, A. and Townsend, T.}, year={2006}, pages={74} }
 @inproceedings{peng_chassignet_kwon_riser_2004, title={Investigation of the Temporal and Spatial Variability of the North Atlantic Subtropical Mode Water Using Float Data and Numerical Model Outputs}, author={Peng, G. and Chassignet, E.P. and Kwon, Y.-O. and Riser, S.C.}, year={2004}, month={Jun} }
 @book{peng_olson_liu_2004, title={Simulated Somali coastal oceanic response to various atmospheric wind products during fall transitions}, journal={RSMAS technical report 2004-004}, author={Peng, G. and Olson, D.B. and Liu, T.}, year={2004} }
 @article{peng_2004, title={Validation of a global reanalysis model in representing synoptic scale eddies using the scatterometer data: A case study}, volume={31}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000223543900006&KeyUID=WOS:000223543900006}, DOI={10.1029/2004GL020297}, abstractNote={A synoptic‐scale cyclonic eddy was captured in the National Centers for Environmental Prediction (NCEP) global reanalysis surface winds in the Bay of Bengal and the Arabian Sea in October 1996. The eddy was formed in the Bay of Bengal in the middle of October 1996. It migrated across southern India, entered the Arabian Sea, and moved along the west coast of India before it turned and propagated across the Arabian Sea toward the Somali coast. The eddy eventually dissipated near Socotra in early November. The performance of the model winds is validated using the National Aeronautics and Space Administration Scatterometer (NSCAT) data along swath measurements. The model is able to resolve the eddy. The results compare well with observations in circulation intensity but differ in eddy center locations and, therefore, in westward propagation speeds in the late stage of the eddy life cycle.}, number={16}, journal={Geophys. Res. Lett}, author={Peng, Ge}, year={2004}, pages={L16201} }
 @inproceedings{peng_rooth_fine_bleck_chassignet_2000, place={San Antonio, TX}, title={Off line computation of oceanic tracer dispersal based on stored data fields from an intermediate resolution ocean circulation model}, volume={80}, number={49}, booktitle={AGU 2000 Ocean Sciences Meeting}, author={Peng, G. and Rooth, C. and Fine, R. and Bleck, R. and Chassignet, E.}, year={2000}, month={Jan}, pages={24–28,} }
 @inproceedings{rooth_fine_peng_bleck_chassignet_2000, place={San Antonio, TX}, title={Use of tracers in MICOM to classify climate-relevant North Atlantic circulation transients}, volume={80}, number={49}, booktitle={AGU 2000 Ocean Sciences Meeting}, author={Rooth, C. and Fine, R. and Peng, G. and Bleck, R. and Chassignet, E.}, year={2000}, month={Jan}, pages={24–28,} }
 @article{peng_mooers_graber_1999, title={Coastal winds in south Florida}, volume={38}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000084738300007&KeyUID=WOS:000084738300007}, DOI={10.1175/1520-0450(1999)038<1740:CWISF>2.0.CO;2}, abstractNote={Abstract Thirteen-month records for the period of April 1994–April 1995 from eight (out of nine) Coastal-Marine Automatic Network (C-MAN) stations in south Florida are analyzed statistically to study alongshore variability of observed atmospheric variables. The surface variables largely are statistically homogeneous and coherent along the Straits of Florida. The maximum correlation for hourly wind components between adjacent stations (separated alongshore by 30–117 km) ranges from 0.9 to 0.75, respectively. However, there is a lack of coverage in the cross-shore direction; hence, a redistribution of C-MAN stations in the cross-shore direction should be considered to provide better spatial coverage of surface atmospheric variables in the south Florida region. Surface winds from the National Centers for Environmental Prediction (NCEP) 80-km grid, η (Eta) Model analysis for the same period are compared statistically with observations from an air–sea interaction buoy and a C-MAN station in the south Florida c...}, number={12}, journal={Journal of Applied Meteorology}, author={Peng, G and Mooers, CNK and Graber, HC}, year={1999}, pages={1740–1757} }
 @inproceedings{peng_olson_esenkov_samuels_1997, title={Simulated oceanic response to various atmospheric forcings in the Arabian Sea coastal region}, volume={78}, number={17}, booktitle={S76, AGU 97 Spring meeting}, author={Peng, G. and Olson, D.B. and Esenkov, O. and Samuels, G.}, year={1997}, month={May} }
