Abstract
Abstract. Height of new snow (HN) forecasts help to prevent critical failures of infrastructures in mountain areas, e.g. transport networks and ski resorts. The French national meteorological service, Météo-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, biased and underdispersed. As for many weather variables, post-processing methods can be used to alleviate these drawbacks and obtain meaningful 1 to 4 d 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 large in all situations but is particularly marked when raw forecasts completely miss the snow event. This type of situation 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, and specific humidity).
Highlights
In cold regions, the height of new snow (Fierz et al, 2009; commonly known as the depth of fresh snow) expected for short lead times is critical for many safety issues and the economical impacts of dysfunctional transport networks
height of new snow (HN) is strongly dependent on elevation in mountainous areas, and this relationship cannot be perfectly reproduced by the current resolution of numerical weather prediction (NWP) models
We compare the scores of post-processed forecasts of the 24 h height of new snow between two commonly used statistical methods, namely ensemble model output statistics (EMOS) and quantile regression forests (QRFs)
Summary
In cold regions (e.g. mountainous areas), the height of new snow (Fierz et al, 2009; commonly known as the depth of fresh snow) expected for short lead times is critical for many safety issues (e.g. avalanche hazard) and the economical impacts of dysfunctional transport networks (road, airports, and train track viability). National weather services increasingly provide automatic predictions for that purpose, usually relying on numerical weather prediction (NWP) model outputs. The precipitation forecasts in NWP models are biased and underdispersed. HN is strongly dependent on elevation in mountainous areas, and this relationship cannot be perfectly reproduced by the current resolution of NWP models. Several processes affecting snow properties (density, height, and precipitation phase) are either absent or poorly represented in NWP models (e.g. density of falling snow and mechanical compaction during the deposition). The evolution of the rain–snow limit elevation can greatly differ according to meteorological conditions and is only partly understood (Schneebeli et al, 2013)
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