Preprints
https://doi.org/10.5194/amt-2020-284
https://doi.org/10.5194/amt-2020-284

  28 Sep 2020

28 Sep 2020

Review status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

RainForest: A random forest algorithm for quantitative precipitation estimation over Swizerland

Daniel Wolfensberger1,2, Marco Gabella2, Marco Boscacci2, Urs Germann2, and Alexis Berne1 Daniel Wolfensberger et al.
  • 1LTE, Ecole polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • 2MeteoSwiss, via ai Monti 146, Locarno, Switzerland

Abstract. Quantitative precipitation estimation (QPE) is a difficult task, particularly in complex topography, and requires the adjustment of empirical relations between radar observables and precipitation quantities, as well as methods to transform observations aloft to estimations at the ground level. In this work, we tackle this classical problem with a new twist, by training a random forest (RF) regression to learn a QPE model directly from a large database comprising four years of combined gauge and polarimetric radar observations. This algorithm is carefully fine-tuned by optimizing its hyper-parameters and then compared with MeteoSwiss' current operational non-polarimetric QPE method. The evaluation shows that the RF algorithm is able to significantly reduce the error and the bias of the predicted precipitation intensities, especially for large and solid/mixed precipitation. In weak precipitation, however, and despite a-posteriori bias correction, the RF method has a tendency to overestimate. The trained RF is then adapted to run in a quasi-operational setup providing 5 minute QPE estimates on a Cartesian grid, using a simple temporal disaggregation scheme. A series of six case-studies reveal that the RF method creates realistic precipitation fields, with no visible radar artifacts, that appear less smooth then the original non-polarimetric QPE, and offers an improved performance for five out of six events.

Daniel Wolfensberger et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Daniel Wolfensberger et al.

Daniel Wolfensberger et al.

Viewed

Total article views: 318 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
206 105 7 318 12 8
  • HTML: 206
  • PDF: 105
  • XML: 7
  • Total: 318
  • BibTeX: 12
  • EndNote: 8
Views and downloads (calculated since 28 Sep 2020)
Cumulative views and downloads (calculated since 28 Sep 2020)

Viewed (geographical distribution)

Total article views: 289 (including HTML, PDF, and XML) Thereof 286 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Apr 2021
Download