Inverse modelling of snow depths. (December 2018)

• Record Type: Journal Article
• Authors: Schlink, Uwe; Hertel, Daniel
• Is Part Of: Environmental modelling & software. Volume 110(2018)
• Page Start: 62
• Page End: 71
• Publication Date: 2018-12
• Subjects: Bayesian estimation -- Operational snow forecasting -- Prediction performance -- Sub-alpine snow cover -- ESCIMO model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.01.010
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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 / c m ³ ) 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. Highlights: Models for operational forecasting of snow cover demand for site-specific parameters and use often default values. Processing routinely monitored data, these parameters can be estimated 'on-site' using an inverse modelling approach. The validation of the Bayesian approach demonstrates associations between estimated snow albedo and emissivity. The joint estimation of all parameters is recommended. The suggested inverse model is applicable to data referring to sub-alpine regions.