Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions
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Numerical weather prediction models rely on parameterizations for subgrid-scale processes, e.g., for cloud microphysics. These parameterizations are a well-known source of uncertainty in weather forecasts that can be quantified via algorithmic differentiation, which computes the sensitivities of atmospheric variables to changes in model parameters. It is particularly interesting to use sensitivities to analyze the validity of physical assumptions on which microphysical parameterizations in the numerical model source code are based. In this article, we consider the use case of strongly ascending trajectories, so-called warm conveyor belt trajectories, known to have a significant impact on intense surface precipitation rates in extratropical cyclones. We present visual analytics solutions to analyze the sensitivities of rain mass density to large numbers of model parameters along such trajectories. We propose an interactive visual interface that enables a) a comparative analysis of the temporal development of parameter sensitivities on a set of trajectories, b) an effective comparison of the distributions of selected sensitivities at a single location on each trajectory, and c) an assessment of the spatio-temporal relationships between parameter sensitivities and the shape of trajectories. We demonstrate how our approach enables atmospheric scientists to interactively analyze the uncertainty in the microphysical parameterizations, and along the trajectories, with respect to selected state variables. We apply our approach to the analysis of convective trajectories within the extratropical cyclone "Vladiana", which occurred between 22-25 September 2016 over the North Atlantic.
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