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.

运营降雪预测模型包含的参数通常无法确定特定于站点的值。作为改进措施，建议使用贝叶斯方法，根据过去的观察结果，估计具有置信区间的特定于站点的参数。事实证明，所有参数的同时估计是最准确的。根据2.5年的每日雪深观测，估计的雪反照率分别为0.94、0.89和0.56，雪的发射率分别为0.88、0.92和0.99，雪密度为（$G/C米³$）德国气象站Wasserkuppe，Erfurt-Weimar和Artern分别为0.14、0.05和0.11。使用估计的特定地点参数，事后的积雪深度预报与过去的观察得出的协议指数IA = 0.4–0.8；在51年内，IA = 0.3–0.8。他们优于基于默认参数值（0.1 <IA <0.3）的预测精度。建议使用已开发的反演方法，以对城市或多或少具有影响的亚高山站进行参数估计和降雪预报，并将其应用于教育中。