 @inproceedings{campos_volhote_olson_peng_1997, place={Baltimore, MD}, title={Simulations of the Southwestern South Atlantic coastal flows with different wind products}, volume={78}, number={17}, booktitle={AGU 97 Spring meeting}, author={Campos, E. and Volhote, D. and Olson, D.B. and Peng, G.}, year={1997} }
 @inproceedings{peng_esenkov_olson_1996, place={San Francisco, CA}, title={Biological responses to the Monsoons in the Arabian Sea}, volume={77}, number={46}, booktitle={AGU 96 Fall meeting}, author={Peng, G. and Esenkov, O. and Olson, D.B.}, year={1996}, month={Dec} }
 @book{peng_mooers_1996, title={Simulations of the South Florida sea breeze regime}, journal={South Florida Oil Spill Research Center technical report}, author={Peng, G. and Mooers, Christopher N.K.}, year={1996}, pages={45} }
 @book{peng_mooers_graber_1996, title={Statistical characteristics of South Florida Coastal-Marine Automatic Network Data}, journal={South Florida Oil Spill Research Center technical report}, author={Peng, G. and Mooers, C.N.K. and Graber, H.C.}, year={1996}, pages={24} }
 @inproceedings{peng_mooers_1995, place={January, Dallas, TX, AMS}, title={Preliminary implementation of a South Florida mesoscale model}, booktitle={14th Conference on Weather Analysis and Forecasting}, author={Peng, G. and Mooers, C.N.K.}, year={1995}, month={Feb}, pages={15–20} }
 @article{life on the edge: marine life and fronts_1994, volume={7}, journal={Oceanography}, year={1994} }
 @article{halliwell_peng_olson_1994, title={STABILITY OF THE SARGASSO SEA SUBTROPICAL FRONTAL ZONE}, volume={24}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:A1994NT49200006&KeyUID=WOS:A1994NT49200006}, DOI={10.1175/1520-0485(1994)024<1166:SOTSSS>2.0.CO;2}, abstractNote={Abstract Recent studies suggest that eddy Properties are significantly influenced by the mean current shear associated with the western Sargasso Sea subtropical frontal zone (SFZ). Between 19° and 34°N, the mean density structure is characterized by three layers separated by a climatologically permanent upper (“seasonal”) thermocline that shoals, and a lower (main) thermocline that deepens, toward the north; the thermoclines are separated by the wedge-shaped southern part of the subtropical mode water pool. The SFZ is evident as a zonal band between about 26° and 32°N where subtropical frontogenesis between the westerlies and trades enhances the slope of the mean seasonal thermocline. Classical linear as well as more recent nonlinear stability theories predict that the mean SFZ flow should be unstable. The linear eigenvalue problem suggests that the most unstable perturbations have wavelengths between 150 and 200 km. Analysis of a channel version of the Miami isopycnic-coordinate primitive equation numeri...}, number={6}, journal={Journal of Physical Oceanography}, author={HALLIWELL, GR and PENG, G and OLSON, DB}, year={1994}, pages={1166–1183} }
 @inproceedings{peng_mooers_1994, place={Miami, FL}, title={South Florida mesoscale model (ARPS) for the sea-breeze regime}, booktitle={First South Florida Atmospheric Modeling Workshop}, author={Peng, G. and Mooers, C.N.K.}, year={1994}, month={Nov} }
 @phdthesis{peng_1993, title={A study of extra-tropical dry surface cyclone development from the potential vorticity perspective}, journal={Ph.D dissertation, University of Miami}, school={University of Miami}, author={Peng, G.}, year={1993}, month={Dec}, pages={93} }
 @inproceedings{olson_peng_halliwell_forbes_1993, place={Amsterdam}, title={Evolution of turbulence as a function of initial flows on a beta-plane}, booktitle={Modeling of Oceanic Vortices}, author={Olson, D.B. and Peng, G. and Halliwell, G.R. and Forbes, C.}, year={1993}, month={May} }
 @inproceedings{olson_peng_davis_flierl_1993, place={Seattle, Washington}, title={Modeling the biophysical response to Gulf Stream meandering}, booktitle={The Oceanography Society: Third Scientific Meeting}, author={Olson, D.B. and Peng, G. and Davis, C.S. and Flierl, G.R.}, year={1993}, month={Apr} }
 @inproceedings{peng_bleck_1993, place={San Antonio, TX}, title={The life cycle of dry surface cyclone development arising from potential vorticity anomalies in a westerly jet}, volume={10}, booktitle={Ninth Conference on Atmospheric and Oceanic Waves and Stability}, author={Peng, G. and Bleck, R.}, year={1993}, month={May}, pages={– 14} }
 @inproceedings{bleck_peng_1989, place={Garmisch-Partenkirchen, F.R.G}, title={Numerical model errors affecting the simulation of Lee cyclogenesis}, note={5 – 9 June,}, booktitle={International Conference on Mountain Meteorology and ALPEX}, author={Bleck, R. and Peng, G.}, year={1989}, month={Jun} }