A fingerprinting technique for major weather events

Benjamin Root, Paul Knight, George Young, Steven Greybush, Richard Grumm, Ron Holmes, Jeremy Ross

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or "fingerprints" associated with past events. The potential refinement and applicability of this method as an operational forecasting tool employed by comparing numerical weather prediction forecasts with fingerprints already identified for major weather events are also discussed.

Original languageEnglish (US)
Pages (from-to)1053-1066
Number of pages14
JournalJournal of Applied Meteorology and Climatology
Volume46
Issue number7
DOIs
StatePublished - Jul 2007

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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