What question did this study set out to answer?

To evaluate techniques for estimating derivatives and linear kinematic properties from sparse, irregular samples of vector point functions.

January 1, 1988Journal of the Atmospheric SciencesOpen Access

Derivative Estimation from Marginally Sampled Vector Point Functions

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Authors

CDCharles A. DoswellPublic Citizen FCF. CaracenaNOAA Earth System Research Laboratory

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Implication

Methodological analysis demonstrates errors in conventional finite-difference mapping for sparse vector networks, highlighting an efficient triad-based technique for kinematic estimation.

Key Points

To evaluate techniques for estimating derivatives and linear kinematic properties from sparse, irregular samples of vector point functions.
Derived an analytical technique to calculate linear kinematic properties (translation, curl, divergence, and deformation) across noncollinear triads of observation points.
Evaluated the mathematical equivalence between direct derivative estimation on irregular networks and conventional methods that map vectors to grids prior to finite differencing.
Triad-based calculations yielded kinematic estimates equivalent to line integral methods while requiring markedly less computational effort.
Mapping vector components onto a grid prior to finite differencing failed to utilize all available information and produced substantial errors when sampling networks were sparse relative to phenomenon wavelengths.
Transforming vector representations (such as wind components to speed and direction) without accounting for coordinate properties altered the core characteristics of the vector field.

Cite This Study

Doswell et al. (1988) studied this question.

synapsesocial.com/papers/6a76a97a7ecc675f86adf4b6 https://doi.org/10.1175/1520-0469(1988)045<0242:defmsv>2.0.co;2

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Implication

Methodological analysis demonstrates errors in conventional finite-difference mapping for sparse vector networks, highlighting an efficient triad-based technique for kinematic estimation.

Derivative Estimation from Marginally Sampled Vector Point Functions

Discussion

Member takes

Implication

Key Points

Cite This Study

Derivative Estimation from Marginally Sampled Vector Point Functions

Discussion

Member takes

Implication

Key Points

Cite This Study

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