Editorial Type:
Article
Article Type:
Research Article

A Fingerprinting Technique for Major Weather Events

Benjamin Root The Pennsylvania State University, University Park, Pennsylvania

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Paul Knight The Pennsylvania State University, University Park, Pennsylvania

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George Young The Pennsylvania State University, University Park, Pennsylvania

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Steven Greybush The Pennsylvania State University, University Park, Pennsylvania

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Richard Grumm National Weather Service, State College, Pennsylvania

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Ron Holmes National Weather Service, State College, Pennsylvania

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Jeremy Ross ZedX, Inc., Bellefonte, Pennsylvania

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Print Publication:
01 Jul 2007
DOI:
https://doi.org/10.1175/JAM2509.1
Page(s):
1053–1066
Restricted access

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.

Corresponding author address: Paul Knight, The Pennsylvania State University, Department of Meteorology, 503 Walker Bldg., University Park, PA 16802. Email: pgk2@psu.edu

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.

Corresponding author address: Paul Knight, The Pennsylvania State University, Department of Meteorology, 503 Walker Bldg., University Park, PA 16802. Email: pgk2@psu.edu

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Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

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