An Objective Layer of Maximum Wind Analysis Technique for the Northern Hemisphere

David B. Spiegler The Travelers Research Center, Inc., Hartford, Conn.

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Abstract

An objective layer of maximum-wind (LRMW) analysis technique which is described and evaluated, has two main features: 1) a categorization procedure that sorts wind profiles into nine jet-stream categories, for the derivation of regression equations to generate initial-guess fields of six LRMW parameters; and 2) an analysis technique that locates jet cores between grid points and generates “observations” along these cores from horizontal-jet profile models.

Initial-guess fields are generated for thickness of the LRMW, wind speed maximum, mean height and wind direction of the LRMW, and vector shears below and above the LRMW, providing a three-dimensional picture of the wind field.

The initial-guess LRMW equations are stable in tests with independent data and are capable of specifying very well the general characteristics of the LRMW profile. They also provide realistic values for the LRMW parameters over no-data areas that are consistent with the entire analysis area.

Over data areas, where objective and subjective analyses could be compared, the objective LRMW analyses compare favorably with subjective analyses.

Abstract

An objective layer of maximum-wind (LRMW) analysis technique which is described and evaluated, has two main features: 1) a categorization procedure that sorts wind profiles into nine jet-stream categories, for the derivation of regression equations to generate initial-guess fields of six LRMW parameters; and 2) an analysis technique that locates jet cores between grid points and generates “observations” along these cores from horizontal-jet profile models.

Initial-guess fields are generated for thickness of the LRMW, wind speed maximum, mean height and wind direction of the LRMW, and vector shears below and above the LRMW, providing a three-dimensional picture of the wind field.

The initial-guess LRMW equations are stable in tests with independent data and are capable of specifying very well the general characteristics of the LRMW profile. They also provide realistic values for the LRMW parameters over no-data areas that are consistent with the entire analysis area.

Over data areas, where objective and subjective analyses could be compared, the objective LRMW analyses compare favorably with subjective analyses.

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