Towards Dynamically Adaptive Weather Analysis and Forecasting in LEAD

  • Beth Plale
  • Dennis Gannon
  • Dan Reed
  • Sara Graves
  • Kelvin Droegemeier
  • Bob Wilhelmson
  • Mohan Ramamurthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3515)


LEAD is a large-scale effort to build a service-oriented infrastructure that allows atmospheric science researchers to dynamically and adaptively respond to weather patterns to produce better-than-real time predictions of tornadoes and other “mesoscale” weather events. In this paper we discuss an architectural framework that is forming our thinking about adaptability and give early solutions in workflow and monitoring.


Doppler Radar Business Processing Execution Language Mesoscale Weather Current Weather Condition Hardware Performance Counter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Teragrid (2005),
  2. 2.
    DeRose, L., Zhang, Y., Reed, D.A.: Svpablo: A multi-language performance analysis system. In: 10th International Conference on Computer Performance Evaluation - Modeling Techniques and Tools - Performance Tools 1998, pp. 352–355 (1998)Google Scholar
  3. 3.
    Donaldson, R.J.: Vortex signature recognition by a doppler radar. Journal Applied Meteorology 9, 661–670 (1970)CrossRefGoogle Scholar
  4. 4.
    Droegemeier, K., Kurose, J., McLaughlin, D., Philips, B., Preston, M., Sekelsky, S., Brotzge, J., Chandresakar, V.: Distributed collaborative adaptive sensing for hazardous weather detection, tracking, and predicting. In: Computational Science - ICCS 2004: 4th International Conference (June 2004)Google Scholar
  5. 5.
    Kennedy, K., Mazina, M., Mellor-Crummey, J., Cooper, K., Torczon, L., Berman, F., Chien, A., Dail, H., Sievert, O., Angulo, D., Foster, I., Gannon, D., Johnsson, L., Kesselman, C., Dongarra, J., Vadhiyar, S., Wolski, R., Aydt, R., Reed, D.: Toward a framework for preparing and executing adaptive grid programs. In: Proceedings of the International Parallel and Distributed Processing Symposium Workshop (IPDPS NGS). IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  6. 6.
    Li, X., Ramachandran, R., Rushing, J., Graves, S.: Mining nexrad radar data: An investigative study. In: American Meteorology Society annual meeting (2004)Google Scholar
  7. 7.
    Ribler, R., Vetter, J., Simitci, H., Reed, D.: Autopilot: Adaptive control of distributed applications. In: 7th IEEE Symposium on High- Performance Distributed Computing (July 1998)Google Scholar
  8. 8.
    Wolski, R.: Dynamically forecasting network performance using the network weather service. Journal of Cluster Computing 1, 119–132 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Beth Plale
    • 1
  • Dennis Gannon
    • 1
  • Dan Reed
    • 2
  • Sara Graves
    • 3
  • Kelvin Droegemeier
    • 4
  • Bob Wilhelmson
    • 5
  • Mohan Ramamurthy
    • 6
  1. 1.Indiana University 
  2. 2.University of North CarolinaChapel Hill
  3. 3.University of Alabama Huntsville 
  4. 4.Oklahoma University 
  5. 5.University of Illinois Urbana Champaign 
  6. 6.UCAR, Unidata 

Personalised recommendations