Preprints
https://doi.org/10.5194/egusphere-2024-2625
https://doi.org/10.5194/egusphere-2024-2625
18 Sep 2024
 | 18 Sep 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Combining commercial microwave links and weather radar for classification of dry snow and rainfall

Erlend Øydvin, Renaud Gaban, Jafet Andersson, Remco van de Beek, Mareile Astrid Wolff, Nils-Otto Kitterød, Christian Chwala, and Vegard Nilsen

Abstract. Differentiating between snow and rainfall is crucial for hydrological modeling and understanding. Commercial Microwave Links (CMLs) can provide accurate rainfall estimates for liquid precipitation, but show minimal signal attenuation during dry snow events, causing the CML time series during these periods to resemble non-precipitation periods. Weather radars can detect precipitation also for dry snow, yet, they struggle to accurately differentiate between precipitation types. This study introduces a new approach to improve rainfall and dry snow classification by combining weather radar precipitation detection with CML signal attenuation. Specifically, events where the radar detects precipitation, but the CML does not, are classified as dry snow. As a reference method we use weather radar, with the precipitation type identified by the dew point temperature at the CML location. Both methods were evaluated using ground measurements from disdrometers within 8 km of a CML, analysing data from 550 CMLs in December 2021 and 435 CMLs in June 2022. Our results show that using CMLs can enhance the classification of dry snow and rainfall, presenting an advantage over the reference method. Further, our research provides valuable insights into how precipitation at temperatures around zero degrees, such as sleet or wet snow, can affect CMLs, contributing to a better understanding of CML applications in colder climates.

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.
Erlend Øydvin, Renaud Gaban, Jafet Andersson, Remco van de Beek, Mareile Astrid Wolff, Nils-Otto Kitterød, Christian Chwala, and Vegard Nilsen

Status: open (until 09 Nov 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Erlend Øydvin, Renaud Gaban, Jafet Andersson, Remco van de Beek, Mareile Astrid Wolff, Nils-Otto Kitterød, Christian Chwala, and Vegard Nilsen
Erlend Øydvin, Renaud Gaban, Jafet Andersson, Remco van de Beek, Mareile Astrid Wolff, Nils-Otto Kitterød, Christian Chwala, and Vegard Nilsen

Viewed

Total article views: 120 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
66 9 45 120 2 5
  • HTML: 66
  • PDF: 9
  • XML: 45
  • Total: 120
  • BibTeX: 2
  • EndNote: 5
Views and downloads (calculated since 18 Sep 2024)
Cumulative views and downloads (calculated since 18 Sep 2024)

Viewed (geographical distribution)

Total article views: 113 (including HTML, PDF, and XML) Thereof 113 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 05 Oct 2024
Download
Short summary
We present a novel method for classifying rain and snow by combining data from Commercial Microwave Links (CMLs) with weather radar. We compare this to a reference method using dew point temperature for precipitation type classification. Evaluations with nearby disdrometers show that CMLs improve the classification of dry snow and rainfall, outperforming the reference method.