Trend assessment for daily snow depths with changepoint considerations

Abstract

This paper develops methods to estimate a long‐term trend in a daily snow depth record. The methods use a storage equation model for the daily snow depths that allows for seasonality, support set features (snow depths cannot be negative), correlation, and mean level shift changepoint features. Changepoints can occur in snow processes whenever observing stations move or station instrumentation is changed; they are critical features to consider when estimating a long‐term trend. A likelihood objective function is developed for the storage model and is used to estimate model parameters. Genetic algorithms are used to optimize a minimum descriptive length model selection criterion that estimates the changepoint numbers and locations. The methods are applied in the analysis of a daily series recorded near Warm Lake, Idaho, from 1948 to 2009.

Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach

“…To increase robustness of our results, possible improvements concerning the used statistical trend analysis method (MK trend test and Theil-Sen slope) could be addressed in future, following the work of [71,72].…”

Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

“…The GA is a data-driven search algorithm that finds an optimal solution for a given target function by implementing the principle of natural selection. Similar to the GA methods developed by Davis et al., Li and Lund, and Lee et al., 19 , 20 , 21 , 22 our GA method uses a penalized likelihood approach with a penalty based on the minimum description length criterion. Our GA method can successfully detect multiple change points, unlike some other at-most one-change-point methods used in recent COVID-19 change-point results.…”

“…Second, because a rigorous estimation of change points in the COVID-19 outcomes is very important, we apply a genetic algorithm (GA) with the minimum description length criterion to estimate the number and times of change points in the US COVID-19 outcome. GAs are a data-driven search technique based on the natural selection principle and, in particular, have been very effective in detecting multiple unknown change points in time-series data, 19 , 20 , 21 , 22 unlike some previous studies 17 , 18 that considered at most only one change point. We estimated the change points separately for each state and the District of Columbia.…”

Scalable multiple changepoint detection for functional data sequences

“…Recently, changepoint analysis has become particularly important in climatological and environmental process modeling (Beaulieu et al, 2020;Jarušková & Antoch, 2020;Lee et al, 2020). Although these works focus on important univariate changepoint detection applications, environmental profiles, such as water vapor columns (Sakai et al, 2019), are increasingly the object of study.…”