Preprints
https://doi.org/10.5194/npg-2023-24
https://doi.org/10.5194/npg-2023-24
21 Nov 2023
 | 21 Nov 2023
Status: a revised version of this preprint is currently under review for the journal NPG.

A quest for precipitation attractors in weather radar archives

Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis Vasileios Sideris, Urs Germann, and Isztar Zawadzki

Abstract. Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fields. In the first approach, the phase space dimensions of the attractor are defined using the domain-scale statistics of precipitation fields, such as the mean precipitation, fraction of rain, spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales, climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000 x 4000 km2, 21 years) and the Swiss Alpine region (≈500 x 500 km2, 6 years).

Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis Vasileios Sideris, Urs Germann, and Isztar Zawadzki

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2023-24', Anonymous Referee #1, 05 Jan 2024
    • AC1: 'Reply on RC1', Loris Foresti, 22 Mar 2024
    • AC3: 'Reply on RC1', Loris Foresti, 22 Mar 2024
  • RC2: 'Comment on npg-2023-24', Anonymous Referee #2, 09 Feb 2024
    • AC2: 'Reply on RC2', Loris Foresti, 22 Mar 2024
Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis Vasileios Sideris, Urs Germann, and Isztar Zawadzki
Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis Vasileios Sideris, Urs Germann, and Isztar Zawadzki

Viewed

Total article views: 406 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
291 99 16 406 10 6
  • HTML: 291
  • PDF: 99
  • XML: 16
  • Total: 406
  • BibTeX: 10
  • EndNote: 6
Views and downloads (calculated since 21 Nov 2023)
Cumulative views and downloads (calculated since 21 Nov 2023)

Viewed (geographical distribution)

Total article views: 390 (including HTML, PDF, and XML) Thereof 390 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Apr 2024
Download
Short summary
We compared two ways to define the phase space of low-dimensional attractors describing the evolution of radar precipitation fields. The first defines the phase space by the domain-scale statistics of precipitation fields, such as their mean, spatial and temporal correlations. The second uses principal component analysis to account for the spatial distribution of precipitation. To represent different climates, radar archives over the United States and the Swiss Alpine region were used.