Strategy for Improving Measurement Uncertainty and Data Quality Information for Observations at Atmospheric Radiation Measurement (ARM) Research Facilities

Abstract:

Quantified uncertainty estimates on measured quantities are required for providing prior information for cloud property retrieval algorithms and constraining model parameterizations and simulations. Methodologies for determining uncertainty can be complex and can include instrument accuracy and precision estimates, and random and systematic errors. Measurement uncertainty is also impacted by environmental and field factors that can be introduced when operating instruments outside the laboratory setting, which impacts both uncertainty and data quality. The Department of Energy’s Atmospheric Radiation Measurement (ARM) program operates over 100 unique instruments at fixed, mobile, and aerial facilities in diverse climatic regimes around the world. The ARM program is in the process of standardizing how it currently reports measurement uncertainty and developing a new strategy for improving the determination of measurement uncertainty and communicating both the uncertainty and data quality information to users. We will present ARMs plan to standardize the method of reporting measurement uncertainty, as well as share ARMs overall strategies to standardize uncertainty assessment across instrument classes, improving calibration approaches, and providing more consistent data quality assessments to specifically address measurement bias corrections. Our goal is to provide an open forum for discussing the necessary and sufficient elements needed to meet the requirements for retrieval algorithm and model simulation development activities.