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The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies.Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR).Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data.This presentation outlines a global community effort to develop international guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product.

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 virtual pre-ESIP workshop held on July 13, 2020 was sponsored by ESIP and co-organized by the ESIP IQC and the BSC EQC team, in collaboration with the ARDC AU/NZ DQIG. An additional community engagement event was carried out by the AU/NZ DQIG prior to the pre-ESIP workshop. ESIP is primarily supported by the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA) and the United States Geological Survey (USGS). The technological and infrastructural support during the preparation and conduct of the workshop was invaluable. In particular, we thank Megan Carter, ESIP Community Director, for supporting us throughout the workshop and providing helpful advice during the planning stage of the virtual workshop, and ESIP Community Fellow, Alexis Garretson, for supporting the ESIP SM20 report-out session. We thank all participants for attending the pre-ESIP workshop and the ESIP SM20 session and contributing to productive discussions during the live sessions and the two weeks of the ESIP SM20 period. Portions of this work have been extracted from Peng et al. (2020a), which reported on the workshop and the ESIP SM20 report-out session. The Australian participants acknowledge the support of the ARDC. The constructive suggestions from two anonymous reviewers of Data Science Journal have helped improve the quality of the paper. Peer Reviewed "Article signat per 33 autors/es: Ge Peng , Robert R. Downs, Carlo Lacagnina, Hampapuram Ramapriyan, Ivana Ivánová, David Moroni, Yaxing Wei, Gilles Larnicol, Lesley Wyborn, Mitch Goldberg, Jörg Schulz, Irina Bastrakova, Anette Ganske, Lucy Bastin, Siri Jodha S. Khalsa, Mingfang Wu, Chung-Lin Shie, Nancy Ritchey, Dave Jones, Ted Habermann, Christina Lief, Iolanda Maggio, Mirko Albani, Shelley Stall, Lihang Zhou, Marie Drévillon, Sarah Champion, C. Sophie Hou, Francisco Doblas-Reyes, Kerstin Lehnert, Erin Robinson, Kaylin Bugbee"

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 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.

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.

This paper synthesizes past events in an attempt to define the general magnitude, duration, and location of large surface solar anomalies over the globe. Surface solar energy values are mostly a function of solar zenith angle, cloud conditions, column atmospheric water vapor, aerosols, and surface albedo. For this study, solar and meteorological parameters for the 10-yr period July 1983 through June 1993 are used. These data were generated as part of the Release 3 Surface meteorology and Solar Energy (SSE) activity under the NASA Earth Science Enterprise (ESE) effort. Release 3 SSE uses upgraded input data and methods relative to previous releases. Cloud conditions are based on recent NASA Version-D International Satellite Cloud Climatology Project (ISCCP) global satellite radiation and cloud data. Meteorological inputs are from Version-I Goddard Earth Observing System (GEOS) reanalysis data that uses both weather station and satellite information. Aerosol transmission for different regions and seasons are for an 'average' year based on historic solar energy data from over 1000 ground sites courtesy of Natural Resources Canada (NRCan). These data are input to a new Langley Parameterized Shortwave Algorithm (LPSA) that calculates surface albedo and surface solar energy. That algorithm is an upgraded version of the 'Staylor' algorithm. Calculations are performed for a 280X280 km equal-area grid system over the globe based on 3-hourly input data. A bi-linear interpolation process is used to estimate data output values on a 1 X 1 degree grid system over the globe. Maximum anomalies are examined relative to El Nino and La Nina events in the tropical Pacific Ocean. Maximum year-to-year anomalies over the globe are provided for a 10-year period. The data may assist in the design of systems with increased reliability. It may also allow for better planning for emergency assistance during some atypical events.

Five years of scanner data from the Earth Radiation Budget Experiment (ERBE), eight years of surface radiation budget (SRB) data, and one year of scanner radiation budget data from the French-Russian-German experiment, ScaRaB, will be available for use by colleges and universities [and primary and secondary (K-12) schools] over a World Wide Web browser. The database for ERBE is a 5-yr monthly average time series from February 1985 through December 1989, and ScaRaB is a 1-yr dataset from February 1994 to March 1995. ERBE and ScaRaB include shortwave radiative fluxes, emitted longwave radiative fluxes, and the earth's albedo measured at the top of the atmosphere. The SRB dataset spans the period from July 1983 through June 1991 and includes surface downward shortwave fluxes, surface downward longwave fluxes, surface albedos, and cloud percent. Students will have access to the data in three ways. They can display general image format images of any month and visually observe month-to-month or interannual variations. The data files for each month also have a spreadsheet format and can be downloaded in their entirety into any spreadsheet application program for further analysis. Third, using a live access server (LAS), students can interact directly with the data to select and subset datasets in terms of month, year, latitude, and longitude. The LAS allows students to view images of subsetted regions, and to subset data values to a file for further analysis. The server for this dataset is located at the National Aeronautics and Space Administration Langley Research Center, Hampton, Virginia.

