Estimating the uncertainty of areal precipitation using data assimilation

We present a method to estimate spatially and temporally variable uncertainty of areal precipitation data. The aim of the method is to merge measurements from different sources, remote sensing and in situ, into a combined precipitation product and to provide an associated dynamic uncertainty estimate. This estimate should provide an accurate representation of uncertainty both in time and space, an adjustment to additional observations merged into the product through data assimilation, and flow dependency. Such a detailed uncertainty description is important for example to generate precipitation ensembles for probabilistic hydrological modelling or to specify accurate error covariances when using precipitation observations for data assimilation into numerical weather prediction models. The presented method uses the Local Ensemble Transform Kalman Filter and an ensemble nowcasting model. The model provides information about the precipitation displacement over time and is continuously updated by assimilation of observations. In this way, the precipitation product and its uncertainty estimate provided by the nowcasting ensemble evolve consistently in time and become flow-dependent. The method is evaluated in a proof of concept study focusing on weather radar data of four precipitation events. The study demonstrates that the dynamic areal uncertainty estimate outperforms a constant benchmark uncertainty value in all cases for one of the evaluated scores, and in half the number of cases for the other score. Thus, the flow dependency introduced by the coupling of data assimilation and nowcasting enables a more accurate spatial and temporal distribution of uncertainty. The mixed results achieved in the second score point out the importance of a good probabilistic nowcasting scheme for the performance of the method

Earth Science Remote Sensing Data - Contributions to Natural Resources Policymaking

This paper traces the evolution of space-derived remote sensing data and data products from their initial dissemination to their impact on public policy related to climate change. We focus on the examples of renewable energy, public health, and ecosystem assessment. Our approach differs from previous studies that have characterized the value of data in terms of the fundamental scientific phenomena they describe. In our research we have sought to identify contributions of space-derived earth science in “making a difference” beyond scientific understanding, thereby providing at least a partial answer to questions about the utility of research posed by Congress, the Office of Management and Budget, managers at the National Aeronautics and Space Administration, and other decisionmakers.Natural resources, climate change, space, data