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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/npg-2019-27
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-27
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 05 Jun 2019

Submitted as: research article | 05 Jun 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Nonlinear Processes in Geophysics (NPG).

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

Jari-Pekka Nousu1,2, Matthieu Lafaysse2, Matthieu Vernay2, Joseph Bellier3,4, Guillaume Evin5, and Bruno Joly6 Jari-Pekka Nousu et al.
  • 1University of Oulu, Water, Energy and Environmental Engineering Research Unit, Oulu, Finland
  • 2Univ. Grenoble Alpes- Université de Toulouse- Météo-France- CNRS- CNRM, Centre d'Etudes de la Neige, Grenoble,France
  • 3Cooperative Institute for Research in Environmental Sciences- University of Colorado Boulder- and NOAA Earth SystemResearch Laboratory, Physical Sciences Division, Boulder- Colorado, USA
  • 4Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
  • 5Univ. Grenoble Alpes - IRSTEA, UR ETNA, Grenoble, France
  • 6CNRM-Université de Toulouse- Météo-France- CNRS, GMAP, Toulouse, France

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 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 km2). 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 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 long reforecasts as homogeneous as possible with the operational systems.

Jari-Pekka Nousu et al.
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Jari-Pekka Nousu et al.
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Short summary
Forecasting the height of new snow is crucial for avalanche hazard, roads viability, ski resorts and tourism. The numerical models suffer from systematic and significant errors which are misleading for the final users. Here, we applied for the first time a state-of-the-art statistical method to correct ensemble numerical forecasts of the height of new snow from their statistical link with measurements in French Alps and Pyrenees. Thus, the realism of automatic forecasts can be quickly improved.
Forecasting the height of new snow is crucial for avalanche hazard, roads viability, ski resorts...
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