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Noodles: a tool for visualization of numerical weather model ensemble uncertainty
Jibonananda Sanyal1, Song Zhang, Jamie Dyer
1Mississippi State University, USA. jibo@gri.msstate.edu
IEEE Transactions on Visualization and Computer Graphics
|October 27, 2010
Summary
This study introduces a new visualization tool for numerical weather prediction ensembles, helping meteorologists better understand forecast uncertainties and identify outlier simulations. The tool aids in assessing variability and improving weather model parameterizations.
Area of Science:
- Meteorology
- Data Visualization
- Scientific Computing
Background:
- Numerical weather prediction (NWP) ensembles are crucial for forecasting but generate large, complex datasets.
- Current visualization methods, like spaghetti plots, are limited in exploring ensemble uncertainties.
- Understanding variability among ensemble members is key for accurate weather prediction.
Purpose of the Study:
- To develop and evaluate a novel interactive tool for visualizing ensemble uncertainties in NWP.
- To explore new methods for representing variability in key weather variables.
- To assess the utility of the tool for operational meteorologists.
Main Methods:
- A parameterization ensemble of the 1993 "Superstorm" was generated using the Weather Research and Forecasting (WRF) model.
- A tool was created for interactive exploration of water-vapor mixing ratio, potential temperature, and pressure uncertainties.
- Uncertainty metrics included standard deviation, inter-quartile range, and confidence intervals, with bootstrapping used to address normality assumptions.
Main Results:
- The tool provided a coordinated view of various visualization techniques, including ribbon/glyph plots and transects.
- Meteorologists found the tool effective in identifying ensemble outliers and regions of high uncertainty.
- The visualizations facilitated physical interpretation of model behavior and potential issues.
Conclusions:
- The developed visualization tool enhances the assessment of NWP ensemble uncertainties.
- Interactive visualization aids in identifying problematic model parameterizations and understanding forecast limitations.
- This approach supports improved operational weather forecasting and model development.
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