This paper introduces a time-varying parameter regression model for modeling, forecasting, and assessing inter-annual trends in daily snow depths. The time-varying parameter regression is written in a simple state-space representation and forecasted using a Kalman filter. The recursive Kalman filter algorithm updates the time-varying parameter sequentially when a new data point becomes available and is a flexible forecasting technique. The proposed method is applied to a time series of daily snow depth observations recorded over a 103 year period at a station in Napoleon, North Dakota. The forecasts of the final ten years of data perform well when compared to the actual daily snow depths. Inter-annual snow depth trends indicate an increase in mid-winter snow depths followed by an earlier spring ablation.

中文翻译：

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预测每日降雪深度和评估年际降雪深度动态趋势的统计方法

本文介绍了时变参数回归模型，用于建模，预测和评估日积雪深度的年际趋势。时变参数回归以简单的状态空间表示形式编写，并使用卡尔曼滤波器进行预测。当新数据点可用时，递归卡尔曼滤波算法会顺序更新时变参数，这是一种灵活的预测技术。所提出的方法适用于在北达科他州拿破仑的一个站台记录的103年期间的每日降雪深度的时间序列。与实际每日降雪深度相比，最后十年数据的预测效果很好。年际雪深趋势表明，冬季中雪深度增加，随后春季消融较早。