Preprints
https://doi.org/10.5194/egusphere-2025-487
https://doi.org/10.5194/egusphere-2025-487
24 Mar 2025
 | 24 Mar 2025
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

CLEAR: a new discrete multiplicative random cascade model for disaggregating path-integrated rainfall estimates from commercial microwave links

Martin Fencl and Marc Schleiss

Abstract. A novel disaggregation algorithm for commercial microwave links (CMLs), named CLEAR (CML Segments with Equal Amounts of Rain), is proposed. CLEAR utilizes a multiplicative random cascade generator to control the splitting of link segments, with the generator's standard deviation dependent on the rain rate and segment length. Spatial consistency during the splitting process is maintained using rain rate information from neighboring CMLs. CLEAR is evaluated on a network of 77 CMLs in Prague. The performance is assessed first using simulated rainfall fields and second through a case study with real attenuation data from the network to demonstrate its applicability in real-world scenarios. Results from the virtual rainfall fields indicate good overall performance, including the generation of realistic spatial patterns. CLEAR effectively estimates maximal and minimal rain rates along CML paths and outperforms a commonly used benchmark algorithm. The stochastic nature of CLEAR allows it to represent uncertainty as an ensemble of rain rate distributions along CML paths. However, the generated ensembles significantly underestimate overall variability along the paths. Additionally, the case study on real data highlights challenges associated with uncertainties in CML quantitative precipitation estimates, which are common across all methods. In conclusion, CLEAR contributes to generating more representative rainfall distributions along CMLs, which is critical for spatial reconstruction of rainfall fields from path-integrated CML data. It also has the potential to reduce errors in CML quantitative precipitation estimates caused by assuming uniform rain rates along CML paths.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Share
Martin Fencl and Marc Schleiss

Status: open (until 29 May 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-487', Anonymous Referee #1, 27 Apr 2025 reply
Martin Fencl and Marc Schleiss

Data sets

Data and codes underlying the publication: "CLEAR: a new discrete multiplicative random cascade model for disaggregating path-integrated rainfall estimates from commercial microwave links Martin Fencl and March Schleiss https://doi.org/10.4121/5c4ad375-4e88-402b-ac46-d27bb47250c3.v1

Model code and software

Data and codes underlying the publication: "CLEAR: a new discrete multiplicative random cascade model for disaggregating path-integrated rainfall estimates from commercial microwave links Martin Fencl and March Schleiss https://doi.org/10.4121/5c4ad375-4e88-402b-ac46-d27bb47250c3.v1

Martin Fencl and Marc Schleiss

Viewed

Total article views: 115 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
97 13 5 115 5 6
  • HTML: 97
  • PDF: 13
  • XML: 5
  • Total: 115
  • BibTeX: 5
  • EndNote: 6
Views and downloads (calculated since 24 Mar 2025)
Cumulative views and downloads (calculated since 24 Mar 2025)

Viewed (geographical distribution)

Total article views: 136 (including HTML, PDF, and XML) Thereof 136 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Apr 2025
Download
Short summary
A novel disaggregation algorithm for commercial microwave links (CMLs), named CLEAR (CML Segments with Equal Amounts of Rain), is proposed. CLEAR utilizes a multiplicative random cascade generator to control the splitting of link segments. The evaluation performed both on virtual and real CML data shows that CLEAR outperforms a commonly used benchmark algorithm. Moreover, the stochastic nature of CLEAR allows it to represent uncertainty as an ensemble of rain rate distributions along CML paths.
Share