Nousu, J.-P., Lafaysse, M., Vernay, M., Bellier, J., Evin, G., and Joly, B.: Statistical post-processing of ensemble forecasts of the height of new snow, Nonlin. Processes Geophys., 26, 339–357, https://doi.org/10.5194/npg-26-339-2019, 2019.
Statistical post-processing of ensemble forecasts of the height of new snow
|Author:||Nousu, Jari-Pekka1,2; Lafaysse, Matthieu1; Vernay, Matthieu1;|
1Univ. Grenoble Alpes – Université de Toulouse – Météo-France – CNRS – CNRM, Centre d'Etudes de la Neige, Grenoble, France
2University of Oulu, Water, Energy and Environmental Engineering Research Unit, Oulu, Finland
3Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA Earth System Research 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
|Online Access:||PDF Full Text (PDF, 6.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019102434592
|Publish Date:|| 2019-10-24
Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, road viability, ski resort management and tourism attractiveness. Météo-France operates the PEARP-S2M probabilistic forecasting system, including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool refines the elevation resolution and the Crocus snowpack model represents 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 non-homogeneous regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24 h HN in the 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.
Nonlinear processes in geophysics
|Pages:||339 - 357|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
218 Environmental engineering
© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.