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Issue No.06 - November/December (2010 vol.16)
pp: 1421-1430
Jibonananda Sanyal , Mississippi State University
Song Zhang , Mississippi State University
Jamie Dyer , Mississippi State University
Andrew Mercer , Mississippi State University
Philip Amburn , Mississippi State University
Robert Moorhead , Mississippi State University
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.
Uncertainty visualization, weather ensemble, geographic/geospatial visualization, glyph-based techniques, timevarying data, qualitative evaluation
Jibonananda Sanyal, Song Zhang, Jamie Dyer, Andrew Mercer, Philip Amburn, Robert Moorhead, "Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 1421-1430, November/December 2010, doi:10.1109/TVCG.2010.181
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