Climatological averages of surface radiation budget parameters, namely, the shortwave and longwave surface radiative fluxes, have been derived for each month of the year on a global scale. These climatological averages were derived from an 8-yr (96 month) time series of monthly average fluxes. The monthly averages were computed using fast radiation parameterizations and satellite data from the International Satellite Cloud Climatology Project and the Earth Radiation Budget Experiment. Results are presented as time series of hemispheric and global averages and as geographical distributions and time–latitude cross sections of climatological averages. The spatial/temporal variabilities of the results were found to be clearly related to the corresponding variabilities of meteorological and other inputs to the parameterizations. Numerous comparisons of the present results were made with available surface measurements for the purpose of validation. In most cases, the differences were found to be within...

The surface radiation budget of the earth is important in climate research because it gives an understanding of the distribution of solar and IR radiation exchanges at the surface. Data on the distribution and exchange of these surface radiations are needed over the entire earth on a long-term basis. Global satellite data, coupled with highly developed models, now provide estimates of the surface radiation budget which are near the accuracy required for climate research. A description of surface radiation research, its current state, and planned programs are presented.

A new long-term global data set of monthly average surface shortwave (SW) and longwave (LW) fluxes extending from July 1983 to June 1991 is now available. It is the product of computationally fast radiative transfer algorithms that compute global surface radiative fluxes using satellite data. The data set can be used in the development of general circulation models and in climate studies, including those that examine surface processes and interannual climate anomalies such as El Nino/Southern Oscillation and regional floods and droughts.

Global distributions of surface and atmospheric cloud radiative forcing parameters have been derived using parameterized radiation models with satellite meteorological data from the International Satellite Cloud Climatology Project, and directly measured top-of-atmosphere radiative fluxes from the Earth Radiation Budget Experiment. Specifically, shortwave, longwave, and total cloud forcing at the surface, and column-averaged values of longwave cloud forcing of the atmosphere were derived for the midseasonal months of April, July, and October 1985 and January 1986, covering a complete annual cycle. Seasonal variability is illustrated by comparing the results for July 1985 and January 1986, which represent the seasonal extremes. Surface shortwave cloud forcing is always negative, representing a cooling of the surface, with strongest cooling (-120 to -180 W/sq m) occurring over midlatitude storm tracks of the summer hemisphere. Surface longwave cloud forcing is always positive, representing a warming of the surface, with strongest warming (60 to 75 W/sq m) occurring over storm tracks of the winter hemisphere. Zonal averages show the entire summer hemisphere dominated by shortwave cooling, the middle and high latitudes of the winter hemisphere dominated by longwave warming, and a broad zone of transition in between. The globally averaged total cloud forcing amounts to a cooling throughout the year, ranging from a low of about -12 W/sq m for July 1985 to a high of about -25 W/sq m for January 1986. The longwave cloud forcing of the atmosphere shows a strong warming over deep convective regions in the tropics and a moderate cooling outside the tropics, amounting to a weak cooling (-2 to -5 W/sq m) in the global average. Comparisons of the results with general circulation model simulations show broad qualitative agreement regarding the locations of prominent warming and cooling regions. Quantitative comparisons, on the other hand, show significant differences between the magnitudes of warming and cooling in these regions. Most of the larger differences can be attributed to known deficiences of the general circulation model simulations. Comparisons with satellite-derived results of other investigators show much better agreement.

Surface radiation budget data are presented for the midseasonal months of July and October of 1983 and January and April of 1984. These data allow the examination of geographical and seasonal variations of the entire surface radiation budget from pole to pole. The latest flux estimation techniques have been used along with data from the International Satellite Cloud Climatology Project and the Earth Radiation Budget Experiment. Regional, zonal, and hemispheric distributions of the downward and net components of both shortwave and longwave fluxes and of the net total surface fluxes are determined. Seasonal flux variation per region, expressed as flux range, is illustrated for these components also. The estimated fluxes appear to be accurate to about 16 W/sq m on a global average, based on sensitivity analyses and comparisons with ground data. An analysis of flux errors showed that most of the error was attributable to errors in input data.