Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1016/j.envsoft.2018.01.010
Document author version
Title (Primary) Inverse modelling of snow depths
Author Schlink, U. ORCID logo ; Hertel, D.
Source Titel Environmental Modelling & Software
Year 2018
Department SUSOZ
Volume 110
Page From 62
Page To 71
Language englisch
Keywords Bayesian estimation; Operational snow forecasting; Prediction performance; Sub-alpine snow cover; ESCIMO model
UFZ wide themes RU6;

Operational snow forecasting models contain parameters for which site-specific values are often unknown. As an improvement a Bayesian procedure is suggested that estimates, from past observations, site-specific parameters with confidence intervals. It turned out that simultaneous estimation of all parameters was most accurate. From 2.5 years of daily snow depth observations the estimates were for snow albedo 0.94, 0.89, and 0.56, for snow emissivity 0.88, 0.92, and 0.99, and for snow density (g/cm³) 0.14, 0.05, and 0.11 at the German weather stations Wasserkuppe, Erfurt-Weimar, and Artern, respectively. Using estimated site-specific parameters, ex post snow depth forecasts achieved an index of agreement IA = 0.4–0.8 with past observations; IA = 0.3–0.8 for a 51-years period. They outperformed the precision of predictions based on default parameter values (0.1 < IA<0.3). The developed inverse approach is recommended for parameter estimation and snow forecasting at sub-alpine stations with more or less urban impact and for application in education.

Persistent UFZ Identifier
Schlink, U., Hertel, D. (2018):
Inverse modelling of snow depths
Environ. Modell. Softw. 110 , 62 - 71 10.1016/j.envsoft.2018.01.010