Statistical post-processing of ensemble forecasts of the height of new snow

Abstract

Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, roads viability, ski resorts management and tourism attractiveness. Meteo-France operates the PEARP-S2M probabilistic forecasting system including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool is refining the elevation resolution, and the Crocus snowpack model is representing the main physical processes in the snowpack. It provides better HN forecasts 5 than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on Nonhomogeneous Regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24-hour HN in French Alps and Pyrenees. The calibration is tested at the station-scale and the massif-scale (i.e. aggregating different stations over areas of 1000 km 2 ). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two 10 training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs 15 long reforecasts as homogeneous as possible with the operational systems.30 2018) are considerably lower than errors in solid precipitation measurements.The goal of this study is to test the ability of a nonhomogeneous regression method to improve the ensemble forecasts of HN from the PEARP-S2M ensemble snowpack modelling system. More precisely, the regression method of Scheuerer andHamill (2015) based on the Censored Shifted Gamma Distribution was chosen in this work for the advantages identified by the authors in the case of precipitation forecasts. In particular, this method allows to extrapolate the statistical relationship 35 3 https://doi.between predictors and predictands from common events to more unusual events. Considering the specificities of the available datasets in terms of predictands and predictors, two other scientific questions are considered: (1) can statistical postprocessing be applied at a larger spatial scale than the observation points? (2) what are the requirements of a robust training forecast dataset for statistical postprocessing?The structure of the paper is as follows. Section 2 describes the model components of the PEARP-S2M system, the obser-5 vation and forecast datasets used in this study, the nonhomogeneous regression method chosen for post-processing and the evaluation me...

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

“…Vernay et al, 2019), is forced by these forecasts to provide ensemble simulations of HN accounting for all the main physical processes explaining the variability of HN for a given precipitation amount: the dependence of falling snow density on meteorological conditions, the mechanical compaction over time depending on snow weight, the microstructure and wetness of the snow, a possible surface melting, and so on. The forecasts used in this paper are the same as used by Nousu et al (2019) who provided more details on the models' configurations.…”

“…They consider direct ensemble NWP outputs as predictors (precipitation and temperature). Nousu et al (2019) incorporate physical modelling of the snowpack in order to integrate the high temporal variations of temperature and precipitation intensity during a storm event, which can have highly non-linear impacts on HN. In addition, Nousu et al (2019) demonstrate the ability of a nonhomogeneous regression method to improve the ensemble forecasts of HN from the PEARP-S2M ensemble snowpack.…”

“…Nousu et al (2019) incorporate physical modelling of the snowpack in order to integrate the high temporal variations of temperature and precipitation intensity during a storm event, which can have highly non-linear impacts on HN. In addition, Nousu et al (2019) demonstrate the ability of a nonhomogeneous regression method to improve the ensemble forecasts of HN from the PEARP-S2M ensemble snowpack. Using a regression method based on the censored shifted Gamma distribution Hamill, 2015, 2018), the forecast skill was improved for the majority of the stations from common events to more unusual events.…”

“…Section 2 summarizes the forecasts and observations dataset used in this study. Section 3 provides the details of the ensemble model output statistics (EMOS) method tested in this study, a particular nonhomogeneous regression method already employed in Nousu et al (2019) and considered here as a benchmark method. Section 4 describes the QRF method.…”

“…For meteorological predictands such as rainfall and snow, however, the high number of zero-values motivate the use of a discrete-continuous distribution with a mass of probability in zero. In this study, we use a regression based on the Zero-censored Censored Shifted-Gamma distribution (CSGD, see Hamill, 2015, 2018;Nousu et al, 2019 The non-homogeneous regression method applied in this study is similar to the approach presented in Nousu et al (2019) and is referred to as EMOS hereafter. Further details of this EMOS method are presented in Appendix A.…”