Scenario-Driven Automatic Pattern Recognition in Nowcasting

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  • 1 Arthur D. Little, Inc., Cambridge, MA 02140
  • | 2 Cambridge Consultants Limited, Cambridge, England
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Abstract

The purpose of this paper is to illustrate how the construction of a knowledge-based system (KBS) to support nowcasting, can be used to guide and facilitate the development of objective pattern recognition algorithms for use with meteorological data. We believe that a KBS based on the semantic interpretation of weather data, using the concept of weather scenarios, can assist the development and use of objective algorithms for pattern recognition in two ways:

1) it focuses the development of pattern recognition algorithms on only those phenomena which are most useful to operational forecasters;

2) its top-down logic constrains when, where, and how objective algorithms should be applied.

We first describe our understanding of nowcasting expertise and the use of pattern recognition (“manual”) by human forecasters. We then briefly review the current use of automatic pattern recognition in nowcasting, present the elements within a scenario and discuss a KBS architecture for using scenarios. Finally, we close by discussing the practical benefits of merging a qualitative KBS with algorithmic pattern recognition techniques.

Abstract

The purpose of this paper is to illustrate how the construction of a knowledge-based system (KBS) to support nowcasting, can be used to guide and facilitate the development of objective pattern recognition algorithms for use with meteorological data. We believe that a KBS based on the semantic interpretation of weather data, using the concept of weather scenarios, can assist the development and use of objective algorithms for pattern recognition in two ways:

1) it focuses the development of pattern recognition algorithms on only those phenomena which are most useful to operational forecasters;

2) its top-down logic constrains when, where, and how objective algorithms should be applied.

We first describe our understanding of nowcasting expertise and the use of pattern recognition (“manual”) by human forecasters. We then briefly review the current use of automatic pattern recognition in nowcasting, present the elements within a scenario and discuss a KBS architecture for using scenarios. Finally, we close by discussing the practical benefits of merging a qualitative KBS with algorithmic pattern recognition techniques.

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