Preprints
https://doi.org/10.5194/gmd-2021-266
https://doi.org/10.5194/gmd-2021-266
Submitted as: development and technical paper
23 Sep 2021
Submitted as: development and technical paper | 23 Sep 2021
Status: this preprint was under review for the journal GMD. A final paper is not foreseen.

CycloneDetector (v1.0) – Algorithm for detecting cyclone and anticyclone centers from mean sea level pressure layer

Martin Prantl1, Michal Žák2, and David Prantl3 Martin Prantl et al.
  • 1Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia
  • 2Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University
  • 3Ventusky project, InMeteo, s.r.o., Plzeˇn, Czech Republic

Abstract. Automatic methods for identifying and tracking cyclones were firstly constructed in 1990's and since then there was a big increase in a precision and probability of detection. These methods have been traditionally focused on cyclones (and particularly on tropical cyclones), but the question of anticyclone centers detection remained unsolved since they are usually not a source of turbulent weather, precipitation etc. However, this issue can be important in the era of the climate change. In this paper, an algorithm for an automatic detection of both, cyclones and anticyclones based on mean sea level pressure field, is presented. The algorithm uses two-dimensional raster data as an input and returns a list of detected pressure systems. The main advantages of our solution are easy implementation since it is based on the standard image processing algorithm, sufficient performance of the algorithm, and especially the possibility of high-pressure systems detection. Moreover, the presented solution does not need a direct terrain filtering needed for some algorithms to be done. To validate the quality of detection algorithm results, a comparison against manually prepared data by Met Office was used. It follows from the comparison that the presented algorithm produces results similar to those by Met Office. The most significant differences can be found in the detection of cyclones at the beginning or the end of the lifespan stage. Met Office detects more cyclones in these stages than the presented solution.

This preprint has been withdrawn.

Martin Prantl et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-266', Anonymous Referee #1, 19 Oct 2021
  • RC2: 'Comment on gmd-2021-266', Anonymous Referee #2, 19 Nov 2021
  • EC1: 'Comment on gmd-2021-266', Juan Antonio Añel, 22 Nov 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-266', Anonymous Referee #1, 19 Oct 2021
  • RC2: 'Comment on gmd-2021-266', Anonymous Referee #2, 19 Nov 2021
  • EC1: 'Comment on gmd-2021-266', Juan Antonio Añel, 22 Nov 2021

Martin Prantl et al.

Martin Prantl et al.

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This preprint has been withdrawn.

Short summary
The purpose of our paper is to show our experiences with a new algorithm for detecting of pressure centers based only on mean sea level pressure. While other methods usually focus only on the detection of cyclones, our approach is suitable for finding anticyclones centers as well. Our method is easy to implement with only a few parameters and is based only on standard image processing algorithms. When compared to the manual analysis provided by Met Office, the agreement is around 85 % to 90 %.