moroni, david f. Ramapriyan, Hampapuram Peng, Ge Hobbs, Jonathan Goldstein, Justin Downs, Robert Wolfe, Robert Shie, Chung-Lin Merchant, Christopher J Bourassa, Mark Matthews, Jessica L. Cornillon, Peter Bastin, Lucy Kehoe, Kenneth Smith, Benjamin Privette, Jeffery L. Subramanian, Aneesh C. Brown, Otis Ivanova, Ivana Understanding the Various Perspectives of Earth Science Observational Data Uncertainty Information 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. <br> Data Uncertainty;Uncertainty Quantification;Uncertainty Characterization;Earth Science;Earth Science Information Partners;Information Quality Cluster;Remote Sensing;Observation;In Situ;Calibration;Validation;Probability;Science Quality;Data Quality;Metrology;Atmospheric Sciences;Climate Science;Earth Sciences not elsewhere classified;Geophysics;Oceanography;Computation Theory and Mathematics;Information Engineering and Theory;Information and Computing Sciences not elsewhere classified;Markup Languages;Numerical Computation;Applied Physics;Computational Physics;Physical Sciences not elsewhere classified;Applied Statistics;Applied Mathematics not elsewhere classified;Numerical Analysis;Probability;Probability Theory;Statistical Theory;Statistics;Statistics not elsewhere classified;Atmospheric Sciences not elsewhere classified;Environmental Science;Environmental Science and Management not elsewhere classified;Environmental Sciences not elsewhere classified;Geochemistry not elsewhere classified;Geology not elsewhere classified;Geodynamics;Geophysics not elsewhere classified;Hydrology;Meteorology;Oceanography not elsewhere classified;Physical Geography;Physical Geography and Environmental Geoscience not elsewhere classified;Physical Oceanography;Solid Earth Sciences 2019-11-08
    https://esip.figshare.com/articles/report/Understanding_the_Various_Perspectives_of_Earth_Science_Observational_Data_Uncertainty/10271450
10.6084/m9.figshare.10271450.v1