Preprints
https://doi.org/10.5194/npg-2021-18
https://doi.org/10.5194/npg-2021-18

  28 Apr 2021

28 Apr 2021

Review status: this preprint is currently under review for the journal NPG.

Calibrated ensemble forecasts of the height of new snow using quantile regression forests and Ensemble Model Output Statistics

Guillaume Evin1, Matthieu Lafaysse2, Maxime Taillardat3, and Michaël Zamo3 Guillaume Evin et al.
  • 1Univ. Grenoble Alpes, INRAE, UR ETGR, Grenoble, France
  • 2Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, 38000 Grenoble, France
  • 3CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Abstract. Height of new snow (HN) forecasts help to prevent critical failures of infrastructures in mountain areas, e.g. transport networks, ski resorts. The French national meteorological service, Meteo-France, operates a probabilistic forecasting system based on ensemble meteorological forecasts and a detailed snowpack model to provide ensembles of HN forecasts. These forecasts are however significantly biased and underdispersed. As for many weather variables, post-processing methods can be used to alleviate these drawbacks and obtain meaningful 1-day to 4-day HN forecasts. In this paper, we compare the skill of two post-processing methods. The first approach is an ensemble model output statistics (EMOS) method, which can be described as a Nonhomogeneous Regression with a Censored Shifted Gamma distribution. The second approach is based on quantile regression forests, using different meteorological and snow predictors. Both approaches are evaluated using a 22-year reforecast. Thanks to a larger number of predictors, the quantile regression forest is shown to be a powerful alternative to EMOS for the post-processing of HN ensemble forecasts. The gain of performance is important in all situations but is particularly marked when raw forecasts completely miss the snow event. This type of situations happens when the rain-snow transition elevation is overestimated by the raw forecasts (rain instead of snow in the raw forecasts) or when there is no precipitation in the forecast. In that case, quantile regression forests improve the predictions using the other weather predictors (wind, temperature, specific humidity).

Guillaume Evin et al.

Status: open (until 23 Jun 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Guillaume Evin et al.

Guillaume Evin et al.

Viewed

Total article views: 138 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
120 14 4 138 1 1
  • HTML: 120
  • PDF: 14
  • XML: 4
  • Total: 138
  • BibTeX: 1
  • EndNote: 1
Views and downloads (calculated since 28 Apr 2021)
Cumulative views and downloads (calculated since 28 Apr 2021)

Viewed (geographical distribution)

Total article views: 140 (including HTML, PDF, and XML) Thereof 140 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 May 2021
Download
Short summary
Forecasting the height of new snow is essential for avalanche hazard survey, road and ski resort management, tourism attractiveness, etc. Meteo-France operates a probabilistic forecasting system using a Numerical Weather Prediction system, and a snowpack model. It provides better forecasts than direct diagnostics but exhibits significant biases. Post-processing methods can be applied to provide automatic forecasting products from this system.