Preprints
https://doi.org/10.5194/gmd-2022-116
https://doi.org/10.5194/gmd-2022-116
Submitted as: development and technical paper
19 Jul 2022
Submitted as: development and technical paper | 19 Jul 2022
Status: this preprint is currently under review for the journal GMD.

Fast Barnes Interpolation

Bruno K. Zürcher Bruno K. Zürcher
  • Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland

Abstract. Barnes interpolation is a method that is widely used in geospatial sciences like meteorology to remodel data values recorded at irregularly distributed points into a representative analytical field. When implemented naively, the computational complexity of Barnes interpolation depends directly on both the number of sample points and the number of grid points. In the era of highly resolved grids and overwhelming numbers of sample points, which originate e.g. from the Internet of Things or from crowd-sourced data, this computation can be quite demanding even on high-performance machines.

This paper presents new approaches how very good approximations of Barnes interpolation can be implemented using fast algorithms. Two use cases are in particular considered, namely (1) where the used grid is embedded in the Euclidean plane and (2) where the grid is located on the unit sphere.

Bruno K. Zürcher

Status: open (until 13 Sep 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Bruno K. Zürcher

Bruno K. Zürcher

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Short summary
We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations that have an algorithmic complexity that depends directly on both the number of sample points and the grid size, our approach reduces this dependency essentially to the number of grid points.