Earth Science Informatics

, Volume 3, Issue 1–2, pp 119–126 | Cite as

Application of uncertainty visualization methods to meteorological trajectories

  • Ryan A. Boller
  • Scott A. Braun
  • Jadrian Miles
  • David H. Laidlaw
Research Article

Abstract

We present an application of uncertainty visualization to air parcel trajectories generated from a global meteorological model. We derive an approximation of advection uncertainty due to interpolation and incorporate this uncertainty into our visualization of trajectories. Our work enables efficient visual pruning of unlikely results, especially in regions of atmospheric shear, potentially reducing erroneous interpretations. Finally, we apply these methods to a real-world meteorological problem to demonstrate its use.

Keywords

Uncertainty visualization Multi-field visualization Flow visualization Time-varying data Meteorological visualization techniques 

Notes

Acknowledgements

The authors wish to thank E.J. Kalafarski/Brown University for implementation of the Runge-Kutta integration algorithm along with Jim Byrnes and the Software Engineering Division at NASA/Goddard Space Flight Center for their support of this research.

References

  1. Bergin MS, Noblet GS, Petrini K, Dhieux JR, Milford JB, Harley RA (1999) Formal uncertainty analysis of a Lagrangian photochemical air pollution model. Environ Sci Technol 33:1116–1126CrossRefGoogle Scholar
  2. Berman F, Chien A, Cooper K, Dongarra J, Foster I, Gannon D, Johnsson L, Kennedy K, Kesselman C, Mellor-Crumme J, Reed D, Torczon L, Wolski R (2001) The GrADS project: software support for high-level grid application development. Int J High Perform Comput Appl 15(4):327–344CrossRefGoogle Scholar
  3. Chen LWA, Doddridge BG, Dickerson RR, Chow JC, Henry RC (2002) Origins of fine aerosol mass in the Baltimore-Washington corridor: implications from observation, factor analysis, and ensemble air back trajectories. Atmos Environ 36:4541–4554CrossRefGoogle Scholar
  4. Darmofal DL, Haimes R (1996) An analysis of 3D particle path integration algorithms. J Comput Phys 123(1):182–195CrossRefGoogle Scholar
  5. Draxler RR (1991) The accuracy of trajectories during ANATEX calculated using dynamic model analyses versus rawinsonde observations. J Appl Meteorol 30:1446–1467CrossRefGoogle Scholar
  6. Draxler RR, Rolph GD (2003) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website (http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD
  7. Dunkerton TJ, Montgomery MT, Wang Z (2008) Tropical cyclogenesis in a tropical wave critical layer: easterly waves. Atmos Chem Phys Discuss 8:11149–11292CrossRefGoogle Scholar
  8. Johnson CR, Sanderson AR (2003) A next step: visualizing errors and uncertainty. IEEE Comput Graph Appl 23(5):6–10CrossRefGoogle Scholar
  9. Kahl JD (1996) On the prediction of trajectory model error. Atmos Environ 30(17):2945–2957CrossRefGoogle Scholar
  10. Kahl JD, Samson PJ (1986) Uncertainty in trajectory calculations due to low resolution meteorological data. J Clim Appl Meteorol 25:1816–1831CrossRefGoogle Scholar
  11. Karyampudi VM, Carlson TN (1988) Analysis and numerical simulations of the Saharan air layer and its effect on easterly wave disturbances. J Atmos Sci 45:3102–3136CrossRefGoogle Scholar
  12. Lopes A, Brodlie K (1998) Accuracy in 3D particle tracing. In: Hege HC, Polthier K (eds) Mathematical visualization: algorithms, applications and numerics. Springer Verlag, Heidelberg, pp 329–341Google Scholar
  13. Murray D, Whittaker T, McWhirter J, Wier S (2004) Integrating GIS data with geoscience data in Unidata’s IDV. International conference on interactive information and processing systems for meterology, oceanography, and hyrdology. January 2004Google Scholar
  14. Pang AT, Wittenbrink CM, Lodha SK (1997) Approaches to uncertainty visualization. Vis Comput 13(8):370–390CrossRefGoogle Scholar
  15. Scheele MP, Siegmund PC, Velthoven PFJ (1996) Sensitivity of trajectories to data resolution and its dependence on the starting point: in or outside a tropopause fold. Meteorol Appl 3:267–273CrossRefGoogle Scholar
  16. Shirayama S (1993) Processing of computed vector fields for visualization. J Comput Phys 106:30–41CrossRefGoogle Scholar
  17. Sobel JS, Forsberg AS, Laidlaw DH, Zeleznik RC, Keefe DF, Pivkin I, Karniadakis GE, Richardson P, Swartz S (2004) Particle flurries: synoptic 3D pulsatile flow visualization. IEEE Comput Graph Appl 24(2):76–85CrossRefGoogle Scholar
  18. Stohl A, Seibert P (1998) Accuracy of trajectories as determined from the conservation of meteorological tracers. Q J Roy Meteorol Soc 124:1465–1484CrossRefGoogle Scholar
  19. Stohl A, Wotawa G, Seibert P, Kromp-Kolb H (1995) Interpolation errors in wind fields as a function of spatial and temporal resolution and their impact on different types of kinematic trajectories. J Appl Meteorol 34:2149–2165CrossRefGoogle Scholar
  20. Wernli H, Davies HC (1997) A Lagrangian-based analysis of extratropical cyclones. I: The method and some applications. Q J Roy Meteorol Soc 123:467–489CrossRefGoogle Scholar
  21. Wittenbrink CM, Pang A, Lodha SK (1996) Glyphs for visualizing uncertainty in vector fields. IEEE Trans Vis Comput Graph 2(3):266–279CrossRefGoogle Scholar

Copyright information

© US Government 2010

Authors and Affiliations

  • Ryan A. Boller
    • 1
  • Scott A. Braun
    • 1
  • Jadrian Miles
    • 2
  • David H. Laidlaw
    • 2
  1. 1.NASA / Goddard Space Flight CenterGreenbeltUSA
  2. 2.Department of Computer ScienceBrown UniversityProvidenceUSA

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