Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty

Abstract

Numerical weather prediction ensembles are routinely used for operational weather forecasting. The members of these ensembles are individual simulations with either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists are interested in understanding the uncertainties associated with numerical weather prediction; specifically variability between the ensemble members. Currently, visualization of ensemble members is mostly accomplished through spaghetti plots of a single mid-troposphere pressure surface height contour. In order to explore new uncertainty visualization methods, the Weather Research and Forecasting (WRF) model was used to create a 48-hour, 18 member parameterization ensemble of the 13 March 1993 "Superstorm". A tool was designed to interactively explore the ensemble uncertainty of three important weather variables: water-vapor mixing ratio, perturbation potential temperature, and perturbation pressure. Uncertainty was quantified using individual ensemble member standard deviation, inter-quartile range, and the width of the 95% confidence interval. Bootstrapping was employed to overcome the dependence on normality in the uncertainty metrics. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, iso-pressure colormaps, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers in the ensemble run and therefore avoiding the WRF parameterizations that lead to these outliers. Additionally, the meteorologists could identify spatial regions where the uncertainty was significantly high, allowing for identification of poorly simulated storm environments and physical interpretation of these model issues.

How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies

“…Much of the work has focused on developing typologies of uncertainty that represent various aspects of data and how it might be signified (Buttenfield and Weibel, 1988;Pang et al, 1997;Sanyal et al, 2009;Thomson et al, 2005) and on developing methods to depict uncertainty visually (e.g. Cedilnik and Rheingans, 2000;Ehlschlaeger et al, 1997;Sanyal et al, 2010;Wittenbrink et al, 1996). A comprehensive review of uncertainty typologies is provided by MacEachren et al (2005) and a review of uncertainty visualisation across science by Brodlie et al (2012).…”

Multi-View Data Visualization Based Big Data Analysis Using Clustering Similarity Measure

“…Comparative compose plots, as a standout amongst the most prominent strategies, was first suggested by Inselberg [3] and Wegman recommended it as something for extraordinary viewpoint data inquire about [4]. Directions of n-dimensional data can be authorized upon in parallel tomahawks in a 2-dimensional airplane and connected by straight sections.…”

Feature-Based Uncertainty Visualization

“…In their experiments, scaled sphere and color mapped sphere perform better than traditional error bars and color-mapped surfaces. Later, they proposed graduated glyphs and ribbons to encode uncertainty information of weather simulations [13]. Figure 2.1 is borrowed from Sanyal et al [12] and shows the four uncertainty visualization techniques in their user study.…”

“…Figure 2.1 is borrowed from Sanyal et al [12] and shows the four uncertainty visualization techniques in their user study. While the uncertainty visualization is application-dependent in many cases, two visualization schemes are widely used: using intuitive metaphors, such as blurry and fuzzy effects [11], [14], [15], which naturally implies the existence of uncertainty; and using quantitative glyphs [13], [16], which shows quantified uncertainty information explicitly. Both schemes have their own tradeoffs.…”