esip_iqc_uncertainty_white_paper_7nov2019 (1).pdf (641.72 kB)
Download fileUnderstanding the Various Perspectives of Earth Science Observational Data Uncertainty
report
posted on 2019-11-08, 21:51 authored by david f. moroni, Hampapuram RamapriyanHampapuram Ramapriyan, Ge Peng, Jonathan Hobbs, Justin GoldsteinJustin Goldstein, Robert DownsRobert Downs, Robert Wolfe, Chung-Lin Shie, Christopher J MerchantChristopher J Merchant, Mark Bourassa, Jessica L. Matthews, Peter Cornillon, Lucy Bastin, Kenneth KehoeKenneth Kehoe, Benjamin Smith, Jeffery L. Privette, Aneesh C. Subramanian, Otis Brown, Ivana IvanovaInformation about the uncertainty associated with Earth science observational data is fundamental to use, re-use, and overall evaluation of the data being used to produce science and support decision making. The associated uncertainty information leads to a quantifiable level of confidence in both the data and the science informing decisions produced using the data. The current breadth and cross-domain depth of understanding and application of uncertainty information, however, are still evolving as the practices associated with quantifying and characterizing uncertainty across various types of Earth observation data are diverse. Since its re-establishment in 2015, the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) has convened numerous sessions within the auspices of ESIP and the American Geophysical Union (AGU) to help collect expert-level information focusing on key aspects of uncertainty of Earth science data and addressed key concerns such as: 1) how uncertainty is quantified (UQ) and characterized (UC), 2) understanding the strengths and limitations of common techniques used in producing and evaluating uncertainty information, 3) implications using uncertainty information as a quality indicator 4) impacts of uncertainty on data fusion/assimilation, 5) various methods for documenting and conveying the uncertainty information to data users, and 6) understanding why certain user communities care about uncertainty and others do not. A key recommendation and action item from the ESIP Summer Meeting 2017 was for the IQC to develop a white paper to establish a clearer understanding of the concept of uncertainty and its communication to data users. The information gathered for this white paper has been provided by Earth science data and informatics experts spanning diverse disciplines and observation systems in the cross-domain Earth sciences. The intention of this white paper is to provide a diversely sampled exposition of both prolific and unique policies and practices, applicable in an international context of diverse policies and working groups, made toward quantifying, characterizing, communicating and making use of uncertainty information throughout the diverse, cross-disciplinary Earth science data landscape.
Funding
Various cross-institutional and cross-government funding from the U.S. Government and abroad. Refer to Acknowledgements section for details
History
Usage metrics
Categories
- Atmospheric sciences not elsewhere classified
- Climatology
- Other earth sciences not elsewhere classified
- Geophysics not elsewhere classified
- Oceanography not elsewhere classified
- Theory of computation not elsewhere classified
- Knowledge and information management
- Other information and computing sciences not elsewhere classified
- Software engineering not elsewhere classified
- Numerical computation and mathematical software
- Other physical sciences not elsewhere classified
- Applications in physical sciences
- Applied statistics
- Applied mathematics not elsewhere classified
- Numerical analysis
- Probability theory
- Statistical theory
- Statistics not elsewhere classified
- Other environmental sciences not elsewhere classified
- Environmental management not elsewhere classified
- Geochemistry not elsewhere classified
- Geology not elsewhere classified
- Geodynamics
- Hydrology not elsewhere classified
- Meteorology
- Physical geography and environmental geoscience not elsewhere classified
- Physical oceanography
Keywords
Data UncertaintyUncertainty QuantificationUncertainty CharacterizationEarth ScienceEarth Science Information PartnersInformation Quality ClusterRemote SensingObservationIn SituCalibrationValidationProbabilityScience QualityData QualityMetrologyAtmospheric SciencesClimate ScienceEarth Sciences not elsewhere classifiedGeophysicsOceanographyComputation Theory and MathematicsInformation Engineering and TheoryInformation and Computing Sciences not elsewhere classifiedMarkup LanguagesNumerical ComputationApplied PhysicsComputational PhysicsPhysical Sciences not elsewhere classifiedApplied StatisticsApplied Mathematics not elsewhere classifiedNumerical AnalysisProbabilityProbability TheoryStatistical TheoryStatisticsStatistics not elsewhere classifiedAtmospheric Sciences not elsewhere classifiedEnvironmental ScienceEnvironmental Science and Management not elsewhere classifiedEnvironmental Sciences not elsewhere classifiedGeochemistry not elsewhere classifiedGeology not elsewhere classifiedGeodynamicsGeophysics not elsewhere classifiedHydrologyMeteorologyOceanography not elsewhere classifiedPhysical GeographyPhysical Geography and Environmental Geoscience not elsewhere classifiedPhysical OceanographySolid Earth Sciences