Visual Analysis of Multiple Dynamic Sensitivities along Ascending Trajectories in the Atmosphere

TL;DR

This paper introduces a visual analytics approach for interactively analyzing sensitivities of weather model variables along ascending atmospheric trajectories, aiding in understanding microphysical parameterization uncertainties.

Contribution

It presents a novel visual interface for comparing and analyzing sensitivities along trajectories, enhancing atmospheric scientists' ability to assess model uncertainties.

Findings

Enables interactive analysis of sensitivities along trajectories

Facilitates comparison of sensitivities at single time points and over time

Applied to real cyclone data demonstrating practical utility

Abstract

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 prognostic 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 interactively the sensitivities of a selected prognostic variable, i.e. rain mass density, to multiple model parameters along such trajectories. We propose a visual interface that enables to a) compare the values of multiple sensitivities at a single time step on multiple trajectories, b) assess the spatio-temporal relationships between sensitivities and the shape and location of trajectories, and c) a comparative analysis of the temporal development of sensitivities along multiple 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 a selected prognostic variable. 